into labs [email protected]
AI engineering studio

into labs

We help operations, GTM, and non-engineering teams ship agents, tame AI slop, and cut runaway token costs.

Where are you

Three places teams
get stuck.

01 behind

Your team chats with AI. It doesn't build with it.

Everyone has a ChatGPT tab open. Nobody is building, scaling, or rethinking work around it. We embed, pick the three workflows worth automating, and put real agents in production inside a quarter.

  • Workflow audit and opportunity map
  • Reference workflow your team can extend
  • First agent shipped, owned by your team
02 chaotic

You're shipping AI, but it's slop.

Outputs drift between runs. Nobody trusts a result without a person checking it. Half a dozen GPTs and prompts live in Slack DMs and Notion docs. We bring evals, structure, and the muscle to say no to bad outputs.

  • Evals built on real ground-truth data
  • Cleanup of prompts and tools sprawled across the team
  • Quality gates everyone can trust
most common
03 expensive

Quality is fine. The bill is not.

You proved it works. Now the bill is climbing faster than usage and finance is asking questions. We measure the cost of every task, swap expensive calls for smaller models where it won't hurt quality, and cut spend without cutting output.

  • Cost per task, per use case
  • Right model for each call
  • Caching and routing where they pay off
The pilot

Give us one person
who wants to build.

Introduce us to someone on your team who's been curious about this. We build something real with them — they own it, run it, and teach the rest of the team. No transformation required. One project, one quarter, internal capability that stays.

apply for the pilot
What this looks like now accepting
  1. You intro us to one curious person on your team
  2. We find one workflow worth automating
  3. We build it together — they're in every session
  4. They own it, run it, and teach the team
timeline
one quarter
commitment
one person, part-time
what you keep
the agent + the knowledge
Case study

3 agents replaced
a team of 10 PhDs.

An innovation-scouting team relied on ten PhDs to surface technical partners, screen them, and brief executives. We rebuilt the workflow as three composable agents, owned by one engineer.

role
build partner
duration
one quarter
stack
Claude, Python, evals
asset
compounding, owned in-house
Outcome
10 3

Ten PhD scouts compressed into three composable agents. $122k per year in savings, scouting volume 2x, north-star metric 3x.

Get in touch

Tell us where
you're stuck.

One email. We reply within a day with a read on which of the three you're in and what a first move looks like.

[email protected]