What kind of work do you take on?

Mostly agentic AI and the systems around it: multi-agent workflows, voice agents, RAG over your own docs, and automations that run in production. I build the product too when you need it, web, mobile, or API. I work in whatever stack you already have.

Do you build multi-agent systems?

Yes, that's the core of what I do. LangGraph, CrewAI, or whatever your team standardizes on. Tools wired through MCP, runs traced in LangSmith, and human checkpoints when something needs approval before it goes out.

Can you build a voice agent that books and hands off to a person?

Yes. On FrontDesk AI the voice agent takes inbound calls, checks a live calendar, books, sends an SMS confirmation, logs to the CRM, and transfers when it gets stuck. Same setup works with Vapi, Retell, or Twilio.

We already use n8n, Make, or Zapier. Can you work in that?

Yes. I've shipped eight n8n workflows with approval steps and proper error handling on FrontDesk AI alone. Make and Zapier are the same idea, different triggers. I fit the tool you pay for, not the other way around.

Do you only do AI, or full product builds too?

Both. A lot of the time the AI sits inside a normal app: Next.js on the front, FastAPI or Node on the back, mobile if you need it. I don't stop at the model demo.

Can you build RAG over our private documents?

Yes. Vector search plus keyword search, reranking, inline citations, and it refuses when your docs don't actually cover the question. That pattern is live today on legal research and clinic document Q&A.

We need more than prompts. What does that look like with you?

Orchestration, tool calls, eval in LangSmith, plus the API and UI around it. Not a chain of prompts in a notebook. I'm a core engineer on a $10M+ funded startup with paying users, so I'm used to shipping, not prototyping.

What clouds and tools have you shipped on?

AWS, Firebase, Supabase, PostgreSQL, Azure when clients require it. On the AI side: LangGraph, CrewAI, Vapi, n8n, and the usual stack. I adapt to what you host on rather than forcing my preferences.

What industries have you worked in?

Legal, healthcare, e-commerce, edtech, SaaS, marketing. The moving parts are similar: agents, retrieval, automations. Domain specifics I pick up quickly from your team.

Are you available, and how do we start?

Open to select projects, remote across US and EU time zones (UTC+5). Send a few lines on what you're building through the form below or email hello@rajajahanzaib.dev. I usually reply within a day.

Raja Jahanzaib / work

case study · 2024

Strongsuit

The legal research and AI assistant module inside Strongsuit, a legal-tech platform with live, paying litigators.

Core AI engineerLegal tech · Multi-LLM · RAGlive site ↗

80%: faster legal research
5 LLMs: routed per task
Live: paying litigators

The problem

Litigators spend hours on research that is mostly retrieval and synthesis. Strongsuit needed an assistant that could pull from real legal sources, reason across several models, and return grounded answers fast enough to change how a firm works, at production reliability rather than demo quality.

What I built

01
The legal research and general AI assistant module used by active, paying litigators.
02
Multi-LLM routing across GPT-4o, Claude, Gemini, Grok, and Mistral, so each task runs on the model that handles it best.
03
FastAPI microservices with Redis and Arq queues plus Temporal for durable, async research pipelines that don't drop work.
04
Integrations with CourtListener and Google SERP so answers are grounded in real case law and current sources.

Stack

LangChainFastAPIPostgreSQLRedis/ArqTemporalGPT-4oClaudeGemini

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