AI/ML consultant who ships production systems, not slide decks
An AI/ML consultant and machine learning consultant who builds, not just advises. I’ve put AI systems into production across real-time voice pipelines, RAG knowledge systems, computer vision and forecasting, taking products from "we should use AI" to a deployed system with latency budgets, cost ceilings and monitoring.
Product teams that want AI features shipped into an existing app, with real latency and cost budgets
Founders validating an AI product who need a working prototype in weeks, not a research program
Companies whose first AI attempt stalled, a demo that never survived contact with production
What you get
LLM & chatbot integration
GPT-4/5, Gemini, Claude, and open-source LLaMA, integrated with your data, guardrails, evals, and cost controls. Intent classification in production at ~88% precision (QuickComm).
RAG & knowledge systems
Retrieval-augmented generation over your documents: hybrid BM25 + vector retrieval, reranking, and citation-aware answers, the architecture behind PaperIntel, my research-intelligence system.
Voice AI & real-time audio
Full-duplex voice agents: streaming speech-to-text (Whisper, Deepgram), LLM reasoning, and natural TTS (ElevenLabs) at under 800ms voice-to-voice latency. Deployed in hotels replacing walkie-talkies.
AI agents & automation
Tool-calling agents that act (fetch data, file tickets, route requests), with the reliability engineering (retries, fallbacks, human handoff) that agents need to be trusted.
Custom ML & fine-tuning
When prompting isn’t enough: model fine-tuning, classical ML (forecasting, classification), and evaluation pipelines that prove the gain before you pay for it.
How it works
01Audit
A short discovery pass over your product, data, and goals. Output: what AI can actually do here, what it will cost to run, and what to build first.
02Prototype
A working end-to-end prototype on your real data in about two weeks, not mockups. This is where assumptions die cheaply.
03Production
Hardening: latency budgets, cost ceilings, evals, monitoring, fallbacks, and deployment into your infrastructure.
04Handoff
Documentation, runbooks, and training for your team, or an ongoing retainer if you’d rather I keep operating it.
Hotels needed radio chatter turned into structured, routed, actionable requests, live, on noisy audio, at a cost that works per-property.
Built
Streaming PCM audio ingestion, Whisper/Deepgram transcription, Gemini LLM intent classification, and instant team routing over WebSockets, deployed as AWS microservices.
Finds where AI genuinely pays off in your product, then designs and ships it: picking models, building the data and retrieval pipeline, setting latency and cost budgets, and putting evals and monitoring in place. Unlike a pure advisor, I also write the production code.
What does a machine learning consultant do, and when should a startup hire one?
A machine learning consultant decides whether ML is the right tool, picks the approach (an off-the-shelf model, RAG, fine-tuning or a custom model), builds the first working version on your data and sets up the monitoring that keeps it accurate. Startups should hire an AI consultant when there is a specific outcome and a deadline but no ML expertise in house: a voice agent, a document assistant, a vision model on live cameras. Hire full-time once AI is the core of the product and needs daily iteration.
AI consultant vs AI engineer: which do I need?
A consultant is right for a defined outcome on a deadline: an audit, a prototype, a production launch. A full-time AI or ML engineer is right once AI is core and continuous work. I compare all the roles in AI consultant vs AI engineer vs ML engineer.
How much does an AI integration cost?
The honest answer: it depends on scope, and anyone quoting a flat number before understanding your data is guessing. As a starting point, a fixed-price prototype starts at $600 and a production build at $2,000; hourly work is $25 an hour. I confirm a fixed quote after a free call, so your risk is capped. Running costs are engineered in from day one: one of my production systems runs at roughly $3/month per customer site.
Should we build or buy?
Buy when an off-the-shelf tool solves 90% of the problem. Build when AI touches your core product, your data is proprietary, or per-seat pricing would eat your margin. I’ll tell you which in the audit. Sometimes the deliverable is "don’t hire me, use X".
How long does an AI project take?
A working prototype on your data takes about two weeks. Production hardening (evals, monitoring, cost controls, deployment) typically takes another four to eight weeks depending on integration depth.
RAG or fine-tuning: which do we need?
RAG when the knowledge changes often or must be cited; fine-tuning when you need consistent style, structure, or a smaller/cheaper model. Most "ChatGPT for our data" requests are RAG. I’ve shipped both and will benchmark on your actual queries before recommending either.
Can you build voice agents?
Yes. It’s one of my deepest specialities. I’ve shipped full-duplex voice AI at under 800ms voice-to-voice latency and a production system transcribing noisy radio audio at 94%+ accuracy in live hotels.
Where are you based, and do you work with companies in the US, UK, or UAE?
I’m an AI consultant based in Islamabad, Pakistan, working remotely with clients in the US, UK, UAE, and Australia, including as CTO of a Dubai-based product. UTC+5 gives natural overlap with EU and Gulf hours and evening overlap with US East Coast; every engagement so far has been delivered remotely.