DEV Community: Techforce Global

Building AI Voice Agents for European Businesses: What Actually Matters

Techforce Global — Wed, 26 Aug 2026 19:12:46 +0000

AI voice technology has moved beyond simple IVR replacement.

Today's voice agents can understand natural language, maintain conversational context, connect with APIs, and trigger business workflows. That creates interesting possibilities for European companies but building a useful voice agent requires considerably more than connecting a speech model to a phone number.

The engineering decisions behind the system often determine whether the result becomes a reliable business tool or an impressive demo that fails in production.

Start With the Workflow, Not the Model

A common mistake is choosing an AI model first and then searching for a problem it can solve.

A better approach is to map the workflow.

Consider appointment scheduling. The system needs to understand what the caller wants, collect the required information, check availability, book the appointment, confirm the details, and handle exceptions.

The language model is only one component.

A production architecture may also require speech-to-text, text-to-speech, authentication, business APIs, databases, observability, access controls, and escalation logic.

The business workflow should therefore define the architecture not the other way around.

Integrations Are Where Voice AI Becomes Useful

A voice agent that can only answer general questions has limited operational value.

The real advantage appears when it can interact with existing systems.

For example, a customer might call a logistics company to ask about a delivery. The agent could authenticate the customer, retrieve shipment information through an API, explain the current status, and create a support ticket if something has gone wrong.

That requires reliable integration between the conversational layer and backend systems.

For teams evaluating the broader use cases, this overview of AI voice agents for European businesses offers useful context around where conversational automation can fit into business operations.

Europe Adds Another Engineering Layer

European deployments introduce additional considerations around privacy, security, language support, and regulatory requirements.

A voice system may need to support several languages and regional accents while maintaining consistent intent detection. At the same time, organizations need to understand where conversation data is processed, how long it is retained, and which systems the agent can access.

This makes data architecture especially important.

Sensitive information should not simply flow through every component of the application. Permissions should be scoped to specific tasks, and important actions should be logged for auditing and troubleshooting.

Design for Failure From Day One

Voice conversations are unpredictable.

Customers interrupt. They change their minds. Background noise affects transcription. APIs fail. A caller may provide incomplete information or ask something outside the system's defined capabilities.

Production voice agents therefore need graceful failure paths.

Instead of guessing, the agent should be able to clarify the request, retry an operation, or transfer the conversation to a human.

Human escalation is particularly important for complex or sensitive interactions. The handoff should also preserve relevant conversation context so the customer does not have to explain everything again.

Measure Business Outcomes, Not Just Conversation Quality

A voice agent can achieve impressive language-model evaluation scores and still provide little business value.

Engineering teams should track operational metrics such as task completion rate, successful transfers, average handling time, escalation frequency, booking accuracy, API failure rates, and customer drop-off.

These measurements reveal whether the system is actually improving the process.

For example, reducing average call duration is not necessarily positive if more customers require repeat calls. Likewise, increasing automation rates may be counterproductive if customers cannot reach a human when they need one.

The right metrics depend on the workflow.

Build Narrow, Then Expand

The strongest approach is usually to start with one clearly defined use case.

Appointment scheduling, order-status enquiries, candidate screening, reservation management, or basic customer support can provide controlled environments for testing.

Once the system performs reliably, additional workflows can be introduced.

This incremental approach makes it easier to evaluate accuracy, security, integration reliability, and user experience without turning the first deployment into an unnecessarily complex platform.

AI voice agents are ultimately less about making phone conversations sound intelligent and more about connecting natural language with dependable software systems.

For European businesses, that means combining conversational AI with thoughtful architecture, secure integrations, clear operational boundaries, and human oversight. When those pieces work together, voice automation becomes more than a chatbot on a phone it becomes another interface to the business itself.

From Google Maps to a Sales Pipeline: How I Realized Scraping Was the Easy Part

Techforce Global — Wed, 26 Aug 2026 13:54:29 +0000

A build story for the Google Maps Business Leads Scraper & Sales Intelligence Actor

When I first started working on this Actor, the idea was actually pretty simple.

I wanted to build a Google Maps scraper that could help us find businesses and collect their basic information: business name, address, phone number, website, Google rating, reviews.

Nothing too complicated. And it worked.

But after looking at the output, I had the same thought I've had with a lot of scraping projects: okay, I have the data, now what?

A salesperson doesn't need another CSV with 500 business names. They need to know which businesses are worth contacting, why they should contact them, and ideally, they shouldn't have to spend three hours opening websites one by one to figure that out.

That was the point where this stopped being a Google Maps scraper for me. I wanted to turn it into a lead intelligence tool.

The problem: manual research is where the time disappears

The original problem was manual effort. A salesperson might start with something as simple as "search for resorts in Los Angeles." Then comes the actual work open Google Maps, check the business, open the website, find an email, check whether the website is actually good, look for SEO problems, look for technical problems, try to understand what service could be sold to that business, then decide whether the lead is worth contacting. Repeat for dozens or hundreds of businesses.

Scraping is the easy part. The manual research after scraping is where the time disappears. So I started asking whether the Actor could do more of that work automatically.

The first version was just a Google Maps scraper

I started with the basic version: search Google Maps → collect businesses → return structured data. That proved the idea worked, but there was nothing special about it. There are already loads of Google Maps scrapers on Apify I didn't want to build another one just because I could.

So instead of asking "how can I scrape more businesses," I asked: what information would actually help a salesperson make a decision? That changed the direction of the project.

From scraping to sales intelligence a real run, with real numbers

The Actor now starts with Google Maps, but it doesn't stop there. In one of my tests I searched "Resorts", location “Los Angeles”, capped at 10 businesses, with all four intelligence modules enabled: Sales & Growth Strategy, Website Performance Report, Suggested Business Improvements, and Website Technology Details

The Actor visits the Google Maps results, collects the business information, then visits the business website and starts analyzing it this is where Playwright matters, since the Actor needs to work with real rendered websites, not static HTML responses.

Here's a, run, with actual Console evidence rather than just a description:

Run: "Resorts" in "Los Angeles" - 10 results, 2m 28s, succeeded 2026-08-26

The actual configuration for this run:

run_input = {
    "searchQuery": "Resorts",
    "location": "Los Angeles",
    "subcategory": "",
    "maxResults": 10,
    "includeSalesStrategy": True,
    "includeServiceRecommendations": True,
    "includeTechnicalIntel": True,
    "includeWebsiteHealthScorecard": True,
    "deliveryMode": "summary",
    "mcpConnector": "<your Notion connector ID>",
    "mcpTool": "notion-create-pages",
    "mcpArguments": {
        "parent": {"page_id": "<your Notion page ID>"},
        "pages": [
            {
                "properties": {
                    "title": "Leads: {searchQuery} in {location} ({leadCount})"
                },
                "content": "{leads}"
            }
        ]
    },
    "proxyConfiguration": {"useApifyProxy": False}
}

Run it with the Apify client:

from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_TOKEN>")
run = client.actor("techforce.global/google-maps-leads-sales-intelligence-tool").call(run_input=run_input)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item["businessName"], item["googleRating"], item["totalReviews"])

The output columns from that run: business name, company email, phone, address, Google rating, total reviews, category, website, social media, Google Maps URL, search query, and a Business Growth Opportunity block generated per lead.

The final result isn't just a business record. It's a combination of business data, website intelligence, and sales intelligence and that combination is the part I actually cared about.

What does the output look like?

Here's an actual result from the Four Seasons test run.

Business name: Four Seasons Hotel Los Angeles At Beverly Hills
Address: 300 S Doheny Dr, Los Angeles, CA 90048
Phone: (310) 273-2222
Google rating: 4.6
Total reviews: 2,411
Website: Available
Company email: No email found
That alone is useful. But the interesting part starts after that the Actor generated a lead overview:

Lead priority: Hot Lead
Revenue opportunity: High
Sales angle: Strong reputation but website trust and SEO polish gaps
Estimated monthly service potential: $300–$900
Estimated one-time project potential: $900–$2,400

Instead of saying "here's a hotel with a 4.6 rating," I can say: "this business already has strong social proof, but there are website trust, SEO, and conversion opportunities worth discussing." That's a much better starting point and it was probably the biggest shift in the whole project. I stopped thinking about the output as scraped data. I started thinking about it as a sales decision.

