We architect, build and operate multi-agent systems that turn human bottlenecks into 24/7 automation. Real production. Real paying clients. Real results.
From concept to production. We design agents using Pydantic AI, LangGraph, and the Cole Medin AI Agent Mastery methodology — built for reliability, not demos.
Supervisor patterns, tool-calling orchestration, long-term memory pipelines, and deterministic guardrails. We build agents that scale to thousands of conversations.
pgvector-backed retrieval over domain-specific corpora. Extract → Dedup → Insert memory pipelines. Real-time conversational context that remembers users.
step 01
Map the bottleneck and define success metrics. Without a measurable target, no AI investment is defensible.
step 02
Design the agent topology and data flow. Tools, sub-agents, memory boundaries, and deterministic guardrails specified up front.
step 03
Implement, test, instrument with Langfuse. Every prompt, tool call, and retrieval is observable from day one.
step 04
Deploy, monitor, iterate based on real usage. Production data is the only honest feedback loop.
NITA delivers personalized clinical nutrition through WhatsApp at scale. A multi-agent system orchestrated by a main agent that routes intent across 18 specialized tools — RAG over a 597-food nutritional database, three sub-agents for plan generation, multimodal photo analysis, and recipe suggestions, plus long-term memory backed by pgvector.
Currently in production
Personalized clinical nutrition
End-to-end multi-agent runs
Stored in pgvector
We work project-based on production-grade AI agents. Tell us about the workflow you want to automate and we'll come back with an architecture proposal.