Solutions
Intelligent systems — AI, automation and analytics applied to real decisions
AI, automation and analytics scoped to a decision someone actually makes — with an evaluation set, not a claim.
Intelligent systems fail for an unglamorous reason: nobody identified the decision they were supposed to change. A prediction nobody acts on is a cost with a demo attached, so every engagement here starts with the decision, its frequency, and what it currently costs to get wrong.
The categories that reliably work in operational software are narrow: forecasting from transaction history, extraction from documents with confidence scoring and a review queue, classification and anomaly detection, and retrieval assistants that answer over company records under the asking user's existing permissions.
Automation belongs in the same category because it solves the same problem from the other side — removing work rather than informing it. Both depend on the same prerequisite: coherent data models and documented APIs, which is why we often build those first.
Problem → software → result
Three situations we are asked about most often in this category.
- 01
Demand forecasting
- Problem
- Reorder quantities are set by habit, producing dead stock in one category and shortages in another.
- Software
- A model trained on sales history, surfaced as suggested quantities a buyer approves or overrides.
- Result
- Measured improvement against the previous rule of thumb, with the buyer still making the call.
- 02
Document extraction
- Problem
- Supplier invoices arrive as PDFs and photographs and are retyped by hand into the system.
- Software
- Extraction with per-field confidence scoring and a review queue for anything uncertain.
- Result
- Most documents processed without typing, with humans reviewing only ambiguous cases.
- 03
Records assistant
- Problem
- Answering an operational question means knowing which report to open and how to read it.
- Software
- A retrieval assistant answering from records the user is already permitted to see, with citations.
- Result
- Questions answered in plain language, and an answer that can be traced back to a record.
What else we build
FAQ
Intelligent Systems — questions
Is our data good enough?
That is the first thing we establish, before any model is chosen. Where data is too thin or too inconsistent, we say so — and fixing the data model is usually worth doing regardless of whether AI is ever added.
How do you prove it works?
With a fixed evaluation set agreed before launch: known inputs, known-correct outputs, and a measured baseline representing how the decision is made today.
Have a product in mind?
Tell us what you're building, improving or automating. We'll help turn the idea into reliable software.