Knowledge governance and AI readiness
Inventory, clean, deduplicate, classify and version the knowledge that should become AI accessible.
Enterprise context engineering services
Give enterprise AI the context it needs.
Connect governed knowledge, live data, permissions and tools so enterprise AI can answer, act and show its sources.
Business sources
- Documents
- CRM
- ERP
- Databases
- Live APIs
Context orchestrator
- Identity and permissions
- Governed knowledge
- Retrieval and ranking
- Tools and live data
- Citations and audit
Reliable AI
Answers and actions grounded in the right source for the right user.
source + user + action
The model is rarely the bottleneck.
Enterprise AI fails when the surrounding context is fragmented, stale, permission blind or disconnected from the workflow where the answer matters.
- Conflicting knowledge
- Policies, manuals and product data disagree, while the AI retrieves whichever version is easiest to find.
- Live data trapped elsewhere
- Inventory, pricing and status live in ERP, CRM or databases that a document index cannot keep current.
- Permissions disappear
- A correct answer can still be wrong for the person asking when identity and access rules are missing.
- No production feedback loop
- Teams cannot see weak retrieval, unsupported claims or the questions the system repeatedly fails to answer.
The enterprise context stack
One system, seven context layers.
Context engineering designs what AI may know, retrieve, remember, call and reveal. Each layer has an owner and a failure mode.
- Instructions
- System rules, response boundaries and human escalation policy.
- Identity
- The user, role, department, language and session scope.
- Business knowledge
- Clean, versioned documents, definitions and product information.
- Retrieval
- Source selection, filtering, ranking and evidence quality.
- Memory
- What persists, for how long, and who may inspect or delete it.
- Tools and live data
- Narrow access to ERP, CRM, databases, calculators and APIs.
- Permissions and provenance
- Access control, citations, timestamps and a complete audit trail.
What we design and build
The engagement starts from the business question, then combines only the context layers that question needs.
Enterprise RAG and AI search
Build permission aware retrieval with exact citations, abstention rules and a measurable evaluation set.
Business data and tool integration
Route numeric and live questions to SQL, ERP, CRM or narrow APIs instead of forcing them through document search.
Agent and workflow context
Define tool allowlists, memory boundaries, approval points, iteration budgets and logs for systems that act.
Use the path the question requires.
A production context layer routes by source and task. One retrieval pattern should not handle every business question.
| Question shape | Best path | Reason |
|---|---|---|
| Known answer in governed documents | RAG with citations | Fast, predictable and easy to verify. |
| Totals, trends or current business values | SQL or live API | The answer requires computation or current system state. |
| Repeatable multi-step output | Deterministic workflow | A fixed path is easier to test, operate and audit. |
| Unknown path across several sources | Bounded agentic retrieval | The next search depends on evidence found in the previous step. |
Start with a context assessment.
A small sample of real data and real questions is enough to map the right architecture before a large build begins.
Bring
- 20 to 50 real business questions
- A sample of documents and data sources
- Current access rules and failure examples
Receive
- A RAG, SQL, tool and agent routing map
- Knowledge and permission risks ranked by impact
- A scoped production plan with evaluation criteria
When context engineering pays for itself
This service fits teams moving from a convincing AI demo to a system that people can trust in daily work.
Compare enterprise context engineering platforms- Answers change by user
- Sales, engineering, support and customers should not receive the same context.
- Truth lives in several systems
- Documents explain the policy while ERP or CRM holds the current value.
- Sources must be visible
- Every important claim needs a document, page, owner and date.
- The AI needs to act
- Tool calls and workflow changes require approvals, limits and an audit trail.
Questions buyers ask
Enterprise context engineering questions
What is enterprise context engineering?
Enterprise context engineering is the design of everything an AI system can know, retrieve, remember and act on. It connects governed business knowledge, live data, identity, permissions, tools and provenance so answers remain relevant, secure and verifiable.
Is context engineering the same as RAG?
RAG is the retrieval layer inside a larger context architecture. Enterprise context engineering also covers source governance, live systems, identity, permissions, memory, tool access, citations and operational feedback.
Do we need a context engineering platform?
Sometimes. A platform can accelerate retrieval, governance or connectivity, but most enterprises still need integration around their data model, permissions and workflows. The assessment separates what to buy, what to integrate and what to build.
Can you connect AI agents to ERP, CRM and internal databases?
Yes. Live values should usually come from narrow, permission checked tools or queries. Documents remain in governed retrieval, while structured and current values stay in their systems of record.
When should we use agentic RAG?
Use bounded agentic retrieval when the path to an answer changes with the evidence and spans several sources. Predictable document questions usually need well engineered RAG, while numeric questions usually need SQL or an API.
Build the context before scaling the agent.
Bring real questions, real sources and the constraints that made the pilot fail. We will map the smallest production architecture that can answer reliably.