The problem
You want AI, advanced analytics, a customer 360 or agentic applications. But the data underneath is duplicated, inconsistent, hard to trace, changes without warning and cannot yet be exposed to AI safely.
“We want to scale AI, but we do not trust the data and knowledge foundation underneath it.”
What the engagement covers
It brings together architecture problems that are usually solved separately:
| Trusted entities | Identity, canonical keys, matching, deduplication, survivorship and golden records. |
| Data reliability | Quality rules, exception handling, observability, ownership, remediation and quality SLAs. |
| Contracts & change | Schema evolution, compatibility rules, data contracts and breaking-change management. |
| AI-ready knowledge | Documents and metadata, semantic enrichment, embeddings, vector retrieval, provenance and grounding. |
| Agent access | What an agent may read, under whose identity, which actions need approval, and how its decisions are audited. |
Humans decide what “trusted” means
Agents help with profiling, metadata extraction, schema comparison, candidate entity matching and lineage discovery. People decide what counts as trusted data, resolve ownership disputes, set survivorship rules, establish policy and define acceptable AI risk.
What you receive
- Trusted-data architecture
- Enterprise identity strategy
- Data-quality operating model
- Data-contract and schema-evolution framework
- Governance and ownership model
- AI-ready knowledge architecture
- RAG / retrieval architecture
- Agent access and control model
- Lineage and provenance model
- Implementation roadmap
What you have at the end
“We have a governed, traceable data and knowledge foundation that can support enterprise analytics and AI safely.”