SharePoint, file shares, DMS, content platforms.
ERP, CRM, core, dealer, service and plant systems.
Warehouses, lakehouses, databases, product catalogues.
Tickets, cases, call notes, email, wikis.
Regulatory text, standards, market and reference data.
What is enterprise knowledge AI?
Frozen training data, no citations.
Your own content, cited and current.
Enterprise knowledge AI is a system that answers questions from an organisation's own content: documents, records, data and operational history, rather than from a model's training data, and shows the sources it used.
Retrieval-augmented generation, or RAG, is the common technique: find the relevant material first, then have the model answer using only that material. It is why the system can say "according to revision C of the service manual" rather than producing something that merely sounds right.
What makes it an enterprise problem rather than a technical exercise is everything around the retrieval: entitlements, freshness, conflicting versions, and being able to explain afterwards why a particular answer was given to a particular person.
The fastest way to fail a security review is a helpful assistant.
Most knowledge AI pilots index everything into one searchable store. That works beautifully in a demo, and it is the reason so many of them never reach production.
One index, everyone searches it
All documents are embedded into a single vector store. Retrieval finds the best semantic match regardless of who asked. Permissions were a problem for later.
A field technician asks about a pricing exception and receives a passage from the confidential dealer margin agreement, because it was the closest match.
Entitlements enforced at retrieval
Every chunk carries the access control of its source. The user's identity is resolved at query time and filtering happens before retrieval, not after generation.
The same technician receives the published service pricing guidance, with a citation, and never learns the margin document exists.
Filtering after generation does not work. Once a passage has reached the model, it can influence the answer even if the citation is suppressed. The control has to happen before retrieval.
Four ways to build it. Three of them are probably wrong for you.
The architecture should follow the question your users actually ask. Choosing it before understanding that is the most common design mistake in knowledge AI.
Chunk, embed, retrieve, answer. Documents split into fixed chunks, embedded into a vector store, top matches passed to the model. Fast to build, and the default in most tutorials and pilots.
Permission-aware hybrid retrieval. Entitlements attached at ingestion and enforced at query. Hybrid keyword and semantic search, structure-aware chunking, reranking, freshness rules and version handling, with citations on every answer.
Retrieval that plans its own search. The system decomposes the question, runs several targeted retrievals across different sources, checks whether it has enough to answer, and searches again if not. Slower and more expensive per query, and far better on hard ones.
Retrieval over relationships. A knowledge graph models the entities in your business and how they relate: products to parts, parties to accounts, suppliers to components. Retrieval traverses those relationships rather than matching text.
Five stages. The quality is decided in the first two.
Most knowledge AI disappointment traces back to ingestion and chunking, not to the model that generated the answer.
Pull from repositories, systems of record and databases, carrying the source, version, owner and access control of each item.
Integration FabricParse layout properly. Chunk on meaning rather than character count, keeping tables, sections and context intact.
Document AIEmbed and index with metadata, entitlements and freshness attached. Graph relationships built where the domain needs them.
Knowledge ServicesResolve the user's identity, filter to what they may see, run hybrid retrieval, rerank and assemble the context.
Knowledge ServicesGenerate strictly from retrieved material, cite each source, and say plainly when the answer is not in the corpus.
IX AI GatewayFour knowledge patterns that reach production.
Expert assistants
Give a frontline team the answer an experienced colleague would give, from the same sources that colleague would use, with the citation attached so they can verify it.
Typical workflows- Product, spec and compatibility assistants
- Field service and troubleshooting copilots
- Parts identification and superseding
- Dealer and channel knowledge assistants
Policy and compliance answering
Answer questions about what the rules actually say, from the current version, with the clause cited. Includes the ability to say clearly when the rules do not cover the situation.
Typical workflows- Policy, procedure and standards assistants
- Regulatory and compliance queries
- Contract and obligation lookup
- Internal control and audit support
Research and synthesis
Assemble an answer from many sources rather than find it in one. Produces a written synthesis with every claim traceable back to the document it came from.
Typical workflows- Investment and market research
- Supplier, category and competitor analysis
- Technical literature and standards review
- Case and incident history synthesis
Grounding layer for agents
Not a user-facing product at all. The retrieval service an agent calls to establish facts before it acts, so decisions are based on your content rather than on model memory.
Typical workflows- Fact establishment before an agent acts
- Policy checks inside an agentic workflow
- Context assembly for decision preparation
- Reference lookup during extraction
Decades Of Data & AI Delivery
The scale and experience behind every engagement, from strategy through production.
Six reasons knowledge AI projects disappoint.
None of them are the model. We look for all six in the first two weeks.
Fixed-size splitting cut tables in half and separated headings from the content they governed.
Fix: structure-aware chunkingThree versions of the same policy, two of them superseded, all equally retrievable.