A note on differentiation: at the time of writing I don't yet have a saved example of a Low Priority lead to show side by side I've seen the Actor produce them, but I didn't keep one from an earlier run. What I can show instead is exactly how the tool tells the two apart, which is arguably more useful than one more example: see the scoring logic below.

How lead priority and recommendations actually get decided

I didn't want "Hot Lead" to be a field that just sounds confident without any reasoning behind it. Here's the actual shape of the logic, with exact thresholds abstracted since this is scoring logic I'd rather not hand verbatim to every other Google Maps scraper on the market:

recommendations: list[dict] = []
for field, service, required_work, expected_impact, why_this_matters, expected_after_update in service_rules:
    score = float(scores.get(field, 0) or 0)
    if score >= HIGH_QUALITY_THRESHOLD:
        continue  # already strong, no recommendation needed
    priority = "High" if score < LOW_QUALITY_THRESHOLD else "Medium"
    grade, classification = grade_from_score(score)
    recommendations.append({
        "service": service,
        "priority": priority,
        "currentGrade": grade,
        "professionalClassification": classification,
        "requiredWork": required_work,
        "expectedImpact": expected_impact,
        "whyThisChangeMatters": why_this_matters,
        "whatWillBeAchievedAfterUpdate": expected_after_update,
    })

if not recommendations:
    # Every scored category came back strong — recommend monitoring, not a rebuild
    recommendations.append({
        "service": "Website Monitoring and Continuous Optimization",
        "priority": "Low",
        "currentGrade": "A",
        "professionalClassification": "Growth-ready: maintain current quality through regular checks",
        "requiredWork": "Monitor speed, SEO, security headers, structured data, and conversion paths after every website update.",
        "expectedImpact": "Keeps the website stable, competitive, and ready for future growth.",
        "whyThisChangeMatters": "A strong website can still lose performance over time as plugins, content, tracking scripts, and search requirements change.",
        "whatWillBeAchievedAfterUpdate": "Regular monitoring should preserve website quality, keep growth campaigns stable, and help catch issues before they affect leads or revenue.",
    })

recommendations.sort(key=lambda item: {"High": 0, "Medium": 1, "Low": 2}.get(item["priority"], 3))

In plain language: each website gets scored across several categories (SEO, security, performance, and a few others). Anything scoring above the high-quality threshold gets skipped no point recommending work on something that's already solid. Anything below the lower threshold becomes a High priority recommendation; the middle band becomes Medium. If a business scores well across the board, instead of returning nothing, the Actor recommends ongoing monitoring rather than forcing a fake "issue" just to have something to say. Recommendations are then sorted so the highest-priority, highest-impact items surface first.

That's also why a Hot Lead and a genuinely well-optimized business get treated differently under the hood, even if both show up with good Google ratings — the lead score and the website-quality score are measuring different things.

The website analysis: every site is different

Once the Actor had the business information, the next challenge was pulling something useful from the website itself and this wasn't as straightforward as I expected. Some sites load fast, some don't. Some have clean HTML, some rely heavily on JavaScript. Some expose an email, some don't. Some have good SEO, some look untouched since 2014 and some manage to have both problems and a great Google rating.

The Actor checks multiple parameters before generating its technical and sales analysis. For the Four Seasons test, the generated analysis flagged issues around on-page SEO, SSL/security, and technical SEO which gives a salesperson something concrete to open a conversation with:

Not: "Hi, we provide digital marketing services."

But something closer to: "We noticed that your business already has strong reviews and a good online foundation, but there are a few trust, SEO, and conversion improvements that could help improve enquiry confidence and organic visibility."

The first message sounds like an advertisement. The second one sounds like someone actually looked at the business. That difference was the entire goal.

The hardest problem wasn't scraping Google Maps

Ironically, the hardest part of this project wasn't Google Maps, and it wasn't even getting the website data. It was accuracy. If I'm going to give a salesperson a field called "Hot Lead," I need to be careful about what that actually means. If I say a business has a high revenue opportunity, there should be reasoning behind it, not a generic template with the business name inserted.

The way I actually tested this: run it, look at the result, find something that doesn't make sense, change the implementation, run it again, check the output again, repeat. One concrete example early on, businesses with genuinely excellent websites were still occasionally getting flagged with generic "improve your website" recommendations, because the scoring only looked at surface-level signals like page load time. Adding the category-by-category threshold logic above (rather than one blended score) is what fixed that a business can now score well on security but poorly on SEO, and get a recommendation that reflects the actual gap instead of an average that hides it.

There wasn't a magic switch that made results suddenly perfect. It was a lot of small improvements, and it's the least glamorous part of building a tool like this you don't see it on the Actor page, but it's where most of the work happens

The timeout problem

The basic Google Maps scraping was relatively fast. Once I added website analysis, technical analysis, and sales intelligence, every business became more expensive in execution time the Actor wasn't doing one operation anymore, it was doing several per business. If I scrape 100 businesses, I don't just have 100 Google Maps pages, I potentially have 100 websites that also need visiting and analyzing.

That's a genuinely different performance problem, and it meant keeping a close eye on execution time to keep the whole thing practical to run on the Apify platform which is also one of the reasons I like building on Apify: I don't have to build and manage the infrastructure around the crawler myself. I can focus on the Actor's actual logic.

What I actually used from Apify

Actor : the core execution unit
Playwright : handles browser-based website interaction, since static HTML fetching isn't enough for modern sites
Dataset : where structured lead results go
Key-value store : useful for data that doesn't fit a table-like dataset
Integrations : what turns the output into part of another workflow, not a dead end

Each run gets its own dataset and key-value store, which is convenient for development and testing I don't have to build a storage layer around every scraping project. I build the Actor, run it, and inspect results from the Console or the API.

The development cycle was short testing it properly wasn't

The first usable version took two to three days, but that doesn't mean two days of coding and done. I tested the Actor against hundreds of businesses during that process. The goal wasn't just making the Actor run successfully a run finishing with status "SUCCEEDED" is not the same thing as a run producing something anyone wants to use. That distinction mattered more than I expected going in.

The Apify Console made this testing loop much easier, since I could see input, logs, output, and storage for each run without building separate tooling. The runs used Apify SDK 3.4.1, Apify Client 2.5.1, and Crawlee 1.9.1.

From dataset to actual sales workflow

I didn't want the output to live forever inside an Apify dataset a sales team isn't going to open Apify every morning to check a dataset. They already have tools they use. For this workflow, results get pushed directly into Notion via Apify's MCP connector, using the notion-create-pages tool each run creates a page under a parent Notion page, titled with the search query, location, and lead count, with the leads themselves as page content.

So the pipeline is: Google Maps → the Lead Intelligence Actor → Apify Dataset → Notion → the sales workflow. The Actor isn't sitting at the end of the pipeline as a scraper it's one component in the middle of it. The same output could just as easily be pushed into a CRM, sent through an n8n workflow, or used to trigger another Actor entirely, since Apify supports Actor-to-Actor workflows where one Actor's output becomes the next step's input or trigger.

Why Apify?

I could have built the infrastructure myself managed browser instances, managed proxies, built storage, handled scaling and deployment and monitoring and eventually gotten back to the actual problem I wanted to solve. I didn't want to do that. Apify gives me a developer-friendly platform where I can focus on building the Actor instead of the infrastructure around it, and proxy support mattered too infrastructure problems can quickly become the project if you're not careful, and I wanted the project to stay the Actor.

What I would do differently next time

If I were starting this again, I'd talk to more salespeople before writing the first line of code not developers, not scraping experts, actual salespeople and agencies. What do you actually look at before contacting a lead? What makes you reject one? Which website problems are actually worth knowing? What makes a lead "hot" for you? What information helps you personalize a first message, and what's completely useless?

I built a lot of the intelligence based on what I thought would be useful. That works to a point, but there's a difference between "this is interesting information" and "this information actually changes whether I contact this lead." The second one is what I'd optimize for if I rebuilt this today I'd rather have ten highly useful fields than fifty nobody looks at.

