Build
Senior engineers who ship agentic systems.
Production in weeks. Not promises in quarters.
Scoped build or ongoing engineering partner — we design, build, and harden AI systems inside your real environment: your data, your permissions, your compliance constraints. Working software early, production-grade always.
What we build
From document piles to working agents.
Internal AI systems
Tools that change how your team works.
- Document classification & extraction at scale
- Conversational access to operational data (MCP)
- Workflow automation agents
- Reporting & analysis copilots
Product AI features
Agentic capability inside your software.
- RAG & retrieval systems
- Conversational interfaces
- Generative features with guardrails
- Usage-based cost architecture
Data & agent infrastructure
The plumbing that makes it dependable.
- Pipelines & ingestion (documents, ERP, field data)
- MCP servers over enterprise systems
- Evaluation harnesses & regression suites
- Observability, logging, and audit trails
Engagement models
Two ways to work with us.
Fixed deliverable
Scoped Build
Best for
- A specific system or feature
- Document intelligence & automation builds
- A first AI project with contained risk
Ongoing retainer
Embedded Partner
Best for
- Sustained AI roadmap execution
- Product teams adding AI capability
- Operators compounding workflow automation
Fixed quotes on the intro call — no discovery-phase toll booth.
How we're different
Why our builds make it to production.
AI-native
We build with agents, not just for them — our own delivery pipeline is an agentic system.
Engineers multiplied by agents
Small senior teams that move like big ones, because agents do the repetitive work.
Operating context built in
Every line of code carries the operational context from the survey — we design for your real environment.
Proof
Shipped and running.
The engineers on your build have shipped systems over 5,000+ documents and 15+ enterprise systems.
See all work →FAQ
Questions operators ask.
What kinds of AI systems do you build?
Internal AI systems — document classification and extraction, conversational access to operational data over MCP, workflow automation agents, reporting copilots — product AI features such as RAG and conversational interfaces with guardrails, and the data and agent infrastructure underneath them.
How long until something is in production?
Weeks, not quarters. We ship working software early and harden it inside your real environment — your data, your permissions, your compliance constraints — instead of building a demo first.
What is an MCP integration?
Model Context Protocol servers give an AI system governed, auditable access to a platform such as an ERP, scheduling, cost, or document system, so operators can ask questions in plain English and get answers that respect existing permissions.
How do engagements work?
Two models: a scoped build with a fixed deliverable and a fixed quote, or an embedded-partner retainer for ongoing AI roadmap execution. Quotes are given on the intro call — there is no paid discovery phase.
Which models and stack do you use?
We are model-agnostic: Anthropic Claude, OpenAI, Gemini, Llama, and Mistral. We build on MCP, LangGraph, the Claude Agent SDK, and the Vercel AI SDK, with PostgreSQL, Snowflake, and vector stores, on AWS, Azure, or GCP as your environment requires.
Who owns what you build?
You do. Everything we build runs in your environment and stays with you.
One firm, the full lifecycle
Keep going.
Let's build the thing.
Bring the workflow that's eating your team. We'll tell you in 30 minutes whether it's automatable — and what it would take.