One team takes your data and AI work from the first plan to production, then keeps it efficient.
A priced plan before you spend
Strategy & Advisory
Data moved, every number reconciled
Modernization
AI and analytics in production, tested first
Data & AI
Lower running cost on what already runs
Data Estate Optimization
Programs fail at the handoffs, when work moves on before its proof exists. Each practice hands over only with that proof in hand, and the same team carries the work to the next stage.
Judgment before the build.
An honest reading of your estate, the moves ranked by business value and priced, and governance designed before the first workload lands. You leave with a decision a board can fund.
Assess, typically two weeks
Findings you keep either way
Migrations as a factory.
Work moves in repeatable waves, not one heroic migration. Every figure is reconciled before cutover, and the legacy estate is retired on purpose.
90%
Faster data processing than the legacy ElasticSearch stack
SeAir Exim, after the move off ElasticSearch on AWS onto Microsoft Fabric. Shown by Satya Nadella in the Microsoft Ignite 2024 keynote.
Every decision needs an owner.
A platform is a cost until something intelligent runs on it. Real time where the business moves in seconds, agents where judgment can be safely delegated, people where it cannot.
Up to 10 minutes → seconds
Decision latency
Swiggy, on Microsoft Fabric Real-Time Intelligence.
Optimization on a cadence, not a rescue.
Every estate drifts: spare capacity, pipelines nobody retired, rising spend. We tune it on a standing cadence against benchmarks from your own workloads, and recovered spend funds the next workload.
Two weeks with your bill
A cost and health dashboard you keep
We agree the outcome first, then run every engagement as one loop around it.
We start from the business decision you need to improve. If the honest answer is Delay or Don’t build, we say so.
Each engagement is sized to the decision it must produce, and priced before the work starts.
Migrations are reconciled to the cell before cutover, models pass evaluation before release, and results are judged on your data at your scale.
Findings, designs, evidence reports and baselines are yours as the work ships, whether or not you continue.
98%
Defect-detection accuracy
A deep-learning vision model classifies each inspection image against the defect types the QC team defined.
Grasim Industries · Aditya Birla Group
Forbes India · 28 March 2025
<24h
To onboard a new community, down from 3 weeks
One generic FHIR connector lands each community's EMR data in an Azure Databricks lakehouse.
CenteredCare
21%
Net GST growth, department-reported
The department credits AI-based scrutiny as one driver among several.
Government of Andhra Pradesh
SeAir Exim
Trade intelligence · Export-import data
First proved on 500,000 records in two weeks, then scaled on Microsoft Fabric and Azure OpenAI.
Swiggy
Quick commerce · Retail
One real-time platform turns a continuous stream of events into decisions the business can act on.
FleetEnergies
Fleet & Logistics
An on-premise estate consolidated into one data platform, with integrity preserved through the move.
Pine Labs
Payments · Merchant services
A voice assistant follows the client’s own support procedures and hands the call to a person, with the full context, when one is needed.
An industrial AI company building assistants for the manufacturing industry
Manufacturing · Industrial AI
An assistant grounded on technical documentation, maintenance systems and machine sensor data connects an expert when a problem needs a person.
We spend 30 minutes on your estate and your use cases, and the session ends in one shared shortlist.