Where I want to take this next

The current version can discover businesses, collect their information, visit their websites, analyze technical aspects, generate website health information, identify improvements, and generate sales-oriented intelligence. But I don't think the interesting part ends there.

Instead of just saying "Hot Lead," I want to surface why. Instead of just giving a sales angle, I want the salesperson to understand why that angle was selected. Instead of just generating a recommended pitch, I want the recommendation traceable back to actual signals from the business and its website not a black box.

The original idea was a Google Maps scraper. That version worked, but it wasn't enough. The interesting part started when I stopped asking "how many businesses can I scrape" and started asking "what can I tell a salesperson that will actually save them time." That question changed the entire project, and it's probably the biggest thing I learned building it.

Scraping the data is only half the job. Making the data useful is the real work. And sometimes the best scraper isn't the one that gives you the most data it's the one that makes you open the next lead and think: okay, this one is actually worth calling.

FAQ

Does this work for any business category, or just hotels and dental practices?
The Actor works with any Google Maps search query and location — the examples in this article (resorts, dentists) are just what I happened to test with. The intelligence modules apply the same scoring logic regardless of category

How is "Lead Priority" actually calculated?
Each business's website is scored across several categories (SEO, security, performance, and others). Scores below a threshold generate prioritized recommendations; scores above it don't. See the scoring logic section above for the actual (lightly abstracted) code.

What happens if a business has no email or no website at all?
The Actor still returns the Google Maps data (name, address, phone, rating, reviews) website-dependent fields like technical intel and website health scoring are simply left empty rather than guessed at.

Can I deliver results somewhere other than Notion?
Yes, Apify's MCP connector supports multiple delivery targets (Slack, Airtable, Google Sheets, and others depending on what's configured on your account). The dataset is always written in full regardless of delivery settings.

Try it yourself: Google Maps Business Leads Scraper & Sales Intelligence on Apify start with a small maxResults value (5–10) to see the output shape before scaling up a run.

Building an AI Agent Workflow with a LinkedIn MCP Connector

Techforce Global — Wed, 26 Aug 2026 08:00:00 +0000

Most tutorials on MCP cover the protocol in the abstract the spec, the message format, a toy example. This post is the opposite: a working walkthrough of wiring a real MCP connector (this actor, which finds LinkedIn decision makers) into an agent, plus the API-level details for anyone who wants to call it directly instead of through an MCP client.

If you've read the protocol docs and still aren't quite sure what an actual integration looks like end to end, this should close that gap three concrete paths (MCP client, direct API, real-time endpoint), each with working code, so you can pick whichever fits your existing stack rather than starting from the spec every time.

The two directions of MCP on Apify, and which one this uses

Apify's own MCP server exposes actors as tools TO outside AI clients Claude, Cursor, and others discover and call Apify actors as part of their toolset. MCP connectors work in the opposite direction: an actor calls OUT to a third-party service (Notion, Slack, Jira) on the user's behalf. This actor supports both it can be called as a tool by an agent, and it can push its own results out via a connector. Understanding which direction you're working with matters, since the setup differs, and mixing them up is the most common early confusion when first working with MCP-enabled actors.

Connecting via MCP client config

If you're using Claude Desktop, Cursor, or another MCP-compatible client, connecting to Apify's hosted MCP server looks like this:

{ "mcpServers": { "apify": { "url": "https://mcp.apify.com", "headers": { "Authorization": "Bearer YOUR_APIFY_API_TOKEN" } } } }

Once connected, the agent can discover and call this actor (and any other Apify actor) as a tool, passing companyName as an argument the same way it would call any other function.

Calling it directly via API Python

import requests

api_token = 'YOUR_APIFY_API_TOKEN'
actor_id = 'techforce.global~linkedin-company-decision-makers'

response = requests.post(
    f'https://api.apify.com/v2/acts/{actor_id}/runs',
    headers={'Authorization': f'Bearer {api_token}'},
    json={'companyName': 'Acme Corp', 'exactMatch': True}
)
dataset_id = response.json()['data']['defaultDatasetId']`

## Using the real-time Standby endpoint Node.js
For agent workflows that need an instant answer rather than waiting on an async run, the Standby mode exposes a live HTTP endpoint:

`const response = await fetch(
  'https://techforce-global--linkedin-company-decision-makers.apify.actor/lookup',
  {
    method: 'POST',
    headers: {
      'Authorization': 'Bearer YOUR_APIFY_TOKEN',
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({ companyName: 'Acme Corp' })
  }
);
const decisionMakers = await response.json();
console.log(decisionMakers[0]);
// { name, linkedin_url, title, location, headline }

The Standby endpoint is capped at a small number of free profiles for testing, with a higher limit on paid plans worth checking current limits before building a high-volume real-time integration around it.

Setting up an MCP connector to push results to Notion

Go to Apify Account Settings → API & Integrations → MCP connectors
Authorise a new connector, selecting Notion Apify provides automatic OAuth setup for Notion, so this is a one-click authorisation, not a manual API key exchange
When configuring a run of this actor, select the authorised Notion connector from the picker the actor will push results into the connected Notion database automatically on completion

Output schema reference

Authentication and credential handling

For the MCP client and direct API paths, your Apify API token authenticates the request standard bearer token auth, available from Settings → Integrations in your Apify account. For the connector path specifically, your Notion/Slack/Jira credentials never pass through this actor's code at all Apify's platform holds the authorised connection and injects it server-side at runtime, which is worth knowing if credential handling is something your security team asks about before approving an integration.

Error handling

As with any scraping-based actor, individual lookups can return no results a company with no meaningful LinkedIn presence, or a name specific enough that exact matching finds nothing. Check for an empty result set explicitly in your integration code rather than assuming every lookup returns at least one decision maker.

For the async batch path specifically, poll the run status endpoint until it reports SUCCEEDED before fetching the dataset treating FAILED or ABORTED runs as an explicit case in your code avoids a pipeline silently proceeding with empty or partial data.

Common integration patterns

Agent-triggered lookup : an agent calls this as an MCP tool mid-conversation, using the Standby endpoint for a fast response
Scheduled enrichment : a batch run against a fixed list of target companies, on a weekly or monthly schedule, pushing results to Notion via connector
Event-triggered : a new row in a CRM or spreadsheet triggers a real-time lookup via a webhook-driven automation (n8n, Make, or a custom function)

The scheduled-enrichment pattern is worth a closer look for teams managing an ongoing pipeline rather than a one-time list. Wiring this actor into a weekly n8n or Make workflow trigger, run, fetch results, push to Notion via the connector turns decision-maker research from something someone remembers to do manually into something that simply happens, with the team only reviewing the output rather than generating it.

Frequently asked

Do I need to use the MCP connector, or can I just get a normal dataset?
Both are supported connector output is optional, not required. Skip it entirely and use the standard Apify dataset if you don't need direct third-party delivery.

Can I chain this with other Apify actors in one agent workflow?
Yes, since Apify's MCP server exposes all actors as tools, an agent can call this actor and any other actor (like a contact-enrichment tool) in the same session, chaining outputs together.

What's the rate limit on the Standby endpoint?
This depends on your Apify plan tier check current limits in your account before building a high-frequency real-time integration.

Can I test the connector setup without a real Notion/Slack workspace?
Not directly for the connector delivery itself, since it requires an authorised real connection but you can fully test the actor's data output via the standard dataset API first, then add the connector once you're confident in the data, decoupling the two concerns during development.

Getting started

Full documentation and the interactive input schema:
LinkedIn Decision Makers MCP Connector for Notion & Slack

Building an Agency Sourcing Pipeline with the TopDevelopers.co API

Techforce Global — Wed, 12 Aug 2026 09:47:15 +0000

If you're building an internal tool for vendor sourcing, CRM enrichment, or partnership discovery, pulling agency data from TopDevelopers.co programmatically beats manual research the same way any API beats manual browsing but the interesting part isn't the extraction, it's what you do with the enrichment layer once you have it. This post covers the actor's input/output schema, two full working integration examples, and a few practical patterns for using this as part of a larger pipeline rather than a one-off pull.

Most agency-directory scrapers stop at raw listing data name, rating, maybe a website. What makes this API worth integrating rather than just running manually through the Apify Console is the enrichment layer: a real email pulled from the agency's own site rather than a directory contact form, and an insights object that turns unstructured review text into something you can actually filter and sort on programmatically.

