Use cases
Enterprise RAG and knowledge bases that cite their sources.
The answer exists somewhere in your company: a contract clause, a wiki page, an old chat thread. Nobody can find it, so people ask each other or guess. Enterprise RAG puts one assistant over the whole corpus. It answers in seconds, cites the exact source, and respects who is allowed to see what. We build the pipeline, the permissions, and the evals that keep it honest.
What is enterprise RAG?
Retrieval-augmented generation (RAG) connects an AI model to your own documents. When someone asks a question, the system retrieves the most relevant passages from your corpus and the model answers from them, citing each source. Enterprise RAG adds what production demands: per-user permissions so people only see what they are cleared to see, a continuously synced index so answers track today's documents, and evaluation suites that score answer quality as a number. The result is a knowledge base that answers questions instead of returning a list of links.
Who this is for
Funded startups and $10M to $100M companies where the knowledge exists but the finding does not: contracts, policies, past tickets, wikis, call notes, product docs. The signals are familiar. New hires take too long to become useful. Senior people answer the same questions again and again. Support answers depend on who picks up the ticket. If the first move on any hard question is to ask a person instead of a system, this is for you.
- Support teams answering from tribal knowledge instead of a source of truth
- Legal and ops teams digging through contracts and policies by hand
- Sales teams reconstructing answers that already exist in past deals and questionnaires
- Founders whose senior staff have become the company search engine
How the system works, and how it fails
The pipeline is simple to describe and unforgiving to run. Connectors pull documents from your systems of record. An index stores them with their access rules attached. At question time, the system checks who is asking, retrieves only what they may see, and the model answers from those passages with citations. Two failure modes kill most enterprise RAG projects. The stale index: documents change, the index does not, and the assistant quotes last quarter's policy with full confidence. The permission leak: indexing flattens access controls, and a question about salaries gets answered from a file the asker could never open. We design against both from day one.
- Continuous sync from systems of record, not a one-time export
- Permission-aware retrieval: access rules travel with every document into the index and are enforced at query time
- Deletions and revocations propagate: when a document goes, its answers go
- Citations on every answer, down to the passage
- Freshness monitors that alert when the index lags the source
- Eval suites that score groundedness and accuracy per release
- Audit logs of every question, retrieval and answer
The first workflow to build
Do not start with all company knowledge. Start with one corpus, one team, and one question type, where the questions already exist in ticket logs and chat history and the permission boundary is clean. That gives you a golden set to evaluate against and a user group that feels the difference fast. The entry point is our Agent Readiness Audit. We map the corpus, the askers, and the permission boundaries, then hand you a build plan with an honest go or no-go, whoever builds it.
- Support: help docs plus resolved tickets, serving your agents first and customers later
- Legal and ops: one contract set or policy library with a clear owner
- Sales: past proposals and security questionnaires
- Engineering: runbooks and postmortems for on-call
Success metrics
A knowledge base that cannot show its numbers is a demo. We instrument from day one, baseline the old way of finding answers, and report against it. Accuracy is a score, not an impression.
- Answer accuracy against a golden set of real questions, tracked per release
- Groundedness: the share of answers fully supported by cited sources
- Freshness lag: time from a document change to a correct answer
- Permission incidents: the count to hold is zero, and the audit log proves it
- Adoption: questions per week and repeat usage per team
- Expert time returned: hours senior people stop spending on repeat questions
Related proof
Retrieval is not a side quest for us. It sits inside systems we run in production today, on the same security posture throughout: least-privilege access, audit logs, PII masking and data residency honored, designed to support DPDP, GDPR, HIPAA and SOC 2 expectations.
- An insurance advisory engine that cut sessions from about an hour to under fifteen minutes, fully automated, offline capable and audit-traceable
- Care-worker matching built on vector search, running for a client under NDA
- Strata-report and contract intelligence for clients under NDA: long documents in, grounded answers out, with sources
- An autonomous newsroom we operate that researches before it writes and publishes daily at under $2 per article
When does RAG beat fine-tuning?
When the knowledge changes and the answers must be auditable. Fine-tuning bakes knowledge into model weights: slow to update, unable to cite a source, and blind to per-user permissions. RAG keeps knowledge in an index you can update in minutes, cite passage by passage, and permission per person. Fine-tuning earns its place for tone, format and domain reasoning, not for facts. Many production systems use both: a tuned model for behavior, retrieval for knowledge. If your documents change weekly and your auditors ask for sources, start with RAG.
How do you prevent permission leaks?
Access rules travel with every document into the index and are enforced at query time: the system checks who is asking before it retrieves, and deny is the default. Revocations propagate, so losing access to a document means losing access to its answers. Service accounts run least-privilege, PII is masked before models see it, and every question, retrieval and answer is logged. The design is built to support DPDP, GDPR, HIPAA and SOC 2 expectations.
How do you keep the index from going stale?
Continuous sync instead of a one-time export. Connectors watch your systems of record, changes and deletions propagate to the index, and freshness monitors alert when lag grows. Eval suites re-run on a schedule, so drift shows up as a falling score before it shows up as a wrong answer in front of a colleague.
Can this run on our own infrastructure?
Yes. When data residency or confidentiality demands it, we deploy open-weight models with self-hosted vector stores such as Weaviate or pgvector, inside your cloud accounts. Your documents stay in your accounts wherever possible, and the audit trail comes with it.
What happens when the model makes something up?
Grounding reduces invention; engineering handles the rest. Every answer must cite retrieved passages, the assistant declines when retrieval comes back weak, and groundedness is scored in evals before every release. For high-stakes questions, a human review queue sits between the system and the reader.
How fast can we see it working?
The entry is the Agent Readiness Audit: a scoped audit. We map one corpus, its askers and its permission boundaries, and hand you a build plan with an honest go or no-go. A working assistant on your real questions follows fast, with eval numbers you can see before you roll it out wider.
Talk to the people who build.
One call. An honest read on what AI can do for this, and the number it has to beat.