Our Knowledge & Data Architecture unifies structured and unstructured data into an AI-ready foundation — knowledge graph, semantic search, and governed pipelines, designed around your business questions rather than your existing technology stack.
Book a diagnostic callEnterprise 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.
Thousands of sites, teams, and channels with no information architecture. People can't find what they need.
Multiple conflicting versions of key datasets. Reports that produce different numbers depending on who runs them.
No reusable data pipelines or knowledge layer. Each use case reinvents the wheel.
Vast amounts of value in documents, emails, and contracts — but no way to make them searchable or AI-accessible.
An integrated platform your teams can trust, use, and extend independently.
Target state architecture connecting structured and unstructured data sources into a coherent, governed layer.
Semantic layer mapping entities, relationships, and concepts across your information landscape — the foundation for intelligent search and AI.
Automated data pipelines that clean, transform, and deliver data to AI applications with quality checks built in.
Enterprise search that understands intent, not just keywords. Finds answers across documents, databases, and people's expertise.
Governance-ready taxonomy, metadata standards, and content lifecycle management for M365 and beyond.
Technical documentation, API specifications, and a maintenance plan so your team can operate and extend the platform independently.
Discover what you have. Design the target architecture. Build iteratively against real business questions. Deploy.
Source inventory, quality assessment. Understand what you have and where it lives.
Knowledge graph schema, pipeline topology, integration patterns, governance rules.
Core components — pipelines, search, graph — with continuous testing against real business questions.
Production deployment, team training, documentation handover. Ongoing support available.
Responsible for the technology foundation and tired of patchwork integrations.
Needs to build a scalable, maintainable platform — not another data swamp.
Wants a single governed data layer that serves both analytics and AI use cases.
Dealing with M365 sprawl and needs to bring order to document chaos.
We architect and build the knowledge infrastructure that unifies your structured data (databases, warehouses) and unstructured data (documents, M365, contracts) into a single AI-ready foundation — designed around your business questions rather than your existing technology stack.
A data lake stores everything; a knowledge architecture makes it findable and AI-usable. We add a semantic layer (knowledge graph), a governance taxonomy, and pipelines that deliver clean, contextualised data to AI applications. The point is not storage — it is making information accessible to humans and models.
Ten to fourteen weeks from data landscape mapping to production deployment, depending on source-system complexity. We build iteratively against real business questions so you see value from week six, not at the end.
No. The architecture connects to your existing data sources. You may consolidate over time, but day-one value comes from unifying access, not replacing infrastructure. The knowledge graph and pipelines sit on top of what you already have.
SharePoint and M365 are first-class sources in the architecture. We design an information architecture (taxonomy, metadata standards, content lifecycle) and connect SharePoint into the unified data layer, so its content becomes searchable and AI-accessible alongside your structured data.
We deliver a fully documented integration playbook — technical specifications, operating procedures, and a maintenance plan — so your team can operate and extend the platform without us. Continued advisory is available but not required.
30 minutes. No pitch deck. No obligation.