Fix: version and freshness rulesPart numbers, error codes and clause references need exact matching, which embeddings are bad at.
Fix: hybrid retrievalThe pilot indexed everything, and the security review ended the project.
Fix: entitlements at ingestionForced to answer from weak context, the system produces something confident and wrong.
Fix: retrieval confidence thresholdsAccuracy was judged by whoever demoed it, so the drop after launch went unnoticed.
Fix: golden sets and scheduled evaluationGrounded in the knowledge your business actually holds.
- Product specialist and configuration assistants
- Field service, troubleshooting and repair guidance
- Parts compatibility and supersession
- Engineering knowledge and design precedent
- Dealer knowledge and warranty policy lookup
- Investment research synthesis and memo generation
- Policy, product and procedure assistants
- Regulatory and compliance question answering
- Advisor preparation and client context assembly
- Case history and prior-decision lookup
Real Outcomes, Delivered at Scale
See how we help enterprises apply AI to improve operations, decision-making and customer experiences.
Transforming Dealer & Customer Support with AI
An AI-powered knowledge platform streamlined dealer and customer support by delivering faster, context-aware responses and reducing manual effort.
Response
AI Virtual CFO Platform for Smarter Financial Decisions
A multi-tenant AI platform consolidated financial data, automated reporting, and delivered advisor-level insights for thousands of concurrent users.
Reporting
From Customer Requirements to Engineering-Ready Designs
An AI-powered platform converted customer requirements into engineering-ready design inputs, reducing design iterations and accelerating production readiness.
Iterations
Decision Intelligence Agents for Financial Institutions
A unified AI finance engine integrated ERP and credit models to deliver real-time liquidity insights and automated working capital decisions.
Decisioning
Technology Supporting Our Knowledge AI
Our knowledge AI builds span models, vector and graph stores, retrieval frameworks, data platforms, entitlement controls, and deployment environments.
The IX AI Foundry provides reusable capabilities for retrieval, ranking, evaluation, entitlements, and operational control.
Python
TypeScript
Go
LangChain
AutoGen
CrewAI
LangGraph
GPT-4
Claude
Llama 3
Mistral AI
Pinecone
ChromaDB
FAISS
Weaviate
CrewAI
AutoGPT
BabyAGI
AutoGen AI
AWS Bedrock
Vertex AI
Azure OpenAI
NVIDIA DGX
DeepSpeed
TensorRT
ONNX
Guardrails AI
Moderation API
Voyager
SWARM AI
CAMEL
SK-LLM
OpenCV
YOLO
TF VisionKnowledge AI Questions
A system that answers questions from an organisation's own content rather than from a model's training data, and shows the sources it used. Retrieval-augmented generation is the common technique. What makes it an enterprise problem is entitlements, freshness, version conflicts and being able to explain afterwards why a specific answer was given to a specific person.
Retrieval-augmented generation means finding relevant material first, then having the model answer using only that material. It remains the right approach for enterprise knowledge, because it keeps answers grounded in content you control, lets you cite sources, and means updating knowledge is a content change rather than a model retrain.
Access control is attached to each chunk at ingestion, carried from the source system. At query time the user's identity is resolved and retrieval is filtered before anything reaches the model. Filtering after generation does not work, because a passage that reaches the model can influence the answer even when its citation is suppressed.
Standard enterprise RAG retrieves once and answers. Agentic RAG decomposes the question, runs several targeted retrievals, checks whether it has enough and searches again if not. Graph RAG traverses a knowledge graph of entities and relationships instead of matching text, which is what compatibility, exposure and dependency questions require.
Usually one of six causes: chunking that destroyed document structure, an uncurated corpus with superseded versions, semantic-only search failing on part numbers and codes, permissions deferred to phase two, no threshold to let the system say it does not know, and no measurement of answer quality. None of them are the model.
Only if your users ask questions about relationships rather than about content. Compatibility, exposure, dependency and impact questions need a graph. Most policy and procedure questions do not, and building a graph you do not need adds significant cost and maintenance.
Freshness rules and content SLAs per source, version handling so superseded material is retired rather than left retrievable, incremental re-indexing on change, and scheduled evaluation that detects when answers begin to drift from the current corpus.
A golden set of real questions with verified correct answers and expected sources, run on a schedule. We measure answer accuracy, whether the right source was retrieved, citation correctness and the rate at which the system correctly declines to answer.
Yes. Content is pulled from document repositories, systems of record and databases through the Enterprise Integration Fabric, carrying the source, version, owner and access control of each item. No migration or rip-and-replace required.
Eight to sixteen weeks to production for a well-scoped corpus and user group. The timeline is driven by content readiness and permission complexity rather than by the model. Corpora that need curation before they can be indexed take longer, and we will tell you that at the assessment stage.