05 — AI & ML
AI development — applied machine learning inside business software
Forecasting, document extraction, classification and assistants that answer over your own data — scoped to a decision, and measured.
AI work here is applied, not exploratory. The first question is never which model — it is which decision the system is meant to change, who makes it, how often, and what it costs to get wrong today. Three categories reliably earn their place in operational software: forecasting from transaction history, extraction from invoices and documents with a human review queue, and retrieval-based assistants that answer over company records under the asking user's existing permissions, with citations. Every model ships with an evaluation set, so accuracy is a number both sides can check rather than a claim.
What you get
- Decision scoping and data assessment
- Baseline model and evaluation set
- The feature integrated into the product
- Monitoring for accuracy regressions
Outcomes
- 01
Measured improvement over the current heuristic
- 02
Manual transcription removed, exceptions kept human
- 03
AI features constrained by permissions and citations
FAQ
AI & ML — questions
How much data do we need?
Enough history for the pattern to have repeated many times — typically a year or more of clean transactions for forecasting. The first phase establishes whether your data supports the question at all.
How do you prevent invented answers?
Constrain the assistant to retrieved records, require citations, enforce the user's permissions at retrieval time, and evaluate against a fixed test set. It should say it cannot find an answer rather than produce one.
Have a product in mind?
Tell us what you're building, improving or automating. We'll help turn the idea into reliable software.