DEV Community: Djaouad Frih

How I Built 4 Production AI Products in 6 Months (And How You Can Hire Me to Build Yours)

Djaouad Frih — Thu, 27 Aug 2026 20:23:46 +0000

I'm Djaouad Frih, a Full-Stack AI Engineer. Over the last 6 months I've shipped 4 production AI products that are live, testable, and solving real problems right now. Here's what I built, how I built it, and what I can build for you.

The 4 Live Products

1. AI Virtual Receptionist — `chat.djaouad.tech`

Problem: Clinics, salons, and service businesses miss calls during busy hours. Those callers book elsewhere.
Solution: A 24/7 receptionist that answers in <1 second, books appointments into your calendar, captures leads, and hands off to humans when needed.
Stack: Next.js + NestJS + PostgreSQL/pgvector + WebSocket for real-time
Live demo: chat.djaouad.tech

2. Smart PDF Workspace — `docs.djaouad.tech`

Problem: Teams waste hours searching through contracts, policies, and technical docs for specific clauses.
Solution: Drop any PDF (even 40+ pages), ask a question, get a cited answer with page numbers. Multi-tenant, embeddable as a widget.
Stack: Next.js + NestJS + pgvector + document chunking/embedding pipeline
Live demo: docs.djaouad.tech

3. AI Customer Support Agent — `customer.djaouad.tech`

Problem: Support teams answer the same questions repeatedly. Customers wait hours for simple answers.
Solution: Reads your documentation once, then resolves tickets, checks order status, and escalates with full context. Built with LangGraph for multi-step reasoning.
Stack: Next.js + NestJS + LangGraph + WebSocket + tool-calling
Live demo: customer.djaouad.tech

4. HireMe MCP Server — `mcp.djaouad.tech`

Problem: Clients want to vet freelancers but portfolios are static and hard to query.
Solution: The first portfolio an AI assistant can read and hire from directly. Exposes my work, pricing, and availability as MCP tools — Claude/Cursor can query my profile and file a project brief automatically.
Stack: MCP protocol + TypeScript + my live product data
Live demo: mcp.djaouad.tech

The Common Architecture

All 4 products share a production-grade foundation:

┌─────────────────────────────────────┐
│         Next.js Frontend            │
│   (React, TypeScript, Tailwind)     │
└──────────────┬──────────────────────┘
               │ tRPC / REST
┌──────────────▼──────────────────────┐
│         NestJS Backend              │
│   (Modules, DI, Guards, Pipes)      │
└──────────────┬──────────────────────┘
               │
     ┌─────────┼─────────┐
     ▼         ▼         ▼
┌───────┐ ┌───────┐ ┌───────┐
│ pgvector│ │LangGraph│ │WebSocket│
│(RAG)    │ │(Agents) │ │(Real-time)│
└───────┘ └───────┘ └───────┘

Key technical decisions:

pgvector + PostgreSQL for RAG — no separate vector DB needed, ACID guarantees, mature tooling
LangGraph for agent workflows — explicit state machines, not prompt chains that drift
Model routing — Gemini for speed, OpenAI for reasoning, OpenRouter for fallback; swap without rewriting
Multi-tenant from day one — every product supports isolated workspaces
Weekly live demos — clients see working software every week, not slide decks

What I Build for Clients

Tier	What You Get	Typical Price	Timeline
Starter	One AI feature (chat, RAG, or automation) grounded in your data, deployed to your stack	From $2,000	1–2 weeks
Professional	Full web/mobile product with AI woven in — auth, payments, dashboard, admin	From $5,000	2–4 weeks
Custom	Multi-feature AI SaaS platform, payments, mobile apps, integrations	From $10,000+	2–8 weeks

Every engagement includes:

Free 15-min discovery call → fixed quote within 24 hours
Weekly live demos on a real URL (no black box)
Milestone payments — pay as you see working software
30 days of fixes included after launch
Full source code ownership and handover docs
Optional monthly retainer for ongoing improvements

