Production AI/ML development — Generative AI agents, RAG copilots, ML pricing & fraud engines, intelligent document processing. Embedded into the systems that already run your business.
CMMI Level 3 Appraised ISO Certified 200+ enterprises 5 regional hubs 9+ years of delivery
Outcomes that show up in production
The numbers we move.
Real benchmarks from four dimensions our clients measure us against.
6–14 wks
Model to production
Classical ML; GenAI agents 6–12 weeks
+4 pts
Combined-ratio impact
Insurance ML pricing engagement
94%
Production confidence
Underwriter co-pilot deployed in regulated BFSI
Day 1
MLOps live
Versioning, evaluation, drift, rollback — not Phase 2
What we deliver
Everything in the box.
Comprehensive scope — designed to remove the gaps where most engagements typically slip.
01
GenAI agents & copilots
Customer service agents, underwriter co-pilots, RFP responders, internal knowledge agents. Built with RAG grounded in your real documents.
02
ML pricing & rating engines
Insurance pricing, credit risk, dynamic loan terms — with bias testing, explainability and regulator-aware audit trails baked in.
03
Intelligent document processing
KYC, claims, policy docs, contracts. OCR + LLM hybrid pipelines with human-in-the-loop review and confidence-scored outputs.
04
Fraud & anomaly detection
Banking transactions, claims, identity, behavioural patterns. Real-time scoring at production volumes.
05
Predictive analytics
Churn, default, NPS, capacity planning. Production-grade models with monitoring, drift detection and quarterly retraining.
06
MLOps from day one
Versioned datasets, evaluation pipelines, drift monitoring, rollback. Not a Phase 2 afterthought — production engineering from sprint one.
Who hires us
Built for the way your team buys.
We've shaped this practice around the patterns we see most — match yours against the list.
Banks & lenders
Fraud, credit scoring, underwriter copilots, document intelligence — production AI in regulator-aware environments.
Insurers & brokers
ML pricing, claims automation, customer service agents, broker copilots — with FCA/IRDAI/CBK-ready controls.
AI-first scale-ups
SaaS products embedding GenAI agents and RAG copilots as core product features, not bolted-on demos.
Enterprise IT
Large enterprises rolling out internal AI agents — knowledge bases, IT helpdesk copilots, document intelligence at scale.
Healthcare & pharma
Clinical document intelligence, intake automation, internal RAG — with HIPAA / GDPR-ready data flows.
Data-rich organisations
Companies with strong data assets needing to turn them into production ML systems — pricing, recommendation, prediction.
Our process
How an engagement unfolds.
Transparent, milestone-driven, with clear owners and timeframes at every stage.
01Week 1
Outcome alignment
Business outcome, evaluation metric, success criteria, go/no-go thresholds. Written before any modelling.
02Weeks 2–4
Data & feature work
Data quality assessment, feature engineering, baseline model. ~30% of serious ML effort lives here — we don't pretend it's optional.
03Weeks 4–10
Model development
Iteration cycles with weekly evaluation against the success metric. MLflow / SageMaker / Vertex AI infrastructure live from week 1.
04Weeks 10–12
Integration & deployment
Workflow integration, API contracts, latency tuning, production rollout in your VPC. Shadow-mode period before live cutover.
05Weeks 12+
Monitor, retrain, evolve
Drift monitoring, A/B testing, quarterly retraining cycles, bias audits, regulator reviews. The boring work that keeps AI in production.
Service overview
In depth — how this practice runs.
The long-form view of what we build, how we sequence it, and the stacks we run.
Most AI projects fail not because of the model
They fail because they sit on the side of the business, disconnected from the policy admin, the loan origination or the CRM where the real work happens. Our AI/ML practice puts the model in the workflow.
What we do
Generative AI agents — customer service, underwriter co-pilots, RFP responders. Built with retrieval-augmented generation grounded in your real documents.
Generative AI agents (customer service, underwriter co-pilots, RFP responders), RAG pipelines, ML pricing & rating engines, fraud detection, intelligent document processing (KYC, claims, contracts), predictive analytics (churn, default), and computer vision. We have shipped all of these in production for BFSI clients.
Can you fine-tune LLMs on our data?
Yes — both fine-tuning and instruction-tuning on your domain data, plus RAG (retrieval-augmented generation) for cases where fine-tuning isn't worth the cost. We typically recommend RAG first; fine-tuning only when accuracy needs that last few percentage points.
Which models do you work with — OpenAI, Anthropic, open source?
All of them. Closed: OpenAI (GPT-4o, GPT-4.1), Anthropic (Claude Opus, Sonnet, Haiku), Google (Gemini), AWS Bedrock, Azure OpenAI. Open: Llama, Mistral, Qwen, DeepSeek. Choice depends on your latency, cost, privacy and compliance constraints — we benchmark before recommending.
How do you handle AI compliance — bias, explainability, audit?
Bias testing on every model before production, explainability through SHAP/LIME/attribution for classical ML and prompt-based audit trails for LLMs, full versioning and rollback through MLflow, and regulator-aware controls for BFSI/healthcare (EU AI Act, RBI/IRDAI AI guidelines, GDPR-ready).
Do you deploy in our cloud or yours?
Either. Most BFSI clients prefer their own VPC (AWS, Azure, GCP, or on-prem) for data residency. We've also deployed to Redian-managed infrastructure for clients who prefer fully-managed services. Your data never leaves your boundary unless you explicitly choose otherwise.
What's a typical AI/ML project timeline?
Production-ready ML model (classical): 8–14 weeks. GenAI agent or RAG system: 6–12 weeks. ML pricing engine: 12–20 weeks including evaluation. We ship working software every 2 weeks regardless of total length, with evaluation harnesses (not just demos) at each milestone.
Do you have MLOps in place from day one?
Yes — versioned datasets, evaluation pipelines, drift monitoring and rollback are not Phase 2 work. We set up MLflow / SageMaker / Vertex AI on the first sprint and treat them as production infrastructure, not afterthought.
Do you handle the data engineering work too?
Yes when needed — Airflow / dbt / Spark / Kafka / BigQuery / Snowflake pipelines, data warehouse design, feature stores. About 30% of any serious ML engagement is data engineering, so we don't pretend it's optional.
Still figuring it out? Tell us what you're trying to solve and we'll send a tailored proposal within one business day.