Authentication

Like any Apify actor, this runs through the standard Apify REST API. You'll need an API token from your Apify account under Settings → Integrations, passed either as a Bearer header or a query parameter depending on which endpoint you're calling.
Input schema

At least one of searchQuery, companyName, or selectedDomain must be set.
**Starting a run Python*

import requests

api_token = 'YOUR_APIFY_API_TOKEN'
actor_id = 'techforce.global~it-agency-lead-finder-enricher-topdevelopers-co'

response = requests.post(
    f'https://api.apify.com/v2/acts/{actor_id}/runs',
    headers={'Authorization': f'Bearer {api_token}'},
    json={
        'searchQuery': 'Flutter developers in UAE',
        'maxResults': 50,
        'maxReviewsPerCompany': 5
    }
)
dataset_id = response.json()['data']['defaultDatasetId']

Fetching and filtering results

import time

def wait_for_run(run_id, token):
    while True:
        status = requests.get(
            f'https://api.apify.com/v2/actor-runs/{run_id}',
            params={'token': token}
        ).json()['data']['status']
        if status == 'SUCCEEDED': return True
        if status in ('FAILED','ABORTED','TIMED-OUT'): return False
        time.sleep(5)

agencies = requests.get(
    f'https://api.apify.com/v2/datasets/{dataset_id}/items',
    params={'token': api_token}
).json()

top_agencies = [a for a in agencies if a.get('rating', 0) >= 4.5 and a.get('email') != 'NA']
print(len(top_agencies), 'qualified agencies with email')

Node.js pushing enriched profiles to a CRM

const { ApifyClient } = require('apify-client');
const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });

const run = await client.actor(
  'techforce.global/it-agency-lead-finder-enricher-topdevelopers-co'
).call({
  selectedDomain: 'Ecommerce Development',
  selectedCategory: 'platform:shopify',
  maxResults: 30
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
const withBudgetFit = items.filter(a =>
  a.insights?.common_project_budgets?.some(b => b.includes('$10,001'))
);

await fetch('https://your-crm.example.com/webhook/vendors', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify(withBudgetFit)
});

Output schema full reference

Filtering on the insights object

The insights.common_project_budgets and insights.top_project_types fields are arrays of strings rather than structured ranges, since they're derived from natural-language review content. Matching on substring, as in the Node example above, is the practical approach checking whether a budget bracket string like "$10,001 - $50,000" appears in the array rather than trying to parse them into strict numeric ranges, which risks breaking if the underlying review phrasing varies.

This matters for anyone building automated scoring on top of the raw data: treat insights fields as signals to combine with your own weighting logic, not as a substitute for a full lead-score calculation. A practical middle ground is a simple point system award points for rating above a threshold, more points for a matching budget bracket, more again for a matching project type and sort by total rather than trying to build a single authoritative score into the pipeline itself.

Handling the two-tier pricing in code

Since basic (listing-only) and enriched (full profile) results are priced differently, a cost-conscious integration pattern is to run a broad basic search first to map out the category, then a second, narrower enriched run against just the shortlist that passed initial filtering — rather than enriching every result from the first pass. This mirrors the manual research pattern of browsing broadly first and only digging deep on serious candidates, just automated.

In practice this means two API calls chained together: the first with a higher maxResults and no need for maxReviewsPerCompany depth, the second with a much smaller maxResults (just the shortlist) but a higher maxReviewsPerCompany, since review depth matters more once you're down to serious candidates.

Common integration patterns

Manual pull a BD or procurement team member runs a search through the Apify Console directly and downloads CSV for immediate use, no code required
Scheduled pipeline a cron job or n8n workflow re-runs key categories monthly or quarterly, refreshing a CRM's vendor table automatically
On-demand internal tool an internal sourcing tool calls the API in the background when a user searches, so end users never need to know Apify is involved
Two-stage enrichment a broad basic run followed by a narrow enriched run against the resulting shortlist, as described above, to control cost on large category sweeps

Error handling

As with any scraping-based actor, individual runs can fail due to transient issues an unusually specific query returning no matches, or a temporary network issue on the source site. Explicitly checking for FAILED or ABORTED status rather than assuming every run reaches SUCCEEDED avoids silent failures in a scheduled pipeline going unnoticed. It's also worth logging the input parameters alongside any failure, since a query that returns zero matches often indicates a category name that doesn't quite match TopDevelopers' own taxonomy rather than an actual system failure.

Rate limits and concurrency

The maxConcurrency setting controls how many agency profiles are processed simultaneously within a single run, but if you're running multiple separate API calls in parallel say, one per domain it's worth staggering their start times slightly rather than firing all of them at once, to avoid hitting your account's overall concurrent-run limit, which varies by Apify plan tier.

Frequently asked

*Is there a sandbox for testing integration code without spending on real results? *
Running against a narrow companyName lookup for a single known agency is the practical equivalent real data, minimal cost, enough to validate your parsing logic.

*Can I run multiple domains in parallel? *
Each run is scoped to one search configuration; running separate parallel API calls for different domains works fine and is the standard pattern for broad multi-category research.

*What's a reasonable maxReviewsPerCompany for a first test? *
Something small, like 3-5, is enough to confirm the reviews array structure without spending on a full review pull across every result.

*Does the API support pagination for very large result sets? *
Use the standard Apify dataset offset/limit query parameters when fetching items rather than assuming a single request returns everything, particularly for runs near the 200-result cap.

Getting started
Full documentation and the interactive input schema are on the Apify listing: apify.com/techforce.global/it-agency-lead-finder-enricher-topdevelopers-co

For a first integration test, start with a companyName lookup against one agency you already know, confirm the output shape matches what's documented above, then move to a broader searchQuery or selectedDomain run once your parsing logic is confirmed working end to end.

How Sales Teams Are Automating Hospital and Clinic Contact Discovery

Techforce Global — Fri, 07 Aug 2026 06:33:26 +0000

How Sales Teams Are Automating Hospital and Clinic Contact Discovery

Finding a hospital or clinic's contact information isn't hard it's finding the right ones worth calling that eats time. This closing piece for the campaign covers exactly how sales teams are using scored Google Maps data to shortcut that process.

The old way

Manually searching by specialty and city, opening each listing, checking a website, noting a phone number repeated hundreds of times per territory.

The automated way

One run per category/location combination returns a lead-scored dataset with contact info already structured name, phone, address, rating, review count, and the no-website flag ready to import directly into a CRM or call sheet.

Frequently Asked

*Does this replace a CRM? *
No, it feeds one. Results export cleanly into any CRM or spreadsheet-based workflow.

Try Our Other Actors

For enrichment beyond Google Maps data, pair this with our
Website Contact Scraper. apify.com/techforce.global/website-contact-scraper-emails-phone-numbers-social-links

Getting Started

apify.com/techforce.global/google-maps-healthcare-leads-sales-intelligence-tool

Google Maps Healthcare Leads API Integrating Lead Scoring Into Your Sales Stack

Techforce Global — Wed, 29 Jul 2026 13:45:00 +0000

Most Google Maps scraper APIs return raw extraction name, address, phone, done. If you're piping healthcare leads into a CRM, an outreach tool, or an internal dashboard, raw extraction was never really the hard part. Google Maps already exposes this information to anyone browsing directly; the actual engineering problem is prioritization deciding, programmatically, which of a few hundred results are actually worth acting on.

This actor's API returns a calculated lead score and a has Website boolean alongside the standard fields, so your integration doesn't have to build a scoring layer on top of the raw data afterward. This post covers the API surface, two working integration examples, and a few of the edge cases worth handling before you wire this into production.

Authentication and starting a run

Like any Apify actor, this runs through the standard Apify REST API you'll need an API token from your Apify account, available under Settings → Integrations. Runs are started with a POST request against the actor's runs endpoint, with your search parameters passed as the JSON body.

