Diagnostics
A trusted data layer for diagnostics operations
Unify laboratory and operational data into governed datasets for reporting, product analytics, and future AI use cases.
Regulated Sectors
Built for industries where data has to hold up.
We work where scientific context, governance, audit trails, and data trust are non-negotiable—and where getting AI wrong is expensive.
Healthcare & Life Sciences
Diagnostics, R&D, and clinical data made traceable, governed, and ready for responsible AI.
Banking & Financial Services
Regulatory reporting, reconciliation, and risk data, traced to source and audit-ready.
Energy & Utilities
Smart-meter pipelines feeding BRSR and disclosure rails without manual rework.
Semiconductors & Data Centres
Yield data and sovereign telemetry governed at fab and facility scale.
Governed·Audit-ready·Evidence on every figure
Data stories · Life sciences
Explore all data stories From scientific data to governed AI.
Practical patterns for diagnostics, R&D, and healthcare teams that need their data to be traceable, reusable, and ready for responsible AI.
R&D and ML
Make experimental history reusable for ML
Harmonise experiments, formulations, conditions, and results while preserving provenance for prediction and discovery.
Applied AI
Ground assistants in approved enterprise knowledge
Connect governed sources, enforce access controls, and measure answer quality before AI reaches production users.
DatadivesThe foundation check
AI-readiness assessmentBefore the next AI initiative
Ambition is easy.
Is your data
ready for it?
A practical check of the foundation underneath your AI. Ten honest answers. A clearer picture of what to fix first.
About 3 minutes
Your score is instant. No email or signup required.
What we look at
- 01
Pipelines & ingestion
Can you rely on the data arriving?
- 02
Data quality & contracts
Do the numbers stand up to scrutiny?
- 03
Lineage & observability
Can you trace a number to its source?
- 04
Compliance readiness
Can you demonstrate control of your data?
- 05
AI / LLM data safety
Is your foundation safe to build on?
Based on your answers, not an automated audit. Built to start a useful conversation.
Framework
One chassis.
Four rails.
The chassis is the shared engine, ingestion, governance, lineage, and evidence that every regulated workload needs. A rail is that chassis pointed at one regulation.
AI proposes
Models read your sources once and suggest the mappings, the lineage, and the controls, a draft, never the system of record.
Humans confirm
Your team reviews and signs off on every mapping. Nothing runs in production on a guess.
Code executes
Deterministic pipelines run the confirmed logic forever, carrying each figure's evidence to whoever asks where it came from.
The rails on the chassis today
Pravah
प्रवाहRBI compliance & reconciliation
Sammati
सम्मतिDPDP & ABDM health-data
Uthsarg
उत्सर्गBRSR Core & CBAM evidence
Nirmit
निर्मितYield & telemetry for OSATs and DCs
What we are researching
Focus programs
The problems we are spending our own time on right now. Each one feeds a rail on the datadives-hub chassis, and each is open to a working conversation if it maps to something you are dealing with.
Retrieval & governance
DPDP-compliant agentic RAG over enterprise lakehouse data
We are working on retrieval-augmented agents that answer questions across a lakehouse without leaking personal data. The agent plans its own retrieval, but every hop is filtered by purpose, consent and retention rules before a row is ever read, and each answer carries the lineage of what it used.
- Purpose-bound retrieval that respects consent and deletion at query time
- Row and column policies enforced inside the plan, not bolted on after
- Answers that cite their source tables and the controls they passed
Deterministic pipelines
Lineage-preserving reconciliation for regulatory reporting
We are studying how to reconcile large volumes of financial records so that every automated match keeps the evidence and the approval that produced it. The goal is a close that a regulator or auditor can trace back to source without a spreadsheet in the middle.
- Matching rules that stay explainable as data volume grows
- Exception handling that records who decided what, and why
- Evidence packs generated from the pipeline, not assembled by hand
Measurement & assurance
Evidence-grade emissions data for BRSR Core and CBAM
We are building methods to turn scattered energy, production and supplier data into emissions figures that survive assurance. Each number is tied to its measurement, its method and the assumptions behind it, so a reviewer can follow any figure back to where it came from.
- Traceable factors and methods behind every reported number
- Supplier data quality flagged and carried through to the report
- Recalculation without losing the prior reported position
If one of these lines up with a problem on your side, we are happy to compare notes.
Start a conversation