An agentic AI consultancy where enterprise systems depth meets modern AI.
Tech Arch Inc is an agentic AI consultancy built on one conviction: agentic AI is a systems problem. Getting a demo working is easy; making multi-agent systems reliable, observable, and affordable at scale is where most teams struggle — and that's exactly the discipline that years of high-scale distributed-systems engineering brings.
We pair fluency in modern agentic frameworks (LangGraph, MCP, NVIDIA NeMo, RAG, evals) with the GPU/memory economics that decide whether these systems are viable in production — and the reliability engineering that keeps them dependable once they're live.
A single prompt that works in a notebook is not a product. Production agents loop, call tools, retrieve context, and chain across models — and every one of those steps can fail, stall, drift, or quietly run up your token and GPU bill. The hard part isn't the model; it's everything around it.
That's familiar territory. Idempotency, retries and back-pressure, observability and tracing, evaluation harnesses, and capacity planning are the same primitives that keep high-scale distributed platforms standing — now applied to agents instead of microservices.
A focused assessment of your agent design, failure modes, and evaluation strategy — with a prioritized plan you can act on.
We profile where memory and money go, then hand you a quantified plan — quantization, batching, KV-cache and instance right-sizing.
End-to-end development of reliable, observable agentic systems — wired into the enterprise platforms you already run on.
Evals before features. If you can't measure it, you can't ship it reliably.
Reliability over demos. Retries, idempotency, and observability from day one.
Cost-aware by default. GPU/memory economics are a first-class design constraint.
Honest about scope. We show what actually runs — and say so when something isn't ready.