Hire the lab.
The experiments are the proof: the same hands that build the toys design and ship production AI systems for companies. No slide decks, no "AI strategy" retainers — just working software.
Three things, done properly.
Agents & agentic workflows
Custom agents wired into your actual tools and data — multi-step workflows that classify, draft, route, and escalate, with a human in the loop where it matters.
Production AI deployment
Getting a demo to survive contact with real users: evals, observability, retries, and costs you can predict. The unglamorous part is the part that works.
AI products people enjoy
Interfaces that make the AI legible instead of spooky. The lab cares as much about how it feels to use as whether it runs.
Your SaaS, agentic. Without the rewrite.
Your users are already asking your product one question: “can I just tell it what I want?” The lab answers it by putting an agent layer on top of the SaaS you already run — speaking to the API you already expose, honouring the tokens you already issue. It lands in weeks, not quarters, because the platform is already written and public.
Agents are folders, not wiring
The convention is the architecture: an agent is a folder, a tool is a folder. Drop one in and it's discovered at startup — routable, testable, in the playground — with zero changes to core files. Your tenth agent costs the same as your first.
Tools are your existing API
An agent tool is a thin wrapper around a REST endpoint your product already exposes. No parallel business logic, no second source of truth — the agent operates your product the same way your frontend does.
Your auth stays in charge
Users log in with the tokens your product already issues; the layer validates and forwards them on every call. Your API keeps enforcing its own permissions — the agent adds convenience, never privilege.
Nothing destructive without a human
Free text becomes a typed plan your user approves. Approved work runs in parallel, pauses to ask when something's ambiguous, and resumes exactly where it stopped — checkpointed in Postgres, so it survives crashes.
The playbook is public
The starting line is Agentic SaaS Starter — the lab's MIT-licensed template with the router, plan approvals, parallel execution, evals, and dev console already working. Read it before you email; bring your API docs after.
Small first. Bigger if it earns it.
Email what's broken
A paragraph is plenty. What you're trying to automate, what you've tried, what annoyed you about it.
Get an honest take
A short call where the lab tells you what would actually work — including "you don't need AI for this," if that's the truth.
Start with a small build
A fixed-scope first project, shipped in weeks. You own the code. If it earns a bigger build, we keep going.
One engineer, no account managers.
Lanvar is run by Athavan Thambimuthu — a Montréal engineer who deploys AI systems in production for a living and builds the rest of this site at night because he can't stop. You talk to the person who writes the code.