Six core practices. One delivery team. From the first whiteboard to production-grade systems running 24ร7.
We design retrieval-augmented assistants, multi-agent workflows and domain copilots โ with evals, guardrails and observability baked in.
We modernise legacy warehouses and build cloud-native lakehouses that scale โ without breaking budgets.
From feature stores to deployed inference โ we ship models that hold up in production with continuous evaluation.
Sub-second pipelines for fraud detection, IoT telemetry, observability and personalisation.
Executive dashboards, embedded analytics and self-serve BI that drive the metric that matters.
Cloud-native foundations on Azure & AWS with zero-trust, IaC and cost guardrails โ built to scale and audit.
We don't chase fashion โ we pick what survives production.
Working POC for one well-defined use case โ model, UI, evals, and a go/no-go report.
Fixed-scope or milestone-based engagements with weekly demos and a production deployment.
A cross-functional pod โ engineers + ML + design โ embedded with your team for 3โ12 months.

We've watched too many ML projects die in pilot. Our delivery model is built around the unglamorous parts that decide whether a model lives or dies in production.
Every engagement ships architecture docs, ADRs, eval harnesses, observability, CI/CD, IaC and a runbook โ because production isn't a finish line, it's a starting point.
Start a Project โ
Idempotent, observable, replayable โ the boring properties that quietly make the difference between calm operations and weekend incidents.
Every engagement ships these โ not just slides and a slack channel.
System diagrams, ADRs and trade-off notes you can hand to any future engineer.
Reproducible evaluation suite that scores models and pipelines on every change.
Logs, traces and dashboards wired into your stack from day one โ not an afterthought.
One-click deploys, IaC, rollback paths and pre-prod environments.
Incident playbooks, on-call rotations and recovery procedures.
Pair-programming, code-walkthrough sessions and recorded knowledge transfer.
Threat model, IAM audit and a hardening checklist signed off before go-live.
Final outcome memo measuring the business metric we agreed on at kickoff.
Three models: fixed-price for well-scoped projects, milestone-based for medium-complexity builds, and monthly retainers for dedicated pods. We share a written estimate after a 30-minute scoping call.
Yes. We routinely sign mutual NDAs, MSAs, DPAs and pass through procurement security reviews for clients in BFSI, healthcare and government adjacent sectors.
You do. All deliverables โ source, models, weights where applicable, infra-as-code and documentation โ are transferred to your repositories and accounts.
Often yes. We start with a 1-week audit: codebase review, architecture assessment, dependency check, and a written take-over plan with risks and timelines.
We work inside your cloud accounts whenever possible, use synthetic / masked data for development, and respect data-residency constraints (India, EU, US). Logging, IAM and audit trails are designed in from day one.
A pod typically has 1 tech lead, 2โ4 engineers (mix of backend, ML, frontend as needed), and fractional design + product input. You'll know each engineer by name and meet them weekly.
For a 2-week AI sprint, usually within 7โ10 days of signoff. For a full pod engagement, 3โ4 weeks depending on the seniority mix.