AI build
Production‑grade AI systems, built inside your architecture
We build AI systems that hold up in production, on the stack you already run, with the observability to prove they work.
Roughly right is fine for a demo. When a whole business depends on AI, it has to be right.
Tell us what business challenge you’re interested in solving.
Since 2009
Working with Fortune 500 companies like Walmart, Johnson & Johnson, Coca-Cola and Mastercard
30%
of the Fortune 20 partner with BOI
ISO 27001
Certified to ISO/IEC 27001:2022 since April 2026
What we build on
Anthropic
OpenAI
Mistral
AWS
Azure
Google Cloud
Docker
Terraform
Ruff
uv
Langfuse
Sentry
Claude Code
MCP
Orca
GitHub
Linear
OpenWiki
Anthropic
OpenAI
Mistral
AWS
Azure
Google Cloud
Docker
Terraform
Ruff
uv
Langfuse
Sentry
Claude Code
MCP
Orca
GitHub
Linear
OpenWiki
The problem
Where AI delivery stalls
We see the same three patterns across the industry, and none of them are about the AI model.
Testing
Nobody defined what good looks like
AI always produces an answer. That doesn’t make it a good one. Without an agreed definition of good, nobody can tell the difference and accuracy never gets measured.
Review
Review becomes the bottleneck
Agents write code faster than people can review it. Without a new way to review, quality drops as speed goes up.
Sustainability
Every AI project starts from scratch
Each team builds its own retrieval, evaluation and guardrails. A year later you’re maintaining several half-finished versions of the same plumbing, and the cost of running them keeps growing.
How it works
Three steps from first call to production
Each step ends with something you can see and test.
Step 01
Agree what good looks like
We agree the success criteria with you before we build, tested on real questions with known answers.
Step 02
Get a working version in weeks
Engineers and agents build in parallel, so the integrations that usually get cut for budget make it in. You react to something real early.
Step 03
Move to production with the proof
Every change goes through automated tests and thorough review before it reaches production. The agent rolls out once it meets the success criteria.
How we build
Enterprise engineering, accelerated by agents
Our engineers design the solution, build it and direct their agents.
01
Work runs in parallel
Every engineer ships backend, frontend and infrastructure, so work never waits on whoever owns one part of the system.
02
A working version first, then production
You get a working version within weeks, then we take it to production with testing, security and operations built in.
03
Engineers explore, agents do the heavy lifting
With agents, code is cheap. They write it, spin up variations, and find and fix issues on their own. Our engineers use that to test more ideas, validate faster and iterate until it’s right.
The harness
Everyone gets the same models, so the engineering around them is now the competitive advantage
Every agent runs inside a harness: the rules it follows, the tools it can use and the checks it has to pass.
01
Reliability
Agents hunt for issues in their own code before anyone asks. Automated tests and a second AI model review every change critically, so only high-quality code reaches production.
02
Security
Safeguards stop risky actions before they happen. Agents run in isolated environments, passwords and keys stay hidden, and nothing connects to the outside without approval.
03
Integration
What we build fits into your stack. Embed it directly in your systems, or connect it through MCP to the agents of your choice.
04
Standards
Our engineering standards are written down and ship with every codebase, so you can audit exactly how agents are told to behave.
05
Automation
Any task done twice becomes reusable. That covers agent skills and the code that builds the code, such as CI/CD pipelines and DevSecOps checks.
Quality
An agent ships when it is proven, against a bar you set
We build the benchmark with your team and test every agent against it before it ships.
Day 1
success criteria agreed with you before we build
Your experts help write the test cases and agree the success criteria at the start. They keep adding cases as the system grows. An agent rolls out once it meets them, and not before.
In practice
Seven things you can check yourself
Each practice comes with a test your team can run, without taking our word for it.
01
Tests built on your data Every benchmark comes from your own data and your definition of good, never a generic test set. It covers expected behavior, edge cases and known failures.
■ Test it
Ask which of your own cases are in the benchmark.
02
Clear right-or-wrong scoring Each test case is scored right or wrong. We review real usage, count what went wrong and fix the biggest problems first.
■ Test it
Ask for last month’s score, case by case.
03
Better with every week in use How people use the system feeds the next round. Every correction and production failure becomes a new test case, so quality compounds.
■ Test it
Ask how many test cases were added since launch.
04
Quality, speed and cost measured together We estimate the running cost before we commit, then track cost per task in production. A gain in accuracy that triples the cost is no gain.
