From multi-agent orchestration to production inference pipelines — I design, build, and ship agentic AI that survives contact with real users and real data.
Four technologies. Zero abstraction layers between me and the metal. I architect at the level where frameworks end and real systems begin.
I design stateful agent graphs that coordinate, branch, and recover — not chains that pray. LangGraph gives me explicit control over agent topology, memory persistence, and human-in-the-loop checkpoints. Every node has a purpose. Every edge has a condition.
Prompts are not engineering. They are configuration. DSPy lets me define signatures, compile optimized pipelines, and swap models without rewriting a single prompt. The result: systems that improve themselves through metric-driven optimization, not manual tuning.
The Model Context Protocol is the USB-C of AI tooling. I build MCP servers that give any LLM structured, authenticated access to enterprise data — databases, APIs, internal tools. One protocol. Every model. Zero custom integrations per tool.
Self-hosted models need self-hosted performance. vLLM gives me PagedAttention, continuous batching, and tensor parallelism — the infrastructure layer that turns a GPU cluster into a production-grade inference engine. I deploy models that serve thousands of concurrent requests without breaking a sweat.
A lead agent you can deploy this month, and the multi-agent systems on my roadmap.
Qualifies inbound enterprise leads, scores budget intent, and drafts a contextual reply in 2.2 seconds on the live demo — deployed to your own private infrastructure.
* Industry research, not a CAT-01 result: Harvard Business Review (2011) audited 2,241 US companies and found those that replied to an online lead within an hour were about 7x more likely to qualify it than those that replied later.
Planned multi-agent BI system: 4 coordinated AI agents that will turn a plain-English question into a checked, sourced answer. Not built yet (October 2026).
Planned AI observability platform that will watch agent quality, catch drift and alert before users notice. Not built yet (October 2026).
Planned MCP gateway that will give any AI model secure, logged access to company tools through one standard. Not built yet (October 2026).
Building an AI system that needs to actually work?
Let's Architect It TogetherEvery engagement follows the same disciplined arc. No surprises. No scope drift. Just systematic execution.
I map the problem space before I write a single line of code. What does success look like? Where does the current system break? What are the constraints — latency, cost, compliance? This phase produces a clear architecture brief that both of us sign off on.
I design the system on paper first. Agent topology, data flow, model selection, infrastructure requirements, failure modes. You get a technical blueprint and a plain-English summary. No black boxes. Every decision is justified.
Iterative two-week sprints with working demos at each checkpoint. I build with production-grade tooling from day one — not notebooks that need to be rewritten later. You see real progress, not slideware.
Deployment is not the finish line. I instrument everything — traces, metrics, evaluations. The system ships with monitoring built in, so when something drifts, you know before your users do. Then I optimize based on real production data.
I'm Moin Dalal. I architect agentic AI systems for enterprises that need more than a chatbot wrapper around GPT.
My background is unusual for this space: an MBA in Analytics & Strategy, which means I don't just build technically sound systems — I build systems that solve actual business problems. I understand unit economics, stakeholder alignment, and why most AI projects die in the pilot phase.
For the past 12 months, I've gone deep into the agentic AI stack — LangGraph for multi-agent orchestration, DSPy for programmatic prompt architecture, MCP for universal tool integration, and vLLM for production inference. Not surface-level familiarity. Production-grade depth.
What I've built so far: CAT-01, an instant lead agent with a live private demo, and VoiceCall, a self-hosted voice broadcast platform proven on real calls. Next on my roadmap (not built yet, October 2026): Athena (multi-agent BI), Sentinel (AI observability) and Nexus (MCP gateway).
I work as a solo architect — which means you get one senior person who owns the entire system, not a team of juniors supervised by someone who doesn't write code. Architecture, implementation, deployment, monitoring. One throat to choke.
Agentic AI Developer & Architect
Core Stack
Four models, from a two-week lead engine deployment to an ongoing retainer.
We take the battle-tested CAT-01 architecture and custom-build it for your exact sales pipeline. Includes intake from web forms and WhatsApp, custom BANT evaluation models, deterministic brand and pricing safety guardrails, CRM sync (Twenty today; other CRMs on request), and instant team alerts on Telegram. You receive the complete workflow source code with zero perpetual SaaS seat fees.
A focused engagement to design your AI system architecture. You get a complete technical blueprint — agent topology, model selection, data pipeline design, infrastructure plan, and cost projections. Ideal when you have a clear problem but need an expert to architect the solution before your team builds it.
End-to-end: I architect it, I build it, I ship it. From initial discovery through production deployment and monitoring setup. This is the model for organizations that need a working system, not a document. Includes iterative two-week sprints with working demos at every checkpoint.
Ongoing architectural oversight for teams building AI systems. Weekly 1-on-1s, async architecture reviews, and on-call access for critical decisions. I become your fractional AI architect — embedded enough to add value, independent enough to stay objective.
Ready to scope your project?
Start a ConversationTell me about your system. I'll tell you if I can help — and if I can't, I'll point you to someone who can.
Review within 24 hours
I personally read every message. No assistants, no auto-replies.
30-minute scoping call
We map your system requirements, constraints, and success criteria.
Tailored proposal in 48 hours
Architecture approach, timeline, investment — everything you need to decide.
Typically responds within 4 hours
US & Global remote engagements