Production AI coaching for engineering teams
Build AI your
team can own.
Stop spending months on AI that only works in demos.
Practical coaching for the engineers you already have.
Build the capability inside your team
Teach your engineers
how to make AI work.
You don’t need to start by hiring another AI team. Teach your existing engineers to find failures, test improvements and build on what works.
A practical workshop, shaped around your project and your engineers’ experience.
Make the work add up
Ship sooner.
Build more. Rebuild less.
Use a repeatable engineering method to shorten trial and error. Carry the useful parts into your next project.
Guess. Retry. Repeat.DiagnoseTestRelease
Less time to release.
Find the failing step before spending another week changing prompts.
One AI Core
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More projects from one core.
Reuse evaluations, model access and monitoring across use cases.
Stop paying for the same setup.
SetupSetupSetup
↓
Shared foundationNew work
Less money lost to rework.
Put engineering time into the next problem, instead of rebuilding the basics.
See what your team will learn
Same AI problem?
Find a better way through.
Explore an example: what breaks, what to change and how to check the improvement.
RAG / search example
The answer sounds right. The source is wrong.
Company knowledge, support search and internal assistants.
The failure“You have 30 days to return it.”
An expired page outranks the current policy. The model confidently repeats it.
The target output“The current return window is 14 days.”
Include the current policy citation. Ask for missing context when eligibility is unclear.
↳What to improve
Version the sources. Filter expired policies. Keep rules and exceptions in the same chunk.
Check the resultCurrent evidence found · supported answers · correct abstentions
What your engineers practise
Build the dataset
Tag questions by intent, source version and answerability. Include conflicting policies, duplicate pages and questions with no answer.
Find the failure
Inspect retrieval before rewriting the prompt. Compare chunk boundaries, keyword + semantic search and reranking.
Test the improvement
Score evidence retrieval separately from answer correctness. Check citations and have reviewers validate the scoring rules.
Email replies example
A polished reply misses half the question.
Support drafts, account requests and operations inboxes.
The failure“Your order arrives tomorrow. I’ve updated the address.”
The reply invents a delivery date and claims an action that never happened.
The target output“Please share your order number so we can check delivery and whether an address change is still possible.”
Answer both requests. Escalate exceptions. Claim an action only after it succeeds.
↳What to improve
Split the request into intents. Check order status and policy. Confirm actions through tools.
Check the resultIntent coverage · unsupported claims · editing time
What your engineers practise
Build the dataset
Tag conversations by every intent, required context and permitted action. Include multi-question threads, missing details and exceptions.
Find the failure
Trace intent detection, retrieved context, tool results and the final reply. Check whether the system had enough information to answer.
Test the improvement
Evaluate completeness and factual support separately. Test handoff and tool failures; measure staff editing time as well as response quality.
AI engineering experience
Experience from dozens of projects in production.
AI engineers with 8+ years of experience in machine learning and software engineering.
Explore the experience on LinkedIn ↗Research
Multi-agent coordination and computer vision in France and Finland.
Publications
Articles on transformer experiments for computer vision, published in 2019.
Industry
Fixed AI projects for startups and enterprises with 1B+ budgets for AI and digitalisation.
What your engineers learn
Find the problem before changing the model.
Learn the AI engineering method, then apply it across more projects.
- Datasets + ML principles
- Clean and label representative examples. Understand splits, leakage and when model training helps.
- Evaluations + metrics
- Measure correctness, consistency, cost and human correction effort.
- RAG + diagnosis
- Separate source, chunking, retrieval and answer failures. Find what needs to change.
- Experiments + improvement
- Compare against a baseline. Check regressions and learn from reviewed mistakes.
- Agents + coding with AI
- Build harnesses with tools, state and limits. Review generated code and test the outcome.
- AWS CDK + scale
- Define reusable infrastructure, queues, worker limits, logs and cost controls.
A workshop built around your team
Learn on a real project.
1–2 days together, personalised to your engineers' knowledge, current problems and future projects.
01 / BeforeA quick assessment.
Each engineer takes a short quiz. We use the answers to understand their AI knowledge, challenges and what they plan to build.
02 / TogetherTest a real improvement.
Sessions follow the team's needs. Build an eval dataset, measure a baseline, analyse errors and compare a change.
03 / NextUse the method again.
Apply the knowledge to current and future projects, with follow-up support agreed around your team.
Choose the help you need
Capability, a shared core,
or help building it.
01Train your engineers.
A tailored workshop on datasets, evaluations and improvements, using your team's project.
Build a reusable AI Core.
Start with one use case. Reuse the service, datasets, experiments, human review and monitoring for the next.
Build alongside us.
Hands-on AI development with your engineers, clear milestones and regular reviews.
Not sure where to start? Take the project fit check.
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