Data & Context Engineering
Build a single governed context layer across every source system in scope.
Data and context engineering unifies enterprise source systems into one queryable context layer — via MCP and knowledge graphs — so agents reason over accurate, governed data in production.
What this accelerator delivers.
Most enterprise AI pilots fail because agents cannot see the full picture of your data estate. Fragmented source systems, undocumented field mappings, and stale catalogues mean agents hallucinate or refuse to act — and compliance teams block go-live. Data and context engineering is the foundation accelerator: embedded Forward Deployed Engineers map every system in scope, unify context through MCP and knowledge graphs, and prepare governed data for agentic workflows. This is Layer 01 of the 4-Layer Intelligence Stack. Without it, every downstream agent operates on guesswork. With it, teams move from demo datasets to production-grade context that survives audit — the prerequisite for every other accelerator in the programme.
Single context layer across all source systems in scope
Field mapping and governance for production agent workloads
MCP and knowledge graph integration built in
Foundation for the 4-Layer Intelligence Stack
Concrete artefacts, not slide decks.
Source system inventory
Complete map of systems, fields, owners, and lineage for every source in accelerator scope.
Context layer architecture
Governed context layer design connecting MCP servers, APIs, and graph stores in your VPC.
Field-level governance rules
Policy controls, access boundaries, and qualification criteria for agent-consumed data.
Production context runbook
Operational documentation for maintaining context quality post go-live.
Four phases to production go-live.
Embed & discover
FDEs embed inside your business, learn the domain, and scope the highest-value use case for this accelerator.
Unify context
Map source systems and stand up MCP connections into a unified, governed context layer in your environment.
Configure & evaluate
Qualify fields, configure graph relationships, and validate context quality against agent workload requirements.
Deploy & monitor
Go live securely in your cloud with FDEE-led monitoring, continuous evaluation, and proactive tuning.
Where this accelerator applies.
Core banking context unification
Unify ledger, CRM, and KYC systems so onboarding agents reason over complete customer context.
Policy and claims data layer
Connect policy admin, claims, and document stores into one queryable context for FNOL agents.
Enterprise data product foundation
Build the governed context foundation for any high-value agentic workflow in scope.
Production outcomes, not pilot metrics.
Typical accelerator go-live in regulated enterprise environments.
Production uptime for governed agent workloads post go-live.
Faster financial close via agentic reconciliation in banking.
Related accelerators
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Frequently asked questions
- What is data and context engineering in enterprise AI?
- It is the foundation capability that maps source-system fields, unifies context, and prepares governed data for downstream agentic workflows and data products.
- How long does context engineering take?
- Typical accelerator engagements scope context unification within the first two delivery phases — embed and ingest — with production context ready before agent configuration begins.
- Does this replace our existing data platform?
- No. Context engineering works within your existing cloud and data infrastructure — connecting and governing what you have rather than replacing it.
- How does context engineering connect to MCP?
- MCP servers provide the connective tissue between source systems and agents. Context engineering stands up and governs those connections as part of the unified layer.