Proof
12 problems we already know how to win.
For each industry we serve, the problem classes we build for — the business problem, the trap in the data, the architecture, and what an engagement hands over. Each one is written against a named public benchmark, so the claim is checkable rather than taken on trust.
Manufacturing
Shop-floor and ERP systems for the NCR mid-market.
On the Severstal Steel Defect Detection benchmarkSurface-defect detection on the production lineCamera-based quality inspection that finds and classifies surface defects at pixel precision, at line speed.On the NASA C-MAPSS Turbofan Degradation benchmarkRemaining useful life for rotating machinerySensor-driven prognostics that say how many cycles a machine has left, so maintenance is scheduled by condition rather than calendar.
Financial services
Regulated data, explainable models, audit trails.
On the Home Credit Default Risk benchmarkCredit scoring for thin-file applicantsDefault-risk models built from relational behavioural data, with the reason codes a regulated lender has to produce.On the IEEE-CIS Fraud Detection benchmarkPayments fraud detection with entity resolutionFraud scoring that first works out who is transacting — because fraud is a property of the actor, not the transaction.
Healthcare
Clinical-adjacent systems, PHI and consent architecture.
On the RSNA Screening Mammography Breast Cancer Detection benchmarkImaging triage for screening programmesDICOM-native models that re-order the reading worklist, so scarce radiologist attention lands on the studies most likely to matter.On the Mechanisms of Action (MoA) Prediction benchmarkMechanism-of-action prediction for drug discoveryMulti-label models over gene-expression signatures that shortlist how a compound acts — narrowing wet-lab work before it is paid for.
SaaS & technology
Product engineering and AI features that survive unit economics.
On the Telco Customer Churn benchmarkChurn prediction with named driversRetention models that say who is leaving and why — so retention spend targets the persuadable instead of the already-lost.On the OTTO Multi-Objective Recommender System benchmarkReal-time recommendations for anonymous sessionsTwo-stage recommenders that personalise from the current session alone — the case that matters, because most traffic is not logged in.
Professional services
Document archives, retrieval with citation discipline.
On the Kaggle LLM Science Exam benchmarkRetrieval-augmented answers over private archivesOn-premise RAG that answers from your documents with citations — built for firms whose archive cannot leave the building.On the Quora Question Pairs benchmarkSemantic matching and deduplication at archive scaleModels that recognise when two documents say the same thing — the quiet capability behind clean knowledge bases and find-the-precedent search.
Public sector
Procurement-ready delivery and documentation.
On the ASHRAE Great Energy Predictor III benchmarkEnergy baselines for building portfoliosCounterfactual consumption models that make retrofit savings measurable — the arithmetic pay-for-performance programmes stand on.On the US Accidents (2016–2023) benchmarkRoad-safety analytics at network scaleSeverity modelling and hotspot analysis over millions of incident records — evidence for where the next crore of road-safety spend goes.
Next step
Bring us the version of this you actually have.
Your data will be messier than the benchmark and your constraint will be different — that is normal, and it is where the engagement starts. If the honest answer is that your data cannot support the build yet, that is what the assessment will say.