Redian Software's AI/ML practice is built inside our BFSI, insurance and enterprise-operations delivery teams, not off to one side. CMMI Level 3 Appraised, ISO 27001 and 9001 certified, with data scientists who sit alongside the domain engineers shipping the systems your models will run inside. Nine years of production engineering across 200+ enterprises, delivered from Noida, Nairobi, Dubai, London and New York.
What we build
- Generative AI and RAG systems — retrieval-augmented copilots for customer service, underwriting, adjustment, sales enablement and internal knowledge. Citation-first UX, human-in-the-loop review where the risk demands it.
- ML pricing and rating — insurance motor, health, property and bespoke commercial pricing; credit scoring and dynamic loan pricing; propensity models in the quote flow.
- Intelligent document processing — KYC packs, claims files, policy documents, contracts, bank statements — OCR plus LLM extraction with confidence scoring and structured hand-off to case management.
- Fraud, anomaly and monitoring models — payment fraud, claims fraud, transaction monitoring, identity anomaly, adverse-event detection.
- Predictive analytics — churn, default, collections, capacity, portfolio stress.
- Computer vision — asset inspection (solar, transmission, distribution), damage assessment, document capture, retail shelf audits.
- MLOps — versioned datasets, evaluation harnesses, drift monitoring, rollback and re-training pipelines.
Who we build this for
- Banks, NBFCs, MFIs and SACCOs embedding scoring, fraud and collections models in production
- Insurers, MGAs, brokers and aggregators deploying pricing, claims and document extraction
- Energy and industrial operators layering predictive maintenance and CV inspection on field workflows
- Product companies bolting AI capabilities onto their SaaS without hiring a full research team
- Regulated firms that need explainability, bias testing and reproducible training
Our approach
Discovery — problem framing, data audit, feasibility spike, and success metrics tied to a business KPI. Prototype — a working evaluation on real data in a secure enclave; baseline model; a defensible build vs buy vs hybrid position. Engineering — production pipelines, model training and evaluation harness, deployment in your VPC (or ours), MLOps from day one. Integration and UAT — the model in the actual workflow, not a side dashboard. Go-live and monitoring — evaluation dashboards, drift alerts, quarterly re-training cadence. Timelines committed at the end of Discovery, in writing.
Why Redian for AI/ML
- CMMI Level 3 Appraised, ISO 27001 and 9001 — the assurance posture regulators and model-risk committees expect.
- Domain-embedded data scientists — BFSI, insurance and enterprise engineers, not lab hires learning your business on your dime.
- Model-neutral — OpenAI, Anthropic Claude, AWS Bedrock, Google Vertex, self-hosted LLaMA, Mistral and fine-tuned open models chosen per use case.
- MLOps and explainability by default — SHAP, LIME, dataset provenance and model cards ready for regulator conversations.
- Multi-region delivery — Noida, Nairobi, Dubai, London and New York.
- 9+ years, 200+ enterprises — production ML in supervised environments, not just POCs.
Where we have delivered
AI/ML programmes across India, Kenya, UAE, UK, USA, Canada and Australia, with additional deliveries in Nigeria, Tanzania, Uganda, Ghana, Rwanda, Ethiopia, South Africa and Saudi Arabia. Reference deliveries include pricing ML behind an East African motor insurance aggregator and document AI in a SuiteCRM KYC programme for an investment bank.
Working with Redian
Tell us the use case, the data you have and the KPI you need to move. A senior data scientist responds inside one business day with a written first read and a feasibility position that respects your regulator. NDAs signed on request. Start at /contact, see our AI/ML consulting and AI/ML development services for the mobilisation path, and browse case studies for comparable deliveries.
