SmartAItomationBook a free call Production AI coaching for engineering teams
Stop spending months on AI that only works in demos.
Practical 2-day workshop for the engineers you already have.
Build the capability inside your team
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
Use a repeatable engineering method to shorten trial and error. Carry the useful parts into your next project.
Find the failing step before spending another week changing prompts.
Reuse evaluations, model access and monitoring across use cases.
Put engineering time into the next problem, instead of rebuilding the basics.
See what your team will learn
Explore an example: what breaks, what to change and how to check the improvement.
RAG / search example
Company knowledge, support search and internal assistants.
“How long do I have to return an order?”
“You have 30 days to return it.”
An expired page outranks the current policy. The model confidently repeats it.
“The current return window is 14 days.”
Include the current policy citation. Ask for missing context when eligibility is unclear.
Version the sources. Filter expired policies. Keep rules and exceptions in the same chunk.
Check the resultCurrent evidence found · supported answers · correct abstentions
Tag questions by intent, source version and answerability. Include conflicting policies, duplicate pages and questions with no answer.
Inspect retrieval before rewriting the prompt. Compare chunk boundaries, keyword + semantic search and reranking.
Score evidence retrieval separately from answer correctness. Check citations and have reviewers validate the scoring rules.
Documents example
Invoices, contracts, forms and scanned documents.
Invoice fields: subtotal 120.00 · tax 24.00 · total 144.00
total: 120.00
The extractor selects the subtotal when the supplier changes the layout.
total: 144.00
Link the value to its source. Route missing or ambiguous fields for human review.
Label each field. Test different layouts. Validate types, totals and row relationships.
Check the resultField accuracy · missing values · time spent correcting
Sample suppliers, layouts, scan quality and languages. Label field values and source locations; distinguish absent fields from extraction errors.
Separate OCR, layout, field mapping and validation errors. Review results per field and document type, not only one overall score.
Compare OCR and extraction approaches on held-out layouts. Check totals and row associations, then measure the remaining review effort.
Email replies example
Support drafts, account requests and operations inboxes.
“Where is my order, and can I change the delivery address?”
“Your order arrives tomorrow. I’ve updated the address.”
The reply invents a delivery date and claims an action that never happened.
“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.
Split the request into intents. Check order status and policy. Confirm actions through tools.
Check the resultIntent coverage · unsupported claims · editing time
Tag conversations by every intent, required context and permitted action. Include multi-question threads, missing details and exceptions.
Trace intent detection, retrieved context, tool results and the final reply. Check whether the system had enough information to answer.
Evaluate completeness and factual support separately. Test handoff and tool failures; measure staff editing time as well as response quality.
AI engineering experience
AI engineers with 8+ years of experience in machine learning and software engineering.
Explore the experience on LinkedIn ↗Multi-agent coordination and computer vision in France and Finland.
Articles on transformer experiments for computer vision, published in 2019.
Fixed AI projects for startups and enterprises with 1B+ budgets for AI and digitalisation.
What your engineers learn
Learn the AI engineering method, then apply it across more projects.
A workshop built around your team
1–2 days together, personalised to your engineers' knowledge, current problems and future projects.
Each engineer takes a short quiz. We use the answers to understand their AI knowledge, challenges and what they plan to build.
Sessions follow the team's needs. Build an eval dataset, measure a baseline, analyse errors and compare a change.
Apply the knowledge to current and future projects, with follow-up support agreed around your team.
Choose the help you need
A tailored workshop on datasets, evaluations and improvements, using your team's project.
Start with one use case. Reuse the service, datasets, experiments, human review and monitoring for the next.
Hands-on AI development with your engineers, clear milestones and regular reviews.
Not sure where to start? Book a free call.
Tell us what your team is building, where it breaks and what you want to build next.
imosnoi@smartaitomation.com