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
- You intro us to one curious person on your team
- We find one workflow worth automating
- We build it together — they're in every session
- 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.
into/labs/ into/labs/ into/labs/ into/labs/ into/labs/ into/labs/ into/labs/ into/labs/ into/labs/ into/labs/ into/labs/ into/labs/
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] →