DEV Community: InterCode

What Building with Claude Actually Looks Like in Practice

InterCode — Thu, 24 Sep 2026 12:30:34 +0000

There's a lot of talk right now about "AI-powered development." Most of it stays vague.

A demo here. A chatbot widget there. A blog post about "the future of coding."

We wanted to write something more concrete instead: what it actually looks like to build a production system with Claude, end to end, for a real paying client.

At InterCode, we're an AI development studio. Our team is made up of CCAR-F Claude Certified Architects by Anthropic.

That means Claude isn't a side experiment for us. It's part of how we architect systems from day one.

In practice, that usually means Claude shows up at more than one layer of a project. Not just as a chatbot bolted onto a finished product afterward.

It's part of how the system gets built. Part of how it makes decisions once it's live. Sometimes even part of how it looks.

We've done this enough times now to have a pretty clear picture of where each layer actually earns its place. Here's a breakdown of one project where all three show up.

Where Claude fits into the stack

We don't bolt on "an AI feature" as an afterthought. On this project, Claude runs through three distinct layers:

1. Claude Code — for building the thing itself Our engineering team used Claude Code throughout development: designing the microservice architecture, working through the data pipeline design, and writing backend and frontend code. This is the layer most engineers reading this will recognize - an AI pair-programmer embedded in the actual dev workflow, not just an autocomplete suggestion.

2. Claude API — for the product's own intelligence This is where it gets more interesting. The Claude API powers several in-product features that the client's marketing team now relies on daily:

Turning property specials into optimized ad copy automatically
Classifying and excluding negative keywords, using property-level context instead of generic keyword lists
Ingesting emailed reports and alerts to surface anomalies and recommended actions - without a human reading every report line by line
Powering an internal natural-language chatbot so the marketing team can just ask the data questions instead of building a new dashboard filter every time

**3. Claude for the UI/UX design work **Even the design side of the platform leaned on Claude — from early UI exploration to refining the actual dashboard experience users interact with daily.

The point isn't "we used AI everywhere for the sake of it." Each layer solves a specific, boring, expensive problem: writing ad copy by hand doesn't scale, reviewing keyword lists by hand doesn't scale, reading every alert email by hand doesn't scale. Claude removes the manual step at each of those points, with engineers still supervising the output.

This particular project is a BI and marketing-automation platform we built for Street Digital, a digital marketing agency serving luxury apartment communities — replacing a no-code setup that worked fine early on but couldn't keep up once their client base grew.

Why this matters beyond one case study

Most companies don't need "an AI feature" bolted onto an existing product.

What they actually need is someone to look at the real bottlenecks. The manual ad copy. The manual keyword review. The manual report reading.

Then figure out which of those a properly supervised AI agent can take off someone's plate entirely — and which ones still need a human in the loop.

That part is genuinely hard to get right.

It's where having architects who understand both the engineering side and Claude's actual capabilities makes the difference. Not treating Claude as a black-box API call. Treating it as part of the architecture.

Why Modern Platforms Are Switching to AI Automation (And How to Do It Right)

InterCode — Thu, 24 Sep 2026 11:44:14 +0000

Technology moves fast, and staying competitive today means one simple thing: speed. Customers no longer want to wait hours or days for an email reply just to check prices or campaign options. At the same time, forcing your sales and support teams to spend hours on repetitive, manual tasks is expensive and hard to scale.

At InterCode, we regularly see how moving toward smart AI automation helps businesses take over repetitive tasks and free up human teams for strategic growth.

Here is why AI automation matters, how it saves hours of work, and how it operates in practice.

1. Freeing Teams from Monotonous Routine

The biggest value of AI isn't just about using new tech - it is about removing everyday bottlenecks.

In traditional setups, sales reps often spend nearly an hour on a single call just guiding a client through basic catalog options and schedules. Doing this manually for every customer makes scaling impossible. To make matters worse, any client browsing your site after work hours usually leaves without an answer because no one is online to help.

Smart AI agents fix this effortlessly:

Instant self-service: Visitors get immediate answers 24/7, turning casual traffic into active leads while your team sleeps.
Massive time savings: Routine tasks that used to take 45 minutes are wrapped up in under 10, letting your sales team focus on closing big deals instead of answering basic questions.

2. Beyond Simple Bots: How Multi-Agent Systems Work

Standard chatbots usually break down during longer conversations - they lose context, give generic replies, or make things up.

