Patient Data Migration
Secure enterprise migration of legacy patient health records to modern cloud infrastructure under strict compliance.
Read case studyAI by Industry
Detect fraud as it happens, decide on credit with explainable models and settle claims faster — with the audit trails, data residency and controls your regulators expect.
Trusted by enterprises and fast-growing startups
Use cases
Hover a use case to see it in context — every one ships with explainability and audit logs.
Solutions
Machine learning that spots fraud rings, mule accounts and suspicious patterns across every channel.
Faster, fairer lending decisions with models your risk team can explain.
Onboard customers end to end with document AI and video KYC support.
Insurance workflows that read documents and photos and route every case by risk.
Assistants for customers and agents across app, web, WhatsApp and IVR.
Monitoring and reporting that keeps pace with your regulators.
Before and after
Flip the switch to see how the same application moves with and without AI.
The customer uploads PDFs and staff check each one by hand.
Statements and IDs are read and verified the moment they arrive.
Analysts total salary credits in spreadsheets.
Cash flow is analysed automatically, with anomalies highlighted.
Rules alone decide, and edge cases wait in a queue for days.
An explainable model works with policy rules; only edge cases reach a credit officer.
Run in batches after the loan is approved.
Run in real time, before any money moves.
Customers call the branch to ask for a status.
Proactive updates on the app and WhatsApp at every step.
Our success stories
Real projects across AI, data, healthcare and commerce — from the first workshop to a live system.
Secure enterprise migration of legacy patient health records to modern cloud infrastructure under strict compliance.
Read case studyClinical-grade diagnostic and imaging assistance for cancer research — CNN classification combined with U-Net segmentation.
Read case studyMulti-vendor digital storefront and catalogue system for high-value designer commerce.
Read case studyPredictive anomaly detection and telemetry aggregation pipelines optimised for industrial manufacturing environments.
Read case studyLarge-scale climate intelligence and environmental classification platform for advanced data processing.
Read case studySecure analytics suite and health data visualisation platform for high-volume enterprise tracking.
Read case studyOur roadmap
Each stage is reviewed with your risk and compliance teams before the next begins.
We map priority journeys, data sources and regulatory constraints with your business, risk and IT teams.
Masking, residency and access rules are agreed before any model is trained.
Models are built, back-tested and documented for model-risk review.
A limited rollout runs alongside today’s process so results can be compared.
Rollout across products and channels, with drift, bias and performance monitoring.
Compliance
We design with the frameworks your risk, compliance and audit teams already work to.
Start with a workshop on your priority journeys and leave with a pilot plan your risk team supports.
Yes. We use explainable models with reason codes for every decision, keep full decision logs, and document models for your model-risk and audit teams.
In India — inside your own cloud or data centre if required. Sensitive fields are masked before any model sees them, and models can run entirely in your environment.
No. We integrate with your existing core, LOS, LMS, CRM and policy administration systems through APIs and events.
Fairness is tested before launch and monitored afterwards across customer segments, against thresholds agreed with your risk team.
A focused pilot on one journey — such as fraud alerts or document checks — typically runs in weeks, with results compared against your current process.
Explore more
See how we apply AI in other sectors, or browse our core AI capabilities.
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