Pilot ★ Featured
M-Pesa fraud detection ML
Real-time anomaly scoring for mobile-money flows surfacing pump-and-dump receipts, layered laundering trails, and SIM-swap takeovers within seconds of the transaction.
#fraud #mpesa #classificationView →
Labs
Where we're going
The proposals on our desk, the R&D we're investing in, and the AI work we believe in shipping carefully. This is the pipeline behind /projects.
AI focus
Getting it right with AI
AI in the kind of systems we ship payments, civic records, healthcare logistics only earns its place when it's explainable, evaluated, and bounded by hard constraints. We won't ship a model just because the demo looks good.
Explainable by default
Every flag, score or recommendation comes with the reasons behind it in plain English, surfaced where the operator works.
Hard constraints, not prompts
Compliance, privacy and safety limits live in code, not in the model's instructions. The platform refuses what the model can only suggest.
Evaluation before launch
Gold-standard evaluation sets, demographic-strata bias tests, and human-in-the-loop review before any AI output reaches a customer.
“I'm not sure” is a valid answer
We'd rather a model defer than confidently wrong. Uncertainty is a feature; calibration is a release-blocker.
Focus
AI
Working on something adjacent?
If any of this overlaps with a problem you're trying to solve, we'd love to compare notes partnership, pilot or just a conversation.