Starting a run Python

import requests

api_token = 'YOUR_APIFY_API_TOKEN'
actor_id = 'techforce.global~google-maps-healthcare-leads-sales-intelligence-tool'

response = requests.post(
    f'https://api.apify.com/v2/acts/{actor_id}/runs',
    headers={'Authorization': f'Bearer {api_token}'},
    json={
        'category': 'Dental Practices',
        'location': 'Miami, FL',
        'websiteFilter': 'No Website Only'
    }
)

run_id = response.json()['data']['id']
dataset_id = response.json()['data']['defaultDatasetId']
print(f'Run started: {run_id}')

Fetching results once the run completes

Runs are asynchronous starting one returns immediately, but the actual scraping happens over the next several minutes depending on result volume. Poll the run status endpoint until it reports SUCCEEDED, then fetch the dataset items.

import time

def wait_for_run(run_id, token):
    while True:
        status = requests.get(
            f'https://api.apify.com/v2/actor-runs/{run_id}',
            params={'token': token}
        ).json()['data']['status']
        if status == 'SUCCEEDED':
            return True
        if status in ('FAILED', 'ABORTED', 'TIMED-OUT'):
            return False
        time.sleep(5)

wait_for_run(run_id, api_token)
leads = requests.get(
    f'https://api.apify.com/v2/datasets/{dataset_id}/items',
    params={'token': api_token}
).json()

top_leads = sorted(leads, key=lambda x: x['leadScore'], reverse=True)
print(top_leads[:10])

Pushing top leads to a CRM webhook Node.js

The official apify-client package handles run polling internally, which simplifies the same workflow considerably in a Node environment

const { ApifyClient } = require('apify-client');

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });

const run = await client.actor(
  'techforce.global/google-maps-healthcare-leads-sales-intelligence-tool'
).call({
  category: 'Clinics & Medical Centers',
  location: 'Chicago, IL',
  websiteFilter: 'No Website Only'
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
const topLeads = items.filter(l => l.leadScore >= 70);

await fetch('https://your-crm.example.com/webhook/leads', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify(topLeads)
});

Understanding the input schema

The actor accepts five parameters, all optional except category and location for a meaningful search. Omitting category returns results across all eight healthcare types for the given location, which is useful for broad market-density research but less useful for targeted outbound prospecting, where narrowing to a single category up front keeps the result set relevant.

Understanding the output schema

The consistent use of 'N/A' rather than null or an empty string across every field is a deliberate choice it means your integration code can treat every field as a non-null string without defensive null-checking on each one individually, which simplifies downstream processing meaningfully once you're handling thousands of records rather than a handful in a manual test.

Handling rate limits and large result sets

For high-volume territories, results are paginated at the dataset level — use the standard Apify offset and limit query parameters when fetching items rather than assuming a single request returns everything. If you're running this across many cities on a schedule, staggering run start times rather than firing them all simultaneously avoids hitting your account's concurrent-run limit, which varies by Apify plan tier.

A note on error handling

Runs can fail for reasons unrelated to your integration code — a temporary Google Maps rate limit, an unusually specific location string with no matching results, or a transient network issue on Apify's infrastructure. Checking for a FAILED or ABORTED status explicitly, rather than assuming every run reaches SUCCEEDED, is worth building in from the start rather than retrofitting after a silent failure goes unnoticed in production.

Common integration patterns

Three patterns cover most real-world usage. The first is a one-off manual pull a sales rep or analyst triggers a run through the Apify Console directly, downloads the dataset as CSV, and imports it into whatever tool they're already using. No code required, and the fastest path for a single territory.

The second is a scheduled batch pull a cron job or n8n workflow triggers a run per territory on a recurring basis (weekly or monthly is typical), fetches results once each completes, and pushes new or changed records into a CRM automatically. This is the pattern that scales past a handful of territories without someone manually re-running searches.

The third is an on-demand API call triggered from inside another application for instance, a custom internal tool where a user picks a city and category from a dropdown, the tool calls this actor's API in the background, and results populate directly in the requesting application's own interface. This pattern requires the most integration work up front but produces the smoothest experience for end users who never need to know Apify is involved at all.

Why the lead score matters for API consumers specifically

If you're integrating this into an automated pipeline rather than reviewing results manually, the lead Score and has Website fields are what make automation actually useful rather than just faster. Without them, an automated pipeline would need to push every single result into a CRM or outreach tool indiscriminately, forcing a human to do the prioritization downstream anyway. With them, you can filter server-side pushing only leads above a chosen score threshold, or only no-website leads and let the pipeline do the triage that would otherwise require manual review.

Frequently asked questions

Does the API support server-side filtering by lead score?
Filtering happens client-side after fetching results, as shown in the examples above the API returns the complete scored dataset per run, and threshold filtering is applied in your own code.

What's the typical run time for a single city/category search?
This varies with result volume, but most single-city searches complete within a few minutes. Larger metro areas with broad categories can take longer, proportional to the number of listings being scored.

Can I run this on a schedule without manual intervention?
Yes, wrap either example above in a cron job, an n8n scheduled workflow, or a cloud function trigger, and the entire flow from run start to CRM push runs unattended.

Is there a sandbox or test mode for development?
Running with a very specific, narrow location (a small town rather than a major metro area) is the practical equivalent it returns real data but a small enough result set to test integration logic without burning through your per-result budget.

What happens if I pass an invalid category string?
The actor validates against the eight fixed categories and returns a clear input validation error rather than silently falling back to an unfiltered search worth handling explicitly in your integration so a typo in a category string fails loudly during testing rather than quietly returning unexpected results in production.

Can results be deduplicated across multiple runs for the same territory?
Deduplication isn't handled automatically at the actor level, since each run is independent if you're running the same territory repeatedly on a schedule, deduplicating on business name and address in your own pipeline before pushing to a CRM avoids creating duplicate records for practices that haven't changed between runs.

Getting started

Full actor documentation and the input schema reference are available on the Apify listing: apify.com/techforce.global/google-maps-healthcare-leads-sales-intelligence-tool. Pricing is pay per result, so testing an integration against a small location costs only what that test run actually returns.

Website : https://techforceglobal.com/

Building a Standardized Multi-Platform Event Scraper: The Architecture Behind Smart Event Scraper

Techforce Global — Thu, 16 Jul 2026 08:00:00 +0000

Building a Standardised Multi-Platform Event Scraper: The Architecture Behind Smart Event Scraper

Event data is scattered by design. AllEvents.in, EventsEye.com, District.in, and Meetup.com each structure their listings differently different field names, different date formats, different ways of representing price and location. Pull from more than one of these sources and you inherit the reconciliation problem: matching fields, renaming columns, deduplicating, before the data is usable for anything downstream.

The Smart Event Scraper by Techforce Global is an Apify actor that solves this by running all four platforms in a single pass and returning one standardised, deduplicated schema regardless of source. This article walks through the technical decisions behind it: why a fixed category taxonomy beats free-text tagging, how the normalisation layer guarantees identical output fields, the full input/output reference, a working n8n pipeline, and code examples for calling it directly from Python or Node.

Try it: https://apify.com/techforce.global/smart-event-scraper

The Core Problem: Four Sources, Four Shapes

Each of the four source platforms exposes event data with a different shape. Dates might be ISO strings on one platform and human-readable text on another. Price might be a number, a currency-formatted string, or simply absent. Location might be a full address or just a city name. None of this is a bug in any individual platform it's just what happens when four independent systems solve the same problem differently.

The naive approach to combining them is to write four separate scrapers and reconcile the output afterward in your own code four sets of field mappings to maintain, four sets of edge cases to handle, and a reconciliation step that has to be re-run mentally every time you add a fifth source.

Why a Fixed Category Taxonomy

Free-text tags look flexible but create long-term inconsistency "tech", "technology", and "Tech & Software" all describe the same events but don't group cleanly when you're filtering across sources. Smart Event Scraper uses six fixed categories Tech & Software, Business & Networking, Food & Beverages, Health & Wellness, Art & Culture, and Education & Learning applied identically across all four platforms. A single category filter reliably returns the same type of event no matter which site it originated from. Leave category blank to pull every event type from all four sources in one run.

How It Works : Four Steps

Step 1 : Parallel source queries: each platform receives the same category, keyword, location, and date-range parameters, queried independently with per-source result limits controlled by maxEventsPerSource.
Step 2 : Field mapping: each platform's native response is mapped onto a fixed 9-field schema (title, date, location, venue, price, url, category, source, description). Fields with no equivalent on a given platform return "N/A" rather than being omitted or left null.
Step 3 : Category normalization: where a platform doesn't natively support the same 6-category system, category is auto-detected from the event title/description and mapped onto the closest fixed category.
Step 4 : Consolidation: all four sources' results merge into a single output list, each record tagged with its source platform, ready for direct import without further transformation.

