Enterprise RAG & Internal AI
Enterprise RAG implementation that turns fragmented company knowledge into an internal AI your team — and your auditors — can trust.
What is enterprise RAG?
Retrieval-augmented generation (RAG) gives an AI assistant a controlled path to your own knowledge: it retrieves the relevant passages from approved company sources and answers from those passages — with a citation — instead of relying on the model's training. An enterprise implementation adds the parts that make it safe to trust: a source registry, permission-aware retrieval, an evidence threshold, abstention when support is weak, human review for sensitive cases, and evaluation before and after rollout. The result is an internal AI that is accurate, traceable, and access-aware.
Where the workflow breaks
Before AI can help, the knowledge has to be findable, current, and access-controlled. These are the failure modes a real implementation is built to remove.
01
Knowledge is fragmented across tools
WhyDrive, SharePoint, Notion, Confluence, CRM notes, PDFs, and SOPs each hold a piece; nothing sees them together.
CostPeople can't find the right source — or trust the wrong one.
02
Answers live in people's heads
WhyCritical context is tribal; it leaves with the person and never reaches new hires.
CostRepeated interruptions, slow onboarding, single points of failure.
03
Generic AI invents answers
WhyA model with no grounding fills the gaps with plausible text about your business.
CostConfident, unverifiable answers no one can trace to a source.
04
Everyone sees everything
WhyA naïve assistant over shared files ignores who is allowed to read what.
CostSensitive contract, HR, and finance content exposed to the wrong people.
05
Sources go stale
WhyDocuments change but the index doesn't; nothing tracks freshness or ownership.
CostThe assistant cites outdated policy as if it were current.
06
No way to tell if it's right
WhyThere is no evaluation of retrieval quality, citation accuracy, or permission leakage.
CostYou can't decide whether the system is safe to roll out.
What Profitec builds
The same path runs on every question: identity and permissions are checked, the relevant policy, SOP, and contract workflow are retrieved, evidence is reranked and thresholded, and the answer is generated only from supported passages — with citations bound to the source, or an honest "I could not verify this" and a route to the owner.
employee question
> What is the approval process for supplier contracts over $50k?
Use cases
Each starts from approved sources, runs inside the control layer, and produces a cited, access-aware answer — or an honest abstention.
Pipeline
Inventory sources, owners, sensitivity, and access rules; pick the first scope.
De-duplicate, structure, and register owners, freshness, and permissions before indexing.
Design chunking, metadata, hybrid search, and reranking for accurate grounding.
Parse, OCR, chunk, embed, and load approved sources into a vector store.
Set the evidence threshold, citation binding, abstention rules, and access checks.
Build offline and live evaluation for retrieval, citations, groundedness, and permissions.
Deliver in Slack, Teams, a portal, or via API with review tooling and guidelines.
Monitor coverage, freshness, and unanswered questions; keep the index up to date.
Production architecture
Chunking, embeddings, and a vector database are necessary but not sufficient. A production system is built in layers — each with its own job, checks, and failure handling — so retrieval is accurate, answers are grounded, and access is enforced.
Documents, policies, SOPs, CRM records, tickets, wikis, contracts.
Owners, source priority, data sensitivity, update cadence, access mapping.
Scheduled syncs, webhooks, parsing, OCR, extraction, deduplication.
Semantic chunking, document hierarchy, metadata, embeddings, vector storage.
Hybrid search, metadata filtering, reranking, relevance thresholds.
Context assembly, grounded generation, answer citations, abstention logic.
RBAC, SSO, source restrictions, review queues, audit records.
Retrieval recall, citation accuracy, groundedness, permission-leakage tests, latency, freshness, unanswered-question analysis.
Integrations
Sources
Vector DB
Models
Access
Delivery
Automation
Tooling is illustrative. The automation is designed around the systems you already use, connected through APIs and orchestration layers such as n8n and Make.
Evaluation
Retrieval coverage
/01Does the correct source appear in the retrieved set for a question?
Citation accuracy
/02Does the cited passage actually support the answer it is attached to?
Groundedness
/03Is the answer supported by retrieved evidence, with nothing invented?
