Give AI the right evidence, not simply more tokens
Reliable enterprise AI depends less on how much context is supplied than on whether the right evidence arrives at the right moment, under the right permissions, in a form the model can use. Altivate builds retrieval and context systems that connect documents, transactions, semantic relationships, policy, and live workflow state—then prove the answer is grounded.
A permission-aware context pipeline.
- Understand: Interpret the question, user, task, and authority
- Retrieve: Search text, vectors, graphs, records, and live systems
- Assemble: Rank, compress, cite, and enforce access controls
- Evaluate: Check groundedness, completeness, and task success
Outcome: Answers and actions grounded in current, authorized enterprise evidence
The context stack enterprise work actually needs
Vector search is useful, but it is only one component of a dependable knowledge system.
From scattered knowledge to a measurable context service
Start with the decisions users need to make, then engineer the retrieval path around them.
Define answerable questions
Collect real questions, required evidence, unacceptable omissions, authority rules, and the action each answer should support.
Map sources and truth
Identify systems of record, document owners, freshness requirements, duplicate content, retention rules, and which source wins when information conflicts.
Build and test retrieval
Create representative evaluation sets and measure whether the right evidence appears—not merely whether the final prose sounds plausible.
Operate the context lifecycle
Monitor ingestion, permissions, stale indexes, retrieval drift, failed citations, cost, latency, and user feedback as one production service.
Use the retrieval pattern that fits the question
No single index should be forced onto every type of enterprise knowledge.
| Pattern | Strength | Typical use |
|---|---|---|
| Keyword + metadata | Exact codes, names, clauses, filters, and deterministic scope. | Policies, contracts, product and material identifiers |
| Vector retrieval | Conceptual similarity across varied language and unstructured content. | Knowledge articles, manuals, case histories |
| Graph retrieval | Multi-hop relationships and business context across entities. | Customer, supplier, asset, and control relationships |
| Agentic retrieval | Plans multiple searches, queries tools, and adapts when evidence is incomplete. | Research, investigations, complex operational questions |
Where context engineering changes the result
The strongest cases combine unstructured knowledge with live operational data.
Enterprise RAG FAQ
Do we need a knowledge graph for every RAG system?
No. A graph earns its place when relationships and multi-hop reasoning matter. Many use cases are best served by a disciplined hybrid of keyword, vector, metadata, and structured queries.
How do we stop confidential documents appearing in answers?
Enforce user and group authorization at retrieval time, carry source permissions into indexes, test denial paths, minimize cached content, and log every source used in an answer.
How do we keep answers current?
Assign freshness requirements by source, use event-driven updates where available, monitor ingestion lag and failed connectors, and query live systems directly when an indexed copy is not authoritative enough.
