STEP 01
Knowledge-base audit
Inventory sources: documents, policies, procedures, client notes, wikis, and operational knowledge.
Automation Infrastructure
RAG Implementation — a secure internal AI knowledge system grounded in your approved sources
Profitec AI builds a private retrieval-augmented generation system so employees can search, retrieve, and use company documents, policies, procedures, and operational knowledge through an AI assistant that answers only from approved sources.
RAG Implementation builds a secure internal AI knowledge system that lets employees search, retrieve, and use company-specific documents, policies, procedures, client notes, and operational knowledge through an AI assistant grounded in approved sources. Profitec AI handles the full build — knowledge-base audit, source cleanup, retrieval architecture, an embeddings/vector database, access rules, answer grounding, and citation logic — with human review flows so answers stay accurate, traceable, and permission-aware.
Where the workflow breaks
Where internal knowledge breaks down
01
Employees cannot find the right document, policy, or procedure when they need it.
02
Knowledge lives in people's heads, scattered drives, and old threads.
03
Generic AI assistants make things up because they are not grounded in your sources.
04
Sensitive documents get exposed to tools with no access control.
05
New hires take months to learn where knowledge lives.
06
No one can tell which source an answer came from.
What Profitec builds
What the RAG system does
A private knowledge layer on top of your approved sources. It retrieves the right passages, grounds every answer in them, and respects who is allowed to see what — with citations and human review where it matters.
Answers employee questions from approved company sources only
Retrieves the exact passages behind every answer
Cites the source document and section for each response
Enforces access rules so people see only what they are permitted to
Cleans, structures, and de-duplicates the knowledge base
Keeps answers current as documents change
Routes low-confidence or sensitive answers to human review
Logs questions, sources, and answers for audit
Pipeline
How the RAG pipeline works
Input
Processing
AI / logic
Human control
Output
Measurement
STEP 02
Source cleanup
De-duplicate, structure, and remove stale or conflicting content before indexing.
STEP 03
Retrieval architecture
Design chunking, metadata, and the retrieval strategy for accurate grounding.
STEP 04
Embeddings & vector database
Generate embeddings and load approved sources into a vector database.
STEP 05
Access rules
Map permissions so retrieval respects who can see each source.
STEP 06
Answer grounding & citations
Constrain the assistant to retrieved passages and attach a citation to every answer.
STEP 07
Human review flows
Route low-confidence or sensitive answers to a person before they are trusted.
STEP 08
Testing, monitoring & handover
Validate against real questions, monitor accuracy and gaps, and hand over documentation.
Integrations
Built around the tools you already run.
Sources
Google DriveSharePointNotionConfluencePDFs
Vector DB
pgvectorPineconeWeaviateQdrant
Models
ClaudeGPTEmbeddingsRerankers
Access
SSORole-based rulesAudit logs
Delivery
SlackTeamsWeb appInternal tools
Automation
n8nMakeAPIsWebhooks
Tooling is illustrative. The automation is designed around the systems you already use, connected through APIs and orchestration layers such as n8n and Make.
What improves
Metrics we measure against a baseline.
Answer accuracy
/01Responses are grounded in retrieved sources, not the model's memory.
Source traceability
/02Every answer cites the document and section it came from.
Time to find knowledge
/03Employees get answers in seconds instead of searching across systems.
Access compliance
/04Retrieval respects permissions, so people see only what they should.
Onboarding speed
/05New hires query the knowledge base instead of interrupting colleagues.
Coverage & gaps
/06Monitoring surfaces questions the knowledge base cannot yet answer.
Controls
Controls & risk
- Answers are constrained to approved, retrieved sources — no ungrounded generation
- Access rules enforced at retrieval, so permissions are respected
- Citations on every answer for traceability
- Human review for low-confidence and sensitive responses
- Audit logs of questions, sources, and answers
- Monitoring for accuracy, stale content, and unanswered questions
Implementation
A controlled path from audit to monitoring.
01
Audit
Inventory knowledge sources, owners, sensitivity, and access rules.
02
Architecture
Design retrieval, chunking, embeddings, the vector database, and grounding logic.
03
Build
Clean sources, index them, and wire access rules, citations, and review flows.
04
Test
Run real employee questions and edge cases; validate accuracy, citations, and permissions.
05
Launch
Roll out in Slack, Teams, or a web app with documentation and review guidelines.
06
Monitor
Track accuracy, gaps, and usage; keep the index current as documents change.
Common questions
What teams ask before we start.
01What is RAG (retrieval-augmented generation)?
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. It is how you get an internal assistant that is accurate, current, and grounded in your own documents.
02How is this different from using ChatGPT?
A general assistant answers from its training and can make things up about your business. A RAG system answers only from your approved documents, cites the source, and respects access rules — so answers are traceable, current, and permission-aware.
03Is our data secure and private?
Yes. Sources stay within your approved environment, retrieval enforces access rules so people see only what they are permitted to, and every answer is logged for audit. We design the architecture around your security and confidentiality requirements.
04What sources can it use?
Documents, policies, procedures, client notes, wikis, and operational knowledge from sources like Google Drive, SharePoint, Notion, Confluence, or PDFs. The knowledge-base audit and source cleanup come first so the system is grounded in clean, approved content.
05How do you keep answers accurate?
Answers are constrained to retrieved passages and carry a citation, low-confidence or sensitive answers route to human review, and monitoring surfaces gaps and stale content so the knowledge base stays trustworthy as it grows.
Next step
Turn scattered company knowledge into one grounded assistant.
A focused review maps your knowledge sources, access rules, and the questions your team asks most — then shows the RAG system worth building and how to keep it accurate.