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What we build
Ten capabilities, one delivery method.
The problems differ. The discipline does not: establish a baseline, build a thin slice end to end, harden it, and hand it over with the evaluation suite that proves it works.
Data Analytics
Turn scattered operational data into decisions people actually act on.
Machine Learning
Custom models trained on your data, evaluated against your economics.
Natural Language Processing
Systems that read documents, tickets and conversations at volume.
Computer Vision
Inspection, counting and recognition on the factory floor and in the field.
Predictive Maintenance
Catch mechanical failure while it is still a maintenance ticket.
Personalised Recommendations
Ranking and discovery that lifts revenue without eroding trust.
Risk & Fraud
Scoring that holds up to auditors, regulators and adversaries.
Supply Chain Optimisation
Forecasting, allocation and routing under real-world constraints.
Customer Insights
Segmentation and churn modelling grounded in behaviour, not personas.
Voice Infrastructure
Speech pipelines for contact centres in Indian and global languages.
01 / Method
Why every one of these is delivered the same way.
A recommendation engine and a defect-detection system share almost no domain knowledge and almost all of their engineering risk. Both fail for the same reasons: no baseline, untested data assumptions, no monitoring, and nobody owning the thing after launch.
- 01Diagnose before proposingA time-boxed paid review of your data, your current process and your constraints. Output is a written assessment — feasibility, approach, cost range, and an explicit list of what would make us recommend against proceeding.
- 02Measure the status quoWhatever the process does today becomes the number to beat. If nobody has measured it, measuring it is the first deliverable, and it frequently changes the brief.
- 03Thin slice, end to endOne narrow path through ingestion, model, serving and interface, on realistic data. Deliberately narrow so that architectural mistakes surface while they are still cheap.
- 04Harden against realityRetries, timeouts, rate limits, cost ceilings, graceful degradation, an evaluation suite wired into CI, and an explicit answer for what the system does when it is wrong.
- 05Transfer ownershipRepository, documentation, runbook, evaluation sets and a working session with the team who will operate it. Ongoing support is available and never assumed.
02 / Error economics
We agree what a mistake costs before we tune a threshold.
Every classifier trades one kind of error for another. Which trade is correct is a business decision, not a technical default — and it changes by sector. A missed fraud case and a wrongly-blocked customer land on different budgets and different people.
We put the four outcomes in front of the people who absorb them and get an explicit decision. It takes an afternoon and it prevents the most common category of post-launch argument.
Next step
Not sure which of these you need?
That is a normal position to be in and a good reason to start with a diagnostic. Describe the process that is slow, expensive or error-prone, and we will tell you which capability applies — or whether none of them do.