Tailor open-source tools.
An open-source AI tool already exists for most work. Capable in a demo, short of the auth, data, policies, and scale a real business needs. The tailored deployment closes that gap.
The repo solves the work. The tailoring closes the gap to production.
The open-source AI ecosystem has filled out. Hundreds of self-hostable tools for specific jobs — chat, retrieval, agents, document processing, transcription, internal search.
Each one is demo-ready in its README. Production-ready inside a real business is a different distance.
The work worth tailoring. Three conditions identify it.
Build is expensive. Tailoring is cheaper. Most AI work belongs on top of something open-source that already does the heavy lifting.
An existing tool covers the core.
The capability is solved by something already public. The work that remains is the gap to your business.
The specifics are yours.
Your data sources, your auth, your policies, your brand — none of which the repo knows.
Production, not demo.
Logging, monitoring, backups, scale-testing — the parts a README skips.
The README ships a demo. Production needs more.
The fork covers the capability. The deployment adds the layers a business actually runs on.
Data connectors
Your data sources in place of generic loaders.
Authentication
Your single sign-on, your identity provider, your roles.
Branding
Your mark inside the UI. Your product, not the repo’s.
Policy layer
Guardrails for PII, role-based access, audit logs, retention.
Model choice
Frontier model where the work earns the cost. Open-weight model on your servers where it doesn’t.
Production hardening
Logging, monitoring, backups, scale-testing.
Two ways to run AI inside a business. One keeps your data home.
SaaS AI is convenient. Self-hosted, tailored open-source is governed by the policies you already wrote, on the infrastructure you already run.
Built around the tool and the work it has to fit.
Short cycles. Weekly demos. A tailored deployment in your environment in weeks, not months.
Evaluate
We map the work and the open-source tools that fit. We test the most promising ones against your data.
Tailor
We fork the repo. Wire it to your auth, your data sources, your model. Apply your policies.
Demo
You see the tailored tool running on your data, inside your perimeter. We walk through what works and what to refine.
Ship
Deployed in your environment. We hand over the fork, the docs, the runbook for upstream merges.
Connected to what you already run.
The tailored tool ties to your existing stack — your identity provider, your data sources, your storage, your monitoring.
Anything with an API connects directly. The rest connects through your existing service mesh.
The open-source AI ecosystem we deploy from.
Mature tools where they fit. Each one tested in real deployments. The right answer is whichever one already does the work.
You own
everything.
A fork in your repository. A deployment on your infrastructure. Docs written for your engineers. Upstream updates pull in cleanly by your team or by us, your call.
Let's talk about the work.
Book a call with the founders. We listen first. Then we come back with what we'd build.
Tell us what you're working on.
A short form. Three minutes. We come back with what we'd build and whether we're the right people to build it.