■ Test it
Ask what a single answer costs today.
05
Model upgrades backed by proof We pin model versions. A newer or cheaper model runs side by side against your benchmark and only takes over once it scores as well.
■ Test it
Ask which model version is running, and why.
06
Adoption tracked from day one Usage dashboards run from launch, with our own team filtered out. Your admins see the same numbers we do.
■ Test it
Open the usage dashboard yourself.
07
A measured error rate No AI model is error-free. You get a measured error rate, the records behind it, and a threshold below which the agent hands the question to a person.
■ Test it
Ask how often it was wrong last week, and what happened next.
Ownership
What you keep when we leave
You own everything we build, including the tests, so your team can keep checking that the system works.
01 · The code
In your own organization from day one, with intellectual property assigned to you immediately.
02 · The tests
The test suite, its data and the scoring rules, so you can hold future suppliers to the same standard.
03 · Agent instructions
Stored with the code and tracked like any other change.
04 · Runbooks and dashboards
Decision records, runbooks and dashboards, with access handed over to a named owner on your team.
How we handle your data and your stack
Agents get only the data and access they need, with temporary credentials that cannot reach your production systems.
They run in isolated environments with spending limits, and nothing connects to the outside without approval. We deploy into your own cloud under zero-retention terms, and your data never trains shared models. Your team can cap spending or switch an agent off at any time.
Standard
ISO/IEC 27001:2022
Certificate
202604-239
Valid
April 2026 to April 2029
Major non-conformities
0
We align with GDPR, CCPA and HIPAA where they apply.
What we build on
We don’t receive partner incentives for any of our work, so model choice stays your decision.
Frontier models
Anthropic
OpenAI
Mistral
We use advanced reasoning models where mistakes get expensive, and more efficient models when cost is the main driver.
Cloud and deployment
AWS
Azure
Google Cloud
We work cloud-agnostic and deploy directly into your own cloud account and region, under your existing identity and network controls.
Built on solid foundations
Docker
Terraform
Ruff
uv
Every build is containerized, its infrastructure defined as code, and quality checks enforced on every commit, so your team can rebuild and run it without us.
Evaluation and observability
Langfuse
Sentry
CloudWatch
Azure Monitor
Cloud Logging
Every agent run is monitored and scored, so no mistake goes unnoticed and each iteration starts from a clear signal.
Coding agents
Claude Code
Codex
MCP
Orca
We engineer with the best coding agents available. Our engineers direct them to write and review code in parallel.
Supporting tools
GitHub
Linear
OpenWiki
Every change, review and task is tracked in the open, and the documentation updates itself as the code changes.
What changes when BOI builds your AI
AI systems built and run to enterprise standards.
A typical AI build
With BOI
Testing
A demo walkthrough looks fine, so it ships. Then real use hits the edge cases
Tested on a fixed benchmark, edge cases included, against success criteria agreed up front
Review
If it works on a developer’s laptop, it goes live
People and AI review every change before it goes live. AI digs into security and edge cases, engineers check it fits the business
Speed
Weeks of scoping and a formal quote before anything starts
We start right after the first call. A rough brief is enough to begin
Ownership
A platform you rent, code you don’t own
Everything we build is yours from day one
Model choice
Tied to a partner’s vendor incentives
Your decision. We don’t receive partner incentives
Case study
Running today at a global industrial manufacturer
Our client is a global manufacturer that builds and services industrial equipment. Every day, its field technicians and sales teams use the AI systems we built with them.
2×
More accurate answers from a troubleshooting agent, after we rebuilt how it finds information. Same AI model.
45,000 support emails
Processed every night. Their data is extracted and structured together with machine codes, serial numbers and historical cases. The next morning, technicians have fresh knowledge and can troubleshoot immediately.
8 countries
Sales teams in eight countries use an aftermarket quoting tool connected to the client’s CRM and ERP.
“BOI has forced us to think differently about how we deploy technology solutions across the company.”
CFO, industrial manufacturing client
FAQ
Questions worth asking any build partner
How do you know it works?
Every agent is tested against a benchmark, with success criteria agreed up front
What do you keep?
Where does it run?
Which AI models do you use?
Contact
Got a project in hand? Let’s talk.
Tell us the business challenge you want to solve. An engineer joins the first call.
We reply within 48 hours.
Amir Ouki
Partner, Americas
Stefano Orani
Managing Director, EMEA
