We help BFSI, healthcare, retail, and SaaS leaders translate GenAI ambition into reliable products — with the guardrails, observability, and governance their security and legal teams actually approve.
Most teams can wire up an LLM in a weekend. The hard part — the part that decides whether your AI ever sees a real customer — is the next 90 days: retrieval that doesn't hallucinate, evals that catch regressions, a cost model that survives scale, and a compliance posture your CISO will sign.
Our AI practice is built around that production reality. Every engagement is led by a senior engineer who has shipped AI to paying users before, paired with a domain analyst who understands your industry's language and edge cases.
We work in tight loops: a measurable success metric is defined in week one, instrumented in week two, and reported on every Friday. No magic. No theatre. Just AI that quietly earns its keep.
We build teams around modern engineering ecosystems ensuring every developer is aligned with your stack, workflows, and delivery standards.
GenAI & RAG
Retrieval
Prompt Design
Guardrails
Everything we offer within this practice, delivered by senior practitioners — not handed to juniors after the contract is signed.
A 2-week diagnostic: where AI moves the metric, where it's a distraction, and what the realistic ROI window looks like.
Production-grade retrieval over your docs, tickets, code, and structured data — with citations, fallbacks, and cost ceilings.
Multi-step agents that draft, review, and act — with human-in-the-loop checkpoints designed by your compliance team.
When prompt engineering hits the wall: SFT, DPO, and distilled open models that cut latency and lock in your voice.
LLM observability, regression evals, prompt versioning, PII scrubbing, and red-team harnesses baked into your CI.
OCR, layout-aware extraction, and visual QA pipelines for insurance, logistics, manufacturing, and healthcare.
We map your workflows, data, and risk appetite — then pick the 1–2 use cases where AI moves a number, not vibes.
A live prototype on your data in 3–4 weeks, paired with an evaluation harness and a cost dashboard from day one.
Hardening for scale, observability, security review, and a clean integration into your existing product surface.
Ongoing evals, drift monitoring, model upgrades, and a quarterly review focused on the business metric we agreed on.
Fixed-price, 2 weeks. A senior AI engineer plus an analyst, working alongside your team to size opportunities and pick a beachhead use case.
Time-and-materials, 8–16 weeks. A pod of 3–5 senior engineers takes the chosen use case from prototype to a production pilot.
Monthly retainer. A long-running AI team that becomes a part of your product org and owns the AI roadmap end-to-end.
Numbers are real, names are sometimes changed at the client's request.
Challenge — Claims adjusters spent 45 minutes per complex file reading unstructured documents — automation pilots never reached production.
What we did — Document AI + human review queue integrated with claims core. Average handling time down 38% with full audit trail.
Challenge — Customer service copilot hallucinated product terms — compliance blocked any customer-facing launch.
What we did — RAG with approved knowledge base, citation requirements, and escalation paths. Pilot passed compliance; 29% call deflection.
“Pure was the only partner who started by asking us how we'd measure success — not by showing slides of someone else's chatbot.”
“We had three vendors attempt the medical summarisation problem. Pure was the only team that took the safety constraints seriously from week one.”
“Their eval harness alone changed how our internal ML team thinks about quality. That's value beyond the engagement itself.”
Trust isn't a logo wall — it's the operating rigour you feel from the first call. Here's what backs ours.
SOC 2 Type II aligned process, ISO 27001 controls, DPDP-ready data handling, and signed MSAs that don't read like a trap.
9 years average experience on every squad. The engineers you meet in the pitch are the engineers who ship — no bait-and-switch.
Every engineer clears a 4-stage technical bar modelled on FAANG-style hiring. Only ~3% of applicants make our bench.
Two-week ship cycles, a Friday demo, and a written changelog. You always know what's done, what's next, and what's at risk.
Average client tenure is 3.4 years. We design for year two of a relationship, not the first invoice — and it shows in the work.
Every engagement starts with a defined success metric and a shared dashboard. We report on outcomes, not just hours burned.
Most engagements eventually pull in a sibling practice — talent into AI, AI into product, product into talent.
Tell us the goal and the constraints. We'll respond within 48 hours with a recommended team shape and a concrete next step.
