Your data exists. It's ready to be unlocked from silos.

We architect and build the knowledge infrastructure that unifies structured and unstructured data into an AI-ready foundation — designed around business questions, not technical capabilities.

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Duration
10–14 weeks
Engagement Type
Implementation Architecture, build & integration
Typical Client
CTO, CIO, Head of Data Engineering Organisations with fragmented data landscapes and M365/SharePoint sprawl
Output
Knowledge platform architecture Unified data layer, knowledge graph, semantic search, and AI-ready pipelines

You can't do AI on a fragmented foundation.

Enterprise data is scattered: structured in databases, unstructured in SharePoint, tribal knowledge in people's heads, documents duplicated across drives. Every AI initiative starts with the same painful data wrangling exercise — and most die there.

The answer isn't another data lake or another vendor platform. It's an architecture designed around your business questions — one that makes all your information findable, trustworthy, and ready for AI to work with.

SharePoint and M365 sprawl

Thousands of sites, teams, and channels with no information architecture. People can't find what they need.

Data quality nobody trusts

Multiple conflicting versions of key datasets. Reports that produce different numbers depending on who runs them.

Every AI project starts from scratch

No reusable data pipelines or knowledge layer. Each use case reinvents the wheel.

Unstructured data locked away

Vast amounts of value in documents, emails, and contracts — but no way to make them searchable or AI-accessible.

From data chaos to AI-ready infrastructure.

An integrated platform your teams can trust, use, and extend independently.

01

Unified Architecture Design

Target state architecture connecting structured and unstructured data sources into a coherent, governed layer.

02

Knowledge Graph

Semantic layer mapping entities, relationships, and concepts across your information landscape — the foundation for intelligent search and AI.

03

AI-Ready Pipelines

Automated data pipelines that clean, transform, and deliver data to AI applications with quality checks built in.

04

Semantic Search

Enterprise search that understands intent, not just keywords. Finds answers across documents, databases, and people's expertise.

05

Information Architecture

Governance-ready taxonomy, metadata standards, and content lifecycle management for M365 and beyond.

06

Integration Playbook

Technical documentation, API specifications, and a maintenance plan so your team can operate and extend the platform independently.

Ten weeks from chaos to clarity.

Discover what you have. Design the target architecture. Build iteratively against real business questions. Deploy.

Week 1–2
Discover

Data Landscape Mapping

Source inventory, quality assessment. Understand what you have and where it lives.

Week 3–5
Architect

Target Architecture

Knowledge graph schema, pipeline topology, integration patterns, governance rules.

Week 6–10
Build

Iterative Development

Core components — pipelines, search, graph — with continuous testing against real business questions.

Week 10–14
Deploy

Production & Handover

Production deployment, team training, documentation handover. Ongoing support available.

For those who own the technology foundation

C

CTO / CIO

Responsible for the technology foundation and tired of patchwork integrations.

H

Head of Data Engineering

Needs to build a scalable, maintainable platform — not another data swamp.

C

CDO

Wants a single governed data layer that serves both analytics and AI use cases.

H

Head of Information Management

Dealing with M365 sprawl and needs to bring order to document chaos.

Your AI ambitions need a foundation. Let's build it.

30 minutes. No pitch deck. No obligation.