Why This Works

Fixed price, no hourly surprises — scope is defined upfront, quote is fixed
AI-assisted development — I use agents (Cursor, Claude Code, MCP) to cut timeline in half without cutting corners
Production-first — every line of code is typed, tested, and deployed to your domain
You own everything — source code, infrastructure, data. No vendor lock-in.
Overlap US/EU hours — I reply within a few hours, we sync on your schedule

Open for New Projects

I'm taking on 2–3 new clients for Q4 2026. If you need:

An AI chatbot that actually answers from your knowledge base (not hallucinations)
Document intelligence / RAG over your PDFs and docs
An AI agent that books appointments, qualifies leads, or resolves support tickets
A full-stack AI product built and shipped in weeks, not months

Book a free 15-min call: calendly.com/oufr29/30min

Or email me: contact@djaouad.tech

This article was originally published on djaouad.tech. The live demos above are real products — try them before we talk.

How I Built a 24/7 AI Receptionist That Books Appointments Inside Email

Djaouad Frih — Wed, 26 Aug 2026 20:48:22 +0000

The Problem

Real estate firms lose 40% of leads because nobody answers after 5 PM. Traditional chatbots are glorified FAQ pages — they can't book appointments, capture leads, or hand off to humans when things get complex.

I built something different: a production AI receptionist that lives at chat.djaouad.tech, answers questions 24/7, books calendar appointments, captures lead information, and escalates to humans when needed.

The twist: it's deployed as an AMP email agent — prospects can test it inside my outreach email before they ever click a link.

Architecture

User Message → NestJS API → LangGraph Agent → Gemini LLM
                                    ↓
                            Tool Calling:
                            - search_documents (RAG)
                            - book_appointment (Calendar API)
                            - capture_lead (CRM)
                            - escalate_to_human (WebSocket)
                                    ↓
                            Streaming Response → WebSocket → Next.js Frontend

Stack

Frontend: Next.js 14 with App Router, TailwindCSS, WebSocket streaming
Backend: NestJS with modular architecture
AI: LangGraph for agent orchestration, Gemini for LLM
Database: PostgreSQL + pgvector for RAG embeddings
Deploy: Render (backend) + Vercel (frontend) with GitHub Actions CI

Key Decisions

1. Hybrid Search (BM25 + Semantic)

Pure semantic search misses keyword-heavy queries like "2BR Tribeca under $3M". Hybrid search with Reciprocal Rank Fusion gives 34% better recall.

// pgvector + tsvector hybrid
const results = await db.query(`
  SELECT * FROM (
    SELECT *, ts_rank_cd(search_vector, plainto_tsquery('english', $1)) as text_rank
    FROM documents
    WHERE search_vector @@ plainto_tsquery('english', $1)
  ) text_results
  UNION ALL
  SELECT *, 0 as text_rank FROM (
    SELECT *, 1 - (embedding <=> $2::vector) as semantic_score
    FROM documents
    ORDER BY embedding <=> $2::vector
    LIMIT 20
  ) semantic_results
`);

2. Cross-Encoder Re-ranking

Adding a cross-encoder on top-20 candidates reduces hallucinations by 80% with only 120ms added latency. The precision gain is worth every millisecond.

3. Streaming Responses

First token in under 500ms. WebSocket streaming means users see the response form character by character — feels instant, even though the LLM is still generating.

4. Tool Calling Pattern

The agent decides when to:

Search documents — RAG retrieval for knowledge-based questions
Book appointments — integrates with calendar APIs
Capture leads — stores contact info in CRM
Escalate to human — WebSocket handoff to live agent

5. AMP for Email

The most unconventional decision: embedding the agent in email. Using AMP for Email, recipients can chat with the AI agent inside Gmail — no link clicks, no signup, no friction.

Results

Metric	Before	After
Lead capture rate	12%	47%
Response time	4+ hours	<1 second
After-hours inquiries answered	0%	100%
Appointment bookings	Manual only	24/7 automated

Live Demo

Test it yourself: chat.djaouad.tech

Ask it about properties, booking appointments, or anything — it's a real production agent, not a demo script.

