The Micap Lab

Where we build
before we deploy.

The Lab is our R&D layer — where agent architectures get designed, stress-tested, and hardened on real work. Everything we ship to clients earns its place here first.

Research focus

What we work on.

Practical work on the parts of agentic AI that actually matter when you put it into production.

/ orchestration

Agent coordination

Multi-agent routing and sandboxed execution patterns that stay reliable as a network scales up or down.

/ memory

Private retrieval

Knowledge-graph and vector approaches that let agents reason over your documents — without that data ever leaving your hardware.

/ safety

Guardrails & control

Permissioning and failure-mode design, so autonomous systems do what they're told and nothing more.

/ local-first

On-prem inference

Getting strong performance from local and low-cost model setups, so privacy never means a worse experience.

/ integration

Workflow wiring

Connecting agents into the tools teams already use — intake, scheduling, documents, comms — without brittle glue code.

/ economics

Cost & attribution

Measuring what automation actually saves, so deployments are scoped on ROI rather than hype.

Selected work

Built, run, and battle-tested.

In production

Autonomous agent network

A self-operating fleet handling research, ops, and execution — the same kind of architecture clients receive, refined daily on real work.

multi-model · local
Tooling

AI workforce templates

Reusable agent workflows — reception, intake/SDR, and back-office automations — packaged for fast, reliable deployment.

n8n · agents
Platform

Domain intelligence pipeline

An extraction and mapping system that surfaces opportunities from public data months ahead of the market.

FastAPI · PostGIS · LLM
Privacy-first

On-device ML systems

Full pipelines that run entirely on a user's device — proving local-first AI can match cloud experiences without the data risk.

on-device · ML

Note: project details are generalized to protect client confidentiality. Want specifics relevant to your use case? We'll walk through comparable work on the discovery call.

Research notes

Things we're thinking about.

Working notes from the Lab — what we're learning as we build and deploy. Practical, not academic.

Note: we publish notes as we have something useful to say — no fixed schedule. Want the thinking behind any of these applied to your business? That's what the discovery call is for.

The stack

Tools we build with.

We're model- and tool-agnostic by design — routing to whatever delivers the best result for your budget and privacy needs.

Anthropic ClaudeGoogle GeminiGroq Ollama · localOpenClawFastAPI PythonPostGISVector stores n8nDockerKnowledge graphs

Want this
on your side?

Book a free 15-minute call and we'll scope a private agent network tailored to your team — built on everything proven in the Lab.

Book your call →