/technology / data-analytics-platforms

Data & Analytics Platforms

Modern data platforms that make analytics and AI possible — lakehouse architectures, streaming pipelines, vector and feature stores, and the governance that keeps data trustworthy and AI-ready.

Data
Databricks
Snowflake
dbt
Spark
Vector DB
// OVERVIEW

Where this fits in your AI stack

Analytics and AI are only as good as the data beneath them. Modern lakehouse platforms — Databricks, Snowflake and friends — unify analytics and AI on one governed foundation, with streaming pipelines, vector and feature stores for AI, and the lineage and quality controls that make outputs trustworthy. We build that foundation so your data is usable, governed and AI-ready.

01 // CAPABILITIES

A trustworthy foundation for analytics and AI

_01

Lakehouse Architecture

Unified data for BI and AI on Databricks or Snowflake.

_02

Pipelines & Ingestion

Reliable batch and streaming pipelines (dbt, Spark, Kafka).

_03

Vector & Feature Stores

Embeddings and features ready for AI workloads.

_04

Analytics & BI

Self-serve analytics on a single source of truth.

_05

Quality & Lineage

Trustworthy data with full traceability.

_06

Governance & Privacy

Access, masking and compliance by design.

02 // PLATFORM PARTNERS
MicrosoftGoogle CloudAWSSalesforce
03 // IMPACT
1
source of truth
real-time
pipelines
AI-ready
by design
04 // FAQ

Frequently asked questions

Lakehouse, warehouse or lake — which do we need?

Most organisations land on a lakehouse (Databricks or Snowflake) that serves both BI and AI from one governed platform. We assess your needs and design the right architecture rather than chasing a label.

Do we need a vector database?

If you're doing retrieval-augmented generation or semantic search, yes — to store embeddings for similarity search. We integrate it into your data platform and help you choose the right one.

Can you build on our existing data stack?

Yes — we extend what you have with the AI-specific layers (vector/feature stores, quality, governance) rather than rip-and-replace, unless a rebuild is genuinely warranted.

Build your data foundation

Start with a data-platform and readiness assessment.

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