Input Configuration

{ "category": "Tech & Software", "location": "Berlin", "maxEventsPerSource": 40, "dateFrom": "2026-09-01", "dateTo": "2026-09-30" }

Output Schema

n8n Weekly Event Digest Pipeline

Schedule Trigger : every Monday 9:00 AM
HTTP Request : POST to Apify API to start Smart Event Scraper with category + location + coming week's date range
Wait/Poll : every 60 seconds until run = SUCCEEDED
HTTP Request : GET results from the Apify dataset API
Google Sheets or Slack node : push results, grouped by source

Because every event record shares the same 9-field schema regardless of source, the Slack/Sheets node requires no conditional field mapping.

Frequently Asked

How do I find tech events in a specific city automatically?
Set category to "Tech & Software" and location to your target city the actor pulls matching events from all four platforms in one run.

Why does this return "N/A" instead of leaving fields blank?
So every output record is safe to import directly into a spreadsheet or database without conditional null handling.

Can I call this from a language other than Python or JavaScript?
Yes, it's a standard REST API. Any language that can make an HTTP POST request can start a run and fetch results.

Getting Started

Go to the Smart Event Scraper on Apify apify.com/techforce.global/smart-event-scraper
Click 'Try for Free' : no credit card required
Set category, location, and date range, or call the API directly using either code example above
Click Run : first results in 1-5 minutes

Actor: https://apify.com/techforce.global/smart-event-scraper
Consultation: calendly.com/techforce-infotech-pvt-ltd/intro-meeting
Website: techforceglobal.com

Playwright Bulk Website Contact Extraction Emails, Phone Numbers & Social Links From Any Domain

Techforce Global — Wed, 01 Jul 2026 13:51:41 +0000

Introduction

Bulk website contact extraction pulling emails, phone numbers, and social media links from a list of company domains is one of the most common B2B data tasks. The naive implementation using HTML parsing (Cheerio, BeautifulSoup) is fast but has a fundamental blind spot: it cannot see contact data that loads via JavaScript. The Website Contact Scraper by Techforce Global addresses this with Playwright (headless Chromium), which renders every page fully before extraction and returns a single consolidated, deduplicated record per domain with emails, phones, social links, and a transparent summary count. This guide covers the technical approach, input/output structure, and a complete n8n pipeline for automated CRM enrichment.

Why HTML Parsing Fails on Modern Websites

When a browser loads a modern website, two events happen: the server delivers the initial HTML, then JavaScript executes and renders additional content. HTML parsers like Cheerio process only the first event. Contact sections built with React, Vue, or Angular components render in the second event and are completely invisible to them.
Playwright runs a real Chromium browser, completes both events, and extracts from the final rendered DOM. This is the same approach used by enterprise contact discovery tools — applied here to bulk URL processing on Apify.

How the Website Contact Scraper Works

Step 1 : Playwright Crawler with Full JS Rendering

Each URL gets a headless Chromium browser instance. The actor waits for full page load including JavaScript execution before extraction begins. No timeout shortcuts. Full render, then extract.

Step 2 : Contact Page Auto-Discovery

From the root page, the actor scans all href links for contact-related URL patterns: /contact, /about, /team, /impressum, /kontakt. These pages are always crawled first regardless of depth position they are the highest-value pages for contact data.

Step 3 : Multi-Source Extraction

Emails: two extraction passes raw HTML attributes (mailto: links) and visible rendered text. Phone numbers: visible text only, which is more accurate for formatted international numbers. Social links: matched against platform URL patterns, grouped by platform name in the output object.

Step 4 : Consolidation Per Domain

All data from all pages crawled for a domain is merged, deduplicated, and returned as one record. Summary counts computed. scannedPages list assembled. Status set to success or no_data_found.

Input Configuration

{ "urls": [{ "url": "https://stripe.com" }, { "url": "https://notion.so" }], "maxPagesPerDomain": 10, "maxConcurrency": 5 }

Output Schema Complete Reference

n8n CRM Enrichment Pipeline 7 Steps

Google Sheets Trigger : fires when new domain URLs added to column A of your prospect sheet
HTTP Request : POST to Apify API to start Website Contact Scraper with new URLs
Wait/Poll : every 60 seconds until run = SUCCEEDED
HTTP Request : GET results from Apify dataset API
Filter : status = 'success' AND summary.emailCount >= 1
HubSpot : create or update contacts with extracted emails and social profiles
Google Sheets : write enriched data back to master sheet

The consistent one-record-per-domain output means the HubSpot node requires no conditional field mapping same schema regardless of how many pages were crawled per domain.

Getting Started

Go to the Website Contact Scraper on Apify
Click 'Try for Free'
Paste your URL list maxPagesPerDomain: 10 recommended
Enable Apify Proxy for bot-protected sites
Click Run results in 1–5 minutes depending on URL count

Actor: https://apify.com/techforce.global/website-contact-scraper-emails-phone-numbers-social-links
Consultation: https://calendly.com/techforce-infotech-pvt-ltd/intro-meeting
Website: https://techforceglobal.com

How to Scrape Quince.com Prices, Auto-Calculated Discounts, Reviews, and Images at Scale

Techforce Global — Thu, 18 Jun 2026 09:55:53 +0000

Introduction : Why Quince Data Is Worth Automating

Quince hit $2 billion in annualized revenue in February 2026 after raising a $500 million Series E at a $10.1 billion valuation. The company's manufacturer-to-consumer (M2C) model consistently prices cashmere, linen, silk, denim, and home goods at 50 to 80 percent below traditional retail a pricing strategy that has reset consumer expectations across premium essentials categories.

For e-commerce teams, brand strategists, affiliate publishers, and market researchers working in these categories, Quince's pricing movements are competitive intelligence worth tracking at scale. The challenge: Quince has no official API, no product data feed, and no bulk export mechanism. This guide explains how the Quince.com Product Scraper by Techforce Global the only dedicated Quince scraper on Apify automates data collection across three input modes and how to integrate it into an automated pricing intelligence pipeline.

Three Input Modes When to Use Each

Mode 1 : Fuzzy Keyword Search
The fuzzy_search mode runs your keyword against Quince's search interface. Use this for cross-category research where you want all products of a type regardless of their location in Quince's hierarchy. 'Cashmere' returns results from Women, Men, and Home simultaneously.

{ 
    "mode": "fuzzy_search", 
    "searchQuery": "mongolian cashmere crewneck", 
    "searchLimit": 50, "region": "US" 
}

Mode 2 : Category Browse (300+ Categories)
The category dropdown is built from Quince's live mega-menu, not a static list. New categories Quince adds to their navigation appear in the actor automatically. Navigate to Women > Sweaters > Cashmere or Home > Bedding > Linen with a single dropdown selection.

{ 
    "mode": "category", 
    "category": "/women/sweaters", 
    "categoryMaxProducts": 200, 
    "region": "US" 
}

Mode 3 : Direct Product URLs
The product_urls mode accepts a list of specific Quince product page URLs for precise, repeatable monitoring of competing SKUs on a weekly schedule.

{ 
    "mode": "product_urls", 
    "productUrls": [{ "url": "https://www.quince.com/women/lightweight-cotton-cashmere-dolman-sleeve-sweater" }], 
    "region": "US" 
}

Complete Output Schema Twelve Fields

Why Auto-Calculated Discount % Matters at Pipeline Scale

Most product scrapers return two price strings and leave the calculation to the consumer of the data. At 50 records this is a minor inconvenience. At 2,000 SKUs tracked weekly across five categories, it is a transformation step that must be built, tested, and maintained indefinitely.

The Quince.com Product Scraper returns discount: '49.5%' as a first-class output field. This eliminates the transformation step entirely: sort by discount immediately, filter in n8n where discount > '40%', display on an affiliate site without a formula, compare month-over-month exports without preprocessing.

