The data

foundation

your AI actually needs.

We clean up your data chaos and build systems your AI can actually trust. Fast, lean, and backed by engineers who know data inside out.

Book a free data-readiness call
Regulated Sectors

Built for industries where data has to hold up.

We work where governance, audit trails, and data trust are non-negotiable, and where getting AI wrong is expensive.

  • Banking & Financial Services

    Regulatory reporting, reconciliation, and risk data, traced to source and audit-ready.

  • Healthcare & Life Sciences

    ABDM mandates and DPDP consent, with patient data you can actually trust.

  • 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

DatadivesThe foundation check

AI-readiness assessment

Before 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

  1. 01

    Pipelines & ingestion

    Can you rely on the data arriving?

  2. 02

    Data quality & contracts

    Do the numbers stand up to scrutiny?

  3. 03

    Lineage & observability

    Can you trace a number to its source?

  4. 04

    Compliance readiness

    Can you demonstrate control of your data?

  5. 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.

10 questions · 5 dimensions · 1 clear next stepGood AI starts with trustworthy data.
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

प्रवाह
flow

RBI compliance & reconciliation

Sammati

सम्मति
consent

DPDP & ABDM health-data

Uthsarg

उत्सर्ग
dedication

BRSR Core & CBAM evidence

Nirmit

निर्मित
manufactured

Yield & telemetry for OSATs and DCs

Technology

Cutting-edge
tech stack

We leverage the most advanced technologies to deliver solutions that scale with your business needs.

PythonCore
TensorFlowML
Apache SparkBig Data
PostgreSQLDatabase
AWSCloud
KubernetesDevOps
SnowflakeWarehouse
dbtTransform
AirflowOrchestration
KafkaStreaming
PyTorchML
GCPCloud
PythonCore
TensorFlowML
Apache SparkBig Data
PostgreSQLDatabase
AWSCloud
KubernetesDevOps
SnowflakeWarehouse
dbtTransform
AirflowOrchestration
KafkaStreaming
PyTorchML
GCPCloud

Data Engineering

Build robust pipelines that transform raw data into actionable insights at scale.

SparkAirflowdbtKafka

Machine Learning

Deploy production-ready ML models that drive real business outcomes.

TensorFlowPyTorchAWSGCP

Cloud Infrastructure

Modern, scalable infrastructure designed for global enterprise demands.

AWSGCPKubernetesTerraform

AI-Native Delivery

Our team runs on agentic AI workflows by default, so a lean team delivers what used to take a crowd, reviewed by senior engineers for production-grade quality.

LLM AgentsAutomationAI Review

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
Accepting new projects

Ready to make your
data AI-ready?

Let's discuss how we can build trusted data foundations for your AI. Book a free data-readiness call with our team today.

contactus@datadives.in+91 8660047988
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