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Customer work

Deployments, not demos.

Every engagement below started with a real data estate and a fixed scope, and ended with the team owning the agents, the semantic graph, and the audit trail. Here is the starting state, what an engineer built, how long it took, and what was handed back.

Pharmaceutical · India Cipla

Analytics and reporting agents on top of three siloed systems.

Starting state 22,500+ field reps. Data siloed across the Cirrius CRM, Oracle, and ERP. Every new question an IT ticket.
What was built A semantic execution layer with a Trino federated query engine and a unified graph. Analytics and reporting agents that reason across all three systems.
How long A fixed-scope build against real data, agents in production, then handover to the commercial and operations teams.
What they kept The semantic graph, governed self-serve, and one consistent metric definition across sales, marketing, and operations.
8×Data adoption among business teams
>90%Decision latency cut (days → minutes)
1000×Faster campaign diagnosis
Read the Cipla case study Healthcare & pharma →
BFSI · Asset reconstruction Confidential ARC

A governed NPA-evaluation agent that ships its own justification.

Starting state Retail NPA evaluation ran in months and constrained bidding speed. Legal, technical, field, and executive judgment lived apart.
What was built An evaluation workflow with RBI SARFAESI and DRT rules modeled into the graph, and an auditable reasoning trail behind every bid rationale.
How long A fixed-scope engagement against the ARC's own account data, with regulatory logic proven before any agent ran.
What they kept 100% regulatory coverage, and a bid rationale traceable to regulation, precedent, and the account itself.
>95%Reduction in evaluation cycle time (months → hours)
100%RBI SARFAESI & DRT coverage modeled
100%Auditable reasoning behind every rationale
Read the BFSI case study Banking & financial services →
Travel retail · Global SSP Group

One governed platform in place of fragmented tooling and a custom system.

Starting state Fragmented database tooling and a custom in-house front-line system that the team had to maintain themselves.
What was built A single platform combining SQL, notebooks, conversational analytics, and Slack, on top of the governed execution layer.
How long A fixed-scope replacement that retired the custom system rather than adding another tool beside it.
What they kept The unified platform and the collaboration model across front-line and central teams.
40%Reduction in data-management overhead
3×Faster issue resolution for front-line staff
80%Improvement in cross-team collaboration
Read the SSP case study Retail & travel retail →

The numbers above are from those deployments and from our first-party research. The customer in the BFSI engagement is intentionally undisclosed.

The engagement

Every deployment ends the same way: you own it.

Map, build, ship, handover. The fourth step is the one that matters. See how we deploy.

01

Map

Your datastores, your access model, your first business questions.

02

Build

The semantic graph, the governance layer, and the first data agents against real data.

03

Ship

Agents in production on your cloud, with compile-time governance on every query.

04

Handover

Your team owns the agents, the graph, the audit trail, and the runbooks.

Data AI agents in production. Yours to keep.

Scope a fixed-scope deployment against your own data, in your own cloud.

Scope a deployment How we deploy