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AI Platform · 2025

01

Atlas
Intelligence

LLM Integration RAG Platform Consultancy
Atlas Intelligence platform

Unify the data.
Unlock the AI.

Atlas is a 600-person professional services firm with client data spread across 14 different systems. They had invested heavily in AI tools — but every tool was isolated, running on a fragment of the picture. The real opportunity wasn't in any single tool. It was in connecting everything.

KIJO was brought in to design and build the unified data layer that made enterprise-wide AI possible — from search and recommendations to real-time operational intelligence.

Client type Professional Services
Engagement Retainer · 12 weeks
Services Consultancy + Development
Delivered Q1 2025

14 systems.
Zero shared context.

Data was siloed — CRM, project management, finance, documents, emails — each living in a different system with no shared schema or API layer. Existing AI pilots had failed not because the models were poor, but because they couldn't see enough of the business to be useful. Leadership needed AI that understood the whole organisation, not just a slice of it.

No unified data model

14 disconnected systems meant every AI query required manual context gathering — making real-time intelligence impossible.

Semantic gap

Keyword search across fragmented data returned noise. Staff were spending hours finding information that should surface in seconds.

No AI governance

Without a central layer, each team's AI tool had different data access, no audit trail, and conflicting outputs.

One layer.
Everything connected.

We designed and built Atlas — a unified intelligence layer sitting above all 14 systems. A shared data model normalises entities across every source. A RAG system indexes and retrieves with semantic precision. A governance layer controls access, logs every query, and ensures consistent outputs across the organisation.

Unified data model

Custom ETL pipeline harmonising 14 sources into a single entity graph — client, project, document, and people relationships all connected.

Semantic RAG system

Vector embeddings across 2.4M documents. Sub-second semantic search with source attribution and confidence scoring.

AI governance layer

Role-based data access, full audit logging, model version control, and a centrally managed prompt library.

89%

Search
accuracy

3×

Faster
decisions

£400k

Annual
savings

12wks

From brief
to live