Modern AI works differently. Instead of one single bot, it relies on a team of specialized AI agents that work together in the background while keeping full track of the conversation:

Dispatcher Agent: Greets users, captures their contact details on the spot, and saves them to your database.
Knowledge Provider Agent: Connects directly to your internal knowledge base to give accurate, up-to-date answers about products, inventory, and pricing.
Smart Guardrails: Filters out irrelevant topics to keep the conversation strictly professional and focused.

For example, when we built our AI Booking Multi-Agent Chatbot for the advertising industry, this multi-agent structure allowed the platform to handle complex billboard searches and booking inquiries automatically without losing data along the way.

3. Smooth Hand-offs to Human Teams

AI isn't here to replace your team — it is here to make them faster and more effective.

Automatic Lead Routing: When a complex request comes in, the system instantly generates a high-priority ticket in your CRM (such as Zoho) and alerts your team.
Ready-to-Close Context: Instead of reading through long chat histories, your sales managers get a short, clear summary of what the customer needs so they can step in immediately and close the deal.

This always-on setup helps platforms respond instantly and double their conversion rates without increasing headcount.

Key Takeaway

Adding AI agents to your business isn't about replacing people; it's about eliminating boring, repetitive tasks. When AI handles initial inquiries and data checks around the clock, your business moves faster, saves money, and scales with ease.

How We Built AI Agents to Eliminate Manual Copywriting and Ad Management at Scale

InterCode — Thu, 24 Sep 2026 11:07:18 +0000

Managing high-volume ad campaigns across multiple platforms often feels like an endless loop of repetitive tasks. Team members spend hours writing similar ad variations, manually excluding negative keywords, and digging through scattered reports to figure out why a campaign's Cost Per Lead (CPL) suddenly spiked.

When our team at InterCode took on the challenge of transforming a complex marketing platform, our goal was clear: eliminate repetitive manual workflows without sacrificing human oversight.

Here is how we designed AI-driven automation workflows that handle copywriting, negative keyword classification, and natural-language data analysis at scale.

1. Automated Ad Transformation (Goodbye, Manual Copywriting)

One of the biggest time sinks in multi-campaign management is turning special offers, product updates, or local promotions into platform-ready ad copy.

The Challenge:
Marketing teams manually wrote and adapted ad variants for each channel (Search, Social, Display) whenever a new promo launched.

The AI Solution:
We built an automated pipeline using LLM integrations that (which we implemented as part of our Street Digital Media project) that:

Ingests structured offer data directly from internal management systems.
Transforms promos into optimized ad copy tailored to the specific character limits and formatting requirements of target platforms.
Pushes approved copy directly to campaign management systems, drastically reducing launch times from hours to seconds.

2. Context-Aware Negative Keyword Classification

Broad negative keyword lists often block relevant traffic or fail to stop budget waste because they lack real-time context.

The Challenge:
Static negative keyword lists don't account for dynamic variables like changing inventory, localized pricing, or shifting service availability.

The AI Solution:
Instead of relying on rigid, pre-made keyword lists, we implemented contextual AI classification:

Real-time context checks: The system analyzes contextual parameters (e.g., current pricing, unit mix, geographic availability).
Smart exclusion: AI automatically flags and excludes negative search queries that don't match the active product state.
Budget protection: By preventing irrelevant clicks before they happen, ad spend is automatically preserved for high-intent users.

3. Conversational Data Analysis with Natural Language

Data without quick accessibility creates bottlenecks. Marketers often wait on analysts to query databases or build custom dashboards just to answer basic performance questions.

The Challenge:
Dashboards can be overwhelming, and finding specific insights (like root causes for performance drops) takes time.

The AI Solution:
We integrated an internal LLM chatbot connected to unified reporting data:

Ask in plain English: Team members can ask direct questions like "Why did CPL spike last week?" or "Which channel brought the highest return this month?"
Instant, data-backed answers: The system queries pre-processed data and returns clear, conversational explanations alongside actionable recommendations.

Need your feedback on my SaaS startup

InterCode — Thu, 06 Mar 2025 01:17:37 +0000

Guys, need your help!

I'm a founder of Startup - SaaS Starter Kit. I launched 3 months ago and my goal of posting here is to get feedback, customers and user testers. Here we go - http://startsaas.com/

It is a development boilerplate custom solution aimed to save ~$20'000 and 2.5-3 months of initial custom development of SaaS, CRM, HRM, and Project Management tool.

Functionality modules:

Authentication
Dashboard
User or Teams subscription model
Admin mode support
File storage management
Internationalization
Stripe integration
Subscription management
Responsive design
DataGrid