Permission integrity
/04Can a user retrieve only material they are allowed to access — tested, not assumed?
Abstention quality
/05Does the assistant refuse when evidence is insufficient, instead of guessing?
Freshness
/06How quickly do source changes appear in the knowledge index?
The control layer
Retrieval and generation run inside a control layer. Access is enforced at retrieval, every answer is logged, and sensitive or low-confidence cases route to a person before anyone trusts them.
We do not claim 100% accuracy — that would be unprofessional. We claim evaluation discipline: the system is measured for retrieval, citation accuracy, groundedness, and permission integrity, and it abstains when evidence is weak.
RBAC and SSO map to source-level access, so retrieval returns only what the user is cleared to see.
A source registry defines what is in scope, who owns it, and how fresh it has to be.
Below the threshold the assistant says it cannot verify the answer, instead of generating one.
Each answer binds to the document section or workflow step it came from.
Sensitive or low-confidence answers route to an owner for review and escalation.
Questions, retrieved sources, and answers are logged for review and audit.
What you receive
Not a demo on a folder of files — the production layers that make an internal AI accurate, access-aware, and measurable.
Owners, sensitivity, freshness, and access for every connected source.
Chunking, metadata, hybrid search, reranking, and relevance thresholds, documented.
Evidence threshold, citation binding, abstention rules, and access checks.
Offline and live evaluation for retrieval, citation accuracy, groundedness, and permission leakage.
Delivery in Slack, Teams, an internal portal, or via API, with review tooling.
Dashboards for coverage, freshness, and unanswered questions, plus documentation.
Why it isn't a chatbot on files
The gap between a weekend prototype and a system employees can trust is the difference between these two columns — and it is why the cost is not the cost of a chatbot.
| Enterprise implementation | Weak implementation | |
|---|---|---|
| Sources | Source registry and ownership | Uploads random PDFs |
| Retrieval | Hybrid retrieval and reranking | Basic vector search |
| Answers | Evidence-bound answer with citations | Generic answer |
| Access | Permission-aware retrieval | Everyone sees everything |
| Answer quality | Offline and live evaluation | No answer-quality checks |
| Failure mode | Abstain, review queue, escalation | No failure mode |
| Freshness | Freshness monitoring and controlled updates | One-time upload |
Fit
Best fit
Usually not a fit yet
Implementation
Inventory sources, owners, sensitivity, and access rules; pick the first scope.
Design retrieval, chunking, metadata, embeddings, the vector store, grounding, and policy.
Ingest and index approved sources; wire access rules, citations, abstention, and review.
Run offline and live evaluation on retrieval, citations, groundedness, and permissions.
Deliver in Slack, Teams, a portal, or via API with review guidelines and documentation.
Monitor coverage, freshness, and unanswered questions; keep the index current.
Common questions
RAG is an approach where an AI assistant retrieves relevant passages from your approved sources and answers using only that retrieved content, instead of relying on the model's general training. An enterprise implementation wraps it in source governance, permission-aware retrieval, citations, abstention, and evaluation so it is safe to trust.
A chatbot on files uploads documents and answers from a basic vector search, with no access rules, no citations, and no failure mode. An enterprise system adds a source registry, hybrid retrieval and reranking, evidence-bound answers with citations, permission-aware access, abstention when evidence is weak, and offline plus live evaluation — which is why it costs more than a chatbot.
Yes. Sources stay within your approved environment, retrieval enforces access rules so people see only what they are permitted to (tested as permission integrity), and every answer is logged for audit. We design the architecture around your security and confidentiality requirements.
It abstains. Below the evidence threshold the assistant says it could not verify the answer from approved sources and routes the question to the relevant owner, instead of generating something plausible but unsupported.
We evaluate it. Before employees rely on it we measure retrieval coverage, citation accuracy, groundedness, permission integrity, abstention quality, and freshness — and keep measuring after launch. We do not claim 100% accuracy; we claim evaluation discipline.
A RAG architecture review maps your sources, access rules, and the questions your team asks most, then shows the retrieval, grounding, and evaluation worth building first.