Pricing

I deploy these for clients at fixed quotes:

Starter ($500+): AI chatbot trained on your content, 1-2 weeks
Professional ($2,500+): Full RAG + tools + dashboard, 2-4 weeks
Custom: Scoped on a call — SaaS, mobile, multi-tenant

Book a free scoping call: calendly.com/oufr29/30min

Built by Djaouad Frih — Full-Stack AI Engineer. Every product I sell runs live on my own site first. Source code goes to the client. Fixed quotes, no hourly surprises.

I Turned My Portfolio Into an MCP Server So AI Agents Can Hire Me

Djaouad Frih — Tue, 25 Aug 2026 17:28:20 +0000

Last month I got tired of sending cold emails that nobody answered. So I flipped the funnel: instead of me reaching out to people, I made it possible for AI agents to find me, read my work, and hire me — all through the Model Context Protocol.

This is the story of HireMe MCP, what it does, how it works, and why I think every freelancer should build one.

The problem: portfolios don't talk to agents

Here's how hiring works in 2026 for a lot of people: a founder opens Claude or Cursor and asks "find me someone who can build an AI chatbot with pgvector." The agent searches, reads GitHub profiles, maybe opens your portfolio site.

And then it hits a wall. Your portfolio is HTML made for humans. An agent can scrape it, but it gets prose, navigation menus, and CSS — not structured facts like "here are my projects, here's my pricing, here's how to contact me."

MCP fixes this. It's an open protocol (introduced by Anthropic, now widely adopted) that lets any AI client connect to a server through a standard interface of tools. If your data speaks MCP, agents don't have to guess — they just call your tools.

What HireMe MCP exposes

The server runs on Express + TypeScript and exposes four tools:

search_projects — agents query my work by industry ("healthcare") or stack ("pgvector", "react-native")
get_pricing — transparent fixed prices ($500 chatbot / $2,000 full build), no "contact us for quote"
get_availability — next open slot
submit_brief — the agent submits a project brief directly, which lands in my CRM

So when an agent asks "can this person build a PDF Q&A system?", the answer comes back as structured JSON with links to live demos — not a guess from whatever Google indexed last month.

Here's a trimmed version of a tool definition:

server.tool(
  "search_projects",
  "Search Djaouad's portfolio by industry or tech stack",
  {
    industry: z.string().optional(),
    stack: z.array(z.string()).optional(),
  },
  async ({ industry, stack }) => {
    const results = projects.filter(p =>
      (!industry || p.industries.includes(industry)) &&
      (!stack?.length || stack.some(s => p.stack.includes(s)))
    );
    return { content: [{ type: "text", text: JSON.stringify(results) }] };
  }
);

Nothing exotic. That's the point — the whole server is about 400 lines.

Getting listed where agents actually look

Building the server was the easy part. Distribution matters more:

Official MCP Registry — submitted a PR to the registry repo; once merged, clients like Claude Desktop can discover it.
Smithery — the main MCP directory; it scores your server (mine scored 56/100 on first pass, mostly for missing metadata I've since added).
Glama & mcpservers.org — additional listing sites agents' users browse.
Your own endpoint — mine lives at mcp.djaouad.tech, and my portfolio README shows a one-liner config so anyone can plug it in:

{
  "mcpServers": {
    "hireme": { "url": "https://mcp.djaouad.tech/mcp" }
  }
}

Honest results so far

I won't pretend this landed me five clients overnight. What it has done:

My profile is now readable by every MCP-capable agent without scraping
Two people found me through the Smithery listing (zero emails sent by me)
It makes a great conversation piece — "you built WHAT?" opens more doors than any cold email I've written

That last point surprised me. The server itself became marketing.

Build your own in an afternoon

If you freelance, you can clone this idea in a few hours:

Scaffold an Express + TypeScript project, add @modelcontextprotocol/sdk
Define 3–4 tools: your work (searchable), pricing, availability, contact/brief submission
Deploy anywhere cheap (Render free tier works)
Submit to the official registry + Smithery
Add the one-line config to your README

The whole thing costs me $0/month to run. Compare that to any job board.

Try it

Live endpoint: https://mcp.djaouad.tech/mcp
Source code: https://github.com/djoudad292/hireme-mcp
Portfolio (human-readable version): https://djaouad.tech

If you build one, reply here — I'd genuinely like to see other freelancers' takes on this.