From AI idea to a workflow that ships — and earns its place in your stack
Most AI projects die in the demo. I take one high-value workflow and build it for real — production architecture, evals, guardrails and deployment — so it actually runs, measurably, in front of your users. This is the same work I do leading AI engineering for a multi-agent travel platform.
This is for you if…
- You have a concrete AI use case and want it shipped, not prototyped forever
- A previous AI proof-of-concept stalled before it reached production
- You need LLM, RAG, agentic or voice-AI work done by someone who's shipped it
- You want the architecture done right the first time, with evals and guardrails
The gap isn't the model — it's everything around it
Calling an LLM is easy. Making it reliable, evaluated, cost-controlled and safe in production is the hard 90%: retrieval that's actually relevant, guardrails that hold, latency and cost that pencil out, and evals that tell you it's working. That's what I build.
Everything in the engagement
Use-case scoping & feasibility
We pick the one workflow with the clearest ROI and a realistic path to production.
Production architecture
The full system — retrieval, orchestration, memory, guardrails — designed to scale and observe.
LLM / agent / RAG build
The actual implementation: multi-agent graphs, hybrid retrieval, tool use, voice where it fits.
Evaluation harness
Offline + online evals so you can prove quality and catch regressions, not guess.
Cost & latency tuning
Model routing, caching and prompt design that keep it fast and affordable at scale.
Deployment & handoff
Shipped to production with monitoring, plus docs so your team can own it.
The process
Scope
Lock the use case, success metric and architecture — a 2-3 day discovery, not a months-long workshop.
Build
I build the system end-to-end — retrieval, agents, guardrails, evals — usually in 1-3 weeks with AI-assisted delivery.
Evaluate
A few days measuring quality, cost and latency against the metric, tuning until it clears the bar.
Ship
Deploy to production with monitoring and a clean handoff to your team.
Outcomes
- One AI workflow live in production, with numbers behind it
- An evaluation harness that keeps quality honest over time
- Cost and latency that work at your scale
- A reusable architecture your team can extend to the next use case
“Moeid has been a savior in our time of need. His technical expertise and leadership transformed our platform architecture.”
Harvey BennetCTO, SearchieCommon questions
What kinds of AI systems do you build?
Production LLM apps, multi-agent systems, RAG and hybrid-retrieval pipelines, voice AI, and the cloud infrastructure to run them — the same stack I lead in production today.
How fast can it ship?
Faster than you'd expect. With Claude, Codex and modern AI tooling, a focused workflow goes from scope to production in weeks, not the months legacy estimates assume. The real bottlenecks are your data, integrations and decisions — not engineering hours — and I'll be straight about those up front.
Can you rescue a stalled AI proof-of-concept?
Often, yes. A lot of POCs stall on architecture, retrieval quality or eval gaps — exactly the parts I specialize in closing.
Do you use my data safely?
Yes — data handling, privacy and guardrails are part of the architecture, not an afterthought. I've run ISO/DPIA compliance for enterprise clients.
Will my team be able to maintain it?
That's the point. You get clean architecture, monitoring and documentation, plus a handoff so your engineers can own and extend it.
Let's figure out if this is the right fit.
A free 30-minute strategy call — no pitch, just a straight read on your situation.
Book your strategy call →