Primary-Color Images Why the Filtering Matters

Quince products come in multiple color variants. A naive scraper returns all variant images for a cashmere crewneck available in 12 colors with 3 photos each, that is 36 images per product. The Quince.com Product Scraper returns only the primary-color hero images: the images that appear by default when a shopper first arrives on the product page. Clean, immediately usable product imagery without a deduplication step.

n8n Automated Pricing Intelligence Pipeline Step by Step

Schedule Trigger : runs every Monday at 8:00 AM IST
HTTP Request : POST to Apify API to start the Quince Scraper (mode: category, /women/sweaters, max 200, region: US)
Wait/Poll node : checks every 60 seconds until Apify run status = SUCCEEDED
HTTP Request : GET request to Apify dataset API to fetch all product records
Filter node : filter records where discount field is NOT equal to 'N/A' (actively discounted products)
Google Sheets node : append all discounted products to weekly pricing tracker with timestamp
Filter node (second pass) : filter where discount value is greater than '40%'
Slack node : post top deals to #pricing-alerts channel: title, sellingPrice, retailPrice, discount, URL

Practical Use Cases With Example Inputs

Use Case 1 — Weekly Category Benchmarking

Input: 
    { 
        "mode": "category", 
        "category": "/women/sweaters", 
        "categoryMaxProducts": 200 
    }

Schedule: Every Monday 8AM
Result: Complete cashmere sweater catalog with current prices and discount % → append to tracking sheet → compare week-over-week

Use Case 2 — Affiliate Deal Newsletter
Filter: discount > 45% from any category sweep
Output: Top deals with title, sellingPrice, discount, averageRating, productUrl → format for newsletter template
No manual calculation required discount field is already there

Use Case 3 — Competing SKU Monitoring

Input: 
    { 
        "mode": "product_urls", 
        "productUrls": [25 specific competing Quince URLs] 
    }

Schedule: Every Sunday night
Output: Current prices and review scores for each SKU → flag where Quince discount > 30% → inform your own pricing response

Getting Started

Go to the Quince.com Product Scraper on Apify
Click 'Try for Free' — Apify provides free trial credits, no card needed
Select mode, configure input, set limit and region (US or CA)
Click Run — export as JSON, CSV, Excel, or RSS — or connect via Apify API to n8n

Actor: https://apify.com/techforce.global/quince-scraper

Consultation: https://calendly.com/techforce-infotech-pvt-ltd/intro-meeting
Website: https://techforceglobal.com
Email: bhavin.shah@techforceglobal.com

How to Automate DesignRush Agency Research and Outreach in a Single Apify Run

Techforce Global — Wed, 03 Jun 2026 13:58:30 +0000

Introduction

B2B outreach to agencies involves two stages: research and contact. Most tools solve one. The DesignRush Outreach Bot by Techforce Global solves both in a single actor run filtering agencies, scraping full profiles with six review sub-dimensions, then automatically submitting your message to each agency's contact form.

How It Works Technical Overview

Step 1 — Location Filtering via Live UI
Rather than URL parameter hacking, the actor uses DesignRush's live location search UI — typing your location string, scoring autocomplete suggestions, and navigating to the canonical scoped URL. All pagination stays on that scoped URL for server-side filtered results.

Step 2 — Listing + Profile Scraping
Listing fields: name, website, location, rating, hourly rate, employees, slogan, description, profileLink. Profile fields (scrapeProfiles: true): all reviews with 6 sub-ratings (Quality, Cost, Schedule, Timely Delivery, Responsiveness, Satisfaction), services array, clients array, team data.

Step 3 — Contact Form Submission
When contactMode: submit, the actor navigates to each agency's contact page, waits for full form load including CSRF tokens, fills in contactName, contactEmail, contactCompany, contactPhone, contactWebsite, contactMessage, submits, detects outcome (success/failure), and records contactSubmitted: true or false.

Input Configuration

n8n Automated Weekly Outreach Pipeline

Schedule Trigger — every Monday 9:00 AM IST
HTTP Request — POST Apify API to start actor (category + location + contactMessage)
Wait/Poll — until run = SUCCEEDED
HTTP Request — GET Apify dataset results
Filter — contactSubmitted = true only
HubSpot — create lead records for successfully contacted agencies
Google Sheets — log full results with sub-ratings for qualification scoring
Slack — weekly summary: X agencies found, Y contacted successfully

🔗 Try it free: https://apify.com/techforce.global/design-rush--outreach-bot
📅 Consultation: https://calendly.com/techforce-infotech-pvt-ltd/intro-meeting
🌐 techforceglobal.com

How GPT-4o Acts as Your Website's Visual QA Engineer Testing From 15 Countries Across Desktop, Mobile, and Tablet

Techforce Global — Wed, 20 May 2026 13:30:00 +0000

Introduction

Visual QA across multiple countries and device types is one of the most important and most skipped steps in web development. Testing how a website renders across 15 countries, three device viewports, and different locale configurations is simply not something teams do manually at any reasonable cadence.

This article explains how the Visual Verification Agent by Techforce Global uses GPT-4o (OpenAI) or Gemini (Google) to automate visual QA at geo-scale: visiting any website from 15+ global locations, capturing full-page screenshots on Desktop, Mobile, and Tablet, using vision AI to analyse layout quality, and returning a structured score from 0 to 100 with a letter grade and specific defect description.

We cover the technical approach, input configuration, output structure, and a complete n8n integration for automated weekly visual QA monitoring.

Why Geo-Visual QA Is Different From Standard QA

Standard QA checks whether a website works. Geo-visual QA checks whether it looks correct from the perspective of a real user in a specific geography, on a specific device, in a specific locale.

These are genuinely different problems. A website can pass all functional tests and still have layout issues that only appear on mobile in Germany due to a combination of smaller viewport size, German locale font rendering, and CSS behaviour differences on that browser version. These issues require both a user in that geography and a user who notices and reports the problem. In practice, many geo-visual bugs go unreported for weeks or months.

How the Visual Verification Agent Works Technical Overview

Step 1 — Geo-Location Injection With Playwright
The actor uses Playwright with stealth geolocation injection to simulate requests from a target country. This is more accurate than a simple IP proxy — it injects the correct locale settings, timezone, geolocation coordinates, and browser language alongside the fingerprint, creating a realistic simulation of a user in that country.

Step 2 — Human-Like Scrolling to Trigger Lazy Content
Before capturing the screenshot, the actor scrolls the page using variable-speed natural scrolling with reading pauses — mimicking real human browsing behaviour. This ensures all lazy-loaded content (images, components that trigger on scroll, sticky headers, chat widgets) is visible in the screenshot before AI analysis begins. Static screenshot tools miss this content entirely.

Step 3 — Multi-Device Screenshot Capture
Screenshots are captured at three viewport dimensions: Desktop (1366×768px), Mobile (390×844px), and Tablet (768×1024px). Each device type gets a full-page, high-resolution screenshot saved to Apify Key-Value Store.

Step 4 — GPT-4o or Gemini Vision Analysis
Each screenshot is sent to the AI model with a structured visual QA analysis prompt. The model analyses layout integrity, element visibility, content readability, and overall visual quality — then returns a score from 0 to 100, a letter grade, and a plain-language description of any detected defect.

Step 5 — Structured JSON Output Per Device Per Country

Sample output record:
{ url: 'https://example.com', country: 'DE', device: 'mobile', grade: 'C', reason: 'Hamburger menu overlaps the logo. CTA button partially hidden below the fold on small screens.' }

One record per device per country tested
Grade: A+ (90-100) / A (75-89) / B (60-74) / C (below 60)
Reason: specific defect text — actionable, not generic

Input Configuration

GPT-4o vs Gemini — Which Should You Use?

