AI engineering studio
Don't take the AI's word for it. Open the record it read.
1See how that is measured, and what it does not cover
Source: abstain precision 1.000 and zero cross-tenant leaks on one production system, measured at every release.
We build AI that answers from your own documents, shows the source for every line, and says it does not know rather than guess. Then we stay on and operate it. Underneath: grounded retrieval, an abstain path, and the evals that gate every release.
One production system, measured at every release
Where this comes from
One production system, on the eval battery run at every release. It does not measure how often the system should have refused and did not, so ask for the refusal rate beside it.
Where this comes from
Same system. A leak test only covers the tenant pairs someone wrote a test for, so the number is a floor, not a proof of impossibility.
Where this comes from
Same system, indexed and citable. Corpus size says nothing about answer quality on its own; it is here to show the approach holds at scale.
Where this comes from
Same system. The verifier can only check a claim against what retrieval returned, so a retrieval miss is still a miss.
These four come from one production system we build and operate, measured by its own eval gate on every release, not averaged across clients. Each number's limit is stated with it.
How it works
AI that looks it up instead of making it up
A standard AI, asked something it does not know, will invent an answer and say it with full confidence. Ours works the other way around.
- 01
It reads your sources first
Point it at your documents, records, and systems. It works from those, not from whatever a general model happens to remember.
- 02
It answers only from what it found
Every answer is pulled from a real record and shows its source, so you can check it. Ask something your data does not cover and it says so, instead of guessing.
- 03
It is checked before you see it
Each answer passes an automatic honesty gate. A claim that cannot be traced back to a source is blocked, not served.
Honest about the limits. Grounding and citations do not make mistakes impossible. They make them visible and traceable, instead of silent.
What an answer looks like
Two outputs. One of them is a refusal, and that is the point
Both of these are illustrations of the shape, not screenshots of a client system. The behaviour is what matters: an answer names the record behind it, and a question the data cannot support gets a no instead of a plausible number.
Q. What is the current occupancy across the Texas properties?
91.4 percent across 11 properties, as of the 31 July rent roll. Two properties are below 85 percent: one is mid-renovation on 62 units, the other lost an employer lease in June.
Rent roll, 31 July, 11 property records. Every figure links to the row it came from.
Q. What will occupancy be next quarter?
I do not have that. Nothing in the portfolio records is a forecast, and I will not derive one from occupancy history and present it as a fact.
No source. The system returns nothing rather than a number it cannot support.
The second one is the harder engineering problem and the one most systems skip. A model will always produce a fluent answer to the forecast question; making it decline takes an abstain path and an eval that catches it when the path fails.
What we do
Four ways to work with us
Each one is scoped before it starts, built inside your stack, and gated on evals before anything reaches a user. The engineering depth underneath is what makes them hold.
- 01AI automationTurn a slow, manual workflow into one that runs itself, with checks you can see.
- 02Forward-deployed AI engineeringWe embed with your team and ship inside your stack.
- 03AI agents and grounded RAGAnswers pulled from your own documents, each traceable to its source.
- 04LLM evals and reliabilityHonesty gates that block a wrong answer before it ships.
Real engineering beneath the AI
- The infrastructure underneath. We run the cloud your agents sit on, and fix it with diagnosis instead of bigger boxes.
- AI for smart-contract security. Multi-agent systems that audit smart contracts and surface real findings, where AI meets web3.
Integrations
Runs on your whole stack.
We build on the models and tools you already run, in your repositories, under your license. No rip-and-replace, and no platform only we can operate.
How an engagement runs- LLM
- Claude
- Anthropic
- AWS Bedrock
- OpenAI
- Voyage
- Cloud
- AWS
- Vercel
- Data
- PostgreSQL
- pgvector
- Framework
- Next.js
- Python
- FastAPI
Selected work
Real systems, real numbers
Case studies from delivered work, anonymized by default. Every figure traces back to a real record.
A $3.4B US real-estate investment firm
One honest answer box over a firm's entire portfolio
We built an internal intelligence platform for a real-estate firm managing about 3.4 billion dollars. One natural-language box answers questions about the portfolio and abstains rather than guess. Honesty is the product: every number is read from a real record.
Ilayer research
A multi-agent pipeline that finds real vulnerabilities
We built an autonomous, multi-agent system that audits smart contracts and surfaces real high and medium findings on live protocols. It runs several models against each other and filters every candidate hard before a human sees it, so the signal is high and the noise is gone.
How an engagement runs
Scoped, embedded, and on the hook
No open-ended retainers, no black box. We scope one piece, prove it on your data, then widen.
- 01
Scope one workflow
We pick the workflow with the most pain and the clearest edges, and write down what good output looks like. Fixed scope, known price.
- 02
Build on real data
We embed with your team and build inside your stack, against your systems. Working software weekly, not status decks.
- 03
Check it before it ships
Before anything reaches a user it runs through an automatic checklist and an honesty gate. A change that makes the system worse is reverted, not shipped.
- 04
Operate and widen
We stay on the hook for what runs: monitoring, incident response, steady improvement. Then we widen to the next piece.
Straight terms
What you can count on
No fabricated logos or testimonials. The trust is in the terms, stated plainly.
We tell you when AI is the wrong tool
If a workflow needs a guarantee AI cannot give, or the data to ground it does not exist, we say so before you spend a dollar. Fewer demos, more things that hold.
You know the price before we start
The build is fixed-scope, so the number is agreed up front, not metered by the hour. After launch, a flat monthly rate to operate and improve it. No open-ended retainers.
You own everything we build
It lives in your repositories, under your license, and your team can run it without us. No platform only we can operate, no lock-in.
Questions
What buyers ask first
What does Ilayer do?
We build and operate AI that answers from your own data, cites its sources, and holds back when it is not sure, embedded with your team. Under the hood that is production AI agents, grounded RAG, and the evals that keep them honest.
How long does a first project take?
A fixed-scope first piece runs 4 to 6 weeks, from kickoff to something working on your real data. We start with one workflow, prove it, then widen.
What does an engagement cost?
The build is fixed-scope, so the price is agreed before we start, not metered by the hour. After that, a flat monthly rate to operate and improve it. No open-ended retainers.
How do you stop AI from making things up?
Instead of answering from memory, it answers from your documents and records, shows the source, and says 'I don't have that' when the answer is not there. An honest 'not sure' beats a confident wrong answer. On one production system we hold abstain precision at 1.000 with zero cross-tenant leaks, measured by its own eval gate at every release.
Do we own what you build?
Yes. It is built in your repositories under your license, with a handover that leaves your team able to run it. No platform lock-in.
What makes forward-deployed different?
Our engineer works inside your team, on your systems, instead of delivering a spec from a distance, and stays on the hook through production.
Start a project
Name the workflow that eats your team's week
Name the workflow that eats your team's time. We will tell you honestly whether AI fits, and if it does, scope a first piece with a known price.