Countries Available

The actor supports 15+ geo-locations including:

United States (US) | United Kingdom (GB) | Germany (DE) | India (IN) | Singapore (SG)
Australia (AU) | Canada (CA) | France (FR) | Japan (JP) | United Arab Emirates (AE)
Netherlands (NL) | Brazil (BR) | South Africa (ZA) | Mexico (MX) | South Korea (KR)

n8n Automated Visual QA Pipeline — Weekly Monitoring

The actor integrates with n8n for fully automated recurring visual QA:

Schedule Trigger — runs every Monday at 9:00 AM IST for pre-week QA check
HTTP Request node — POST to Apify API to start VVA actor (US + DE + IN, all 3 devices)
Wait/Poll node — checks every 60 seconds until Apify run status = SUCCEEDED
HTTP Request node — GET request to Apify dataset API to fetch all output records
Filter node — selects records where grade = C or grade = B
Slack node — posts critical issues to #qa-alerts with country, device, grade, reason
Google Sheets node — logs all results to weekly QA tracking spreadsheet

Result: a weekly QA digest delivered automatically every Monday. Your team sees which countries and devices have issues before users report them.

Practical Use Cases

Pre-Launch Geo-Visual QA
Before any major release: run across your 5 target markets and 3 device types. Any C or B grade is an actionable issue to fix before users see it. 15 test combinations take 5–8 minutes to complete.

Continuous Visual Regression Monitoring
Run weekly on schedule. Get Slack alerts when any new deployment introduces a visual regression. Catch layout breaks before users report them.

Client QA Reports for Agencies
Export structured results showing scores per country and device. Deliver to clients as a PDF or Sheets report. Concrete proof of visual quality that clients can review without technical background.

E-commerce Pre-Sale Event Checks
Before major sale events (Diwali, Black Friday, New Year), run a quick multi-country check to confirm checkout, cart, and product pages render correctly across all target markets. A broken checkout on mobile in the UK during a major sale is significant lost revenue.

Getting Started — 6 Steps

Get a GPT-4o API key from platform.openai.com OR a Gemini API key from aistudio.google.com (both free to start)
Go to: https://apify.com/techforce.global/visual-verification-agent
Click 'Try for Free' on Apify
Enter your website URL, AI model, API key, country, and devices
Click Run — first results appear in 3–5 minutes
Review your grade and defect reason per device per country

🔗 Actor: https://apify.com/techforce.global/visual-verification-agent
📅 Book a consultation: https://calendly.com/techforce-infotech-pvt-ltd/intro-meeting
📧 Contact: bhavin.shah@techforceglobal.com
🌐 Website: https://techforceglobal.com

How to Scrape AllEvents.in, EventsEye.com, District.in, and Meetup.com in a Single Automated Run

Techforce Global — Wed, 06 May 2026 13:30:00 +0000

Introduction

Event data is fragmented. AllEvents.in covers general city events globally. EventsEye.com is the largest B2B trade show directory with 140+ countries. District.in handles ticketed entertainment. Meetup.com surfaces community and professional gatherings. Each platform has different content, different audience, and different data structure.

For developers, analysts, and automation teams that need comprehensive event intelligence, this fragmentation creates two compounding problems: you need to build and maintain four separate scrapers with different logic, and then normalise four different output schemas into one usable dataset.

This guide explains how the Universal Event Scraper by Techforce Global solves both problems with a single Apify Actor and how to integrate it into n8n for a fully automated event intelligence workflow.

Why You Need Data From All Four Platforms

No single event platform is comprehensive enough for serious event intelligence work. Each platform has a distinct audience and content type that the others do not cover.

AllEvents.in
AllEvents.in is one of the most comprehensive general event directories available globally. It covers conferences, workshops, concerts, festivals, and expos across thousands of cities with particularly strong coverage of events in India and Southeast Asia. If you need city-level event data at any reasonable scale, AllEvents.in is the most complete single source.

EventsEye.com
EventsEye.com is a dedicated B2B trade show and exhibition directory covering 140+ countries, organised by industry, region, country, and city. It is the only platform in this set that includes organiser contact details making it essential for B2B outreach to trade show organisers. No general-purpose event scraper includes EventsEye data. This is a meaningful competitive gap that the Universal Event Scraper fills.

District.in
District.in focuses on ticketed entertainment and live events concerts, comedy shows, cultural performances. Its ticket price data is more consistently structured than AllEvents, making it the most reliable source for price-related analysis.

Meetup.com
Meetup.com surfaces community and professional meetups tech groups, startup communities, hobby gatherings, and networking events that do not appear on any of the other three platforms. For teams targeting developer communities, startup founders, or professional groups, Meetup data is often the most relevant.

The Technical Problem: Four Scrapers, Four Schemas

Building individual scrapers for these platforms is not just a one-time cost it is an ongoing maintenance burden. Each platform requires:

Custom scraping logic for its specific HTML structure and dynamic content loading patterns
Custom pagination handling each platform paginates differently and at different scales
Custom date and price normalisation formats vary significantly across platforms
Custom field mapping 'event_title', 'name', 'title', 'eventName' are all used across the four
Separate error handling, retry logic, and proxy management for each source
Ongoing maintenance whenever any platform updates its layout or structure

Even when all four scrapers work correctly, the output is four separate datasets that cannot be merged without an additional ETL step. That ETL step must be rebuilt every time a source platform changes its output structure.

The Solution: Universal Event Scraper on Apify

The Universal Event Scraper handles all four platforms in a single actor run with a unified output schema. No separate scrapers to maintain. No ETL step to build.

Input Configuration
The actor accepts three inputs platform selection, location, and result limit:

Input example:
{ source: ['allevents', 'eventseye', 'district', 'meetup'], location: 'Chicago', limit: 50 }

source — array: select any combination of the four platforms
location — string: city name used across all scrapers
limit — number: maximum events per source (default 50, max 100 per source)

With limit: 50 and all four sources → up to 200 total records in one run

Output Schema 8 Consistent Fields
Every record from every platform returns the same eight fields:

Sample output record:
{ source: 'eventseye', title: 'Tech Expo Chicago 2026', url: 'https://eventseye.com/...', date: 'Mon, 15 Jun 2026', city: 'Chicago', venue: 'McCormick Place', price: 'N/A', venue_address: '2301 S King Dr, Chicago, IL 60616' }

source — which platform (allevents / eventseye / district / meetup)
Fields unavailable for a given source → returned as 'N/A' not null
Same 8 fields every time → no conditional handling needed in your pipeline

Practical Use Cases With Example Inputs

Use Case 1 - B2B Sales: Weekly Event Intelligence for Outreach
Goal: Every Monday, automatically generate a list of upcoming trade shows, conferences, and meetups in your target city for the sales team's outreach calendar

source: ['allevents', 'eventseye', 'meetup']
location: 'London'
limit: 100

Run schedule: every Monday 8:00 AM
Output: up to 300 events across 3 sources filtered by your team for outreach

Use Case 2 - Event Directory Builder: City Guide Data Feed
Goal: Maintain a comprehensive event directory for a city guide app all event types, updated weekly.

source: ['allevents', 'district', 'meetup']
location: 'Bangalore'
limit: 50

Run schedule: every Friday 6:00 PM
Output: up to 150 events → direct database insert, no transformation needed

Use Case 3 - Trade Show Organiser Outreach (EventsEye Only)
Goal: Pull trade shows in a target industry with organiser contact details for a B2B partnership outreach campaign.

source: ['eventseye']
location: 'Frankfurt'
limit: 100

Output: up to 100 exhibitions with organiser name, contact details, event website

n8n Workflow Integration Fully Automated Event Intelligence Pipeline

The actor connects to n8n via the Apify HTTP API. Here is a complete weekly automated pipeline:

Schedule Trigger node fires every Monday at 8:00 AM IST
HTTP Request node POST to Apify API to start the Universal Event Scraper actor run
Wait node polls every 30 seconds until run status = SUCCEEDED
HTTP Request node GET request to Apify dataset API to fetch all records
Google Sheets node appends all new records to your master event tracking sheet
Filter node filters source = 'eventseye' records for trade shows with contacts
HubSpot / CRM node creates lead records from EventsEye organiser contacts

Getting Started

Go to: https://apify.com/techforce.global/universal-event-scraper
Click 'Try for Free'
Select your platforms, enter your city, set your result limit
Click Run — results ready in 3–5 minutes
Export as JSON, CSV, or Excel — or connect to n8n via the Apify API

🔗 Actor: https://apify.com/techforce.global/universal-event-scraper
📅 Book a consultation: https://calendly.com/techforce-infotech-pvt-ltd/intro-meeting
📧 Contact: bhavin.shah@techforceglobal.com
🌐 Website: https://techforceglobal.com