Service
Internal AI Copilot Development
Every question costs twenty minutes and interrupts someone senior?
VelvoAI builds a private assistant that knows how your company actually operates. It reads your SOPs, contracts, past projects and internal wikis, and answers with citations back to the source document — so people can check the answer rather than trust it.
Built by VelvoAI, an independent AI systems studio. One engineer scopes and writes every system.
What the system does
- To an answer
- Seconds
- Every response
- Cited
- Per-user scoping
- RBAC
- ComponentRAG
- ComponentPinecone
- ComponentLlamaIndex
- ComponentNext.js
- ComponentPostgres
The problem
Why this keeps costing you
Institutional knowledge lives in three bad places: documents nobody can find, Slack threads nobody searches, and the heads of your two longest-tenured employees. New hires take months to get productive. Simple questions consume a senior person's afternoon. When someone leaves, part of the business leaves with them.
What we build
Inside an internal AI copilot we ship
- Ingestion pipelines for Drive, SharePoint, Notion, Confluence, PDFs, and email
- Hybrid semantic and keyword retrieval tuned against real staff questions
- Answers with inline citations linking to the exact source passage
- Role-based access control enforced at the retrieval layer
- Slack and Teams surfaces so people ask where they already work
- Feedback capture that flags weak answers for retrieval tuning
How we approach it
The part most implementations skip
Retrieval quality is the whole game. We invest in how documents get chunked, indexed, and ranked before touching the answer layer, because a copilot that retrieves the wrong passage will confidently explain the wrong thing. Permissions are enforced at retrieval time, not filtered afterwards — the model never sees a document the user is not cleared for.
Deliverables
What you actually receive
Every VelvoAI engagement ends with working software you own, not a slide deck. Here is what ships with an internal AI copilot.
How a build runs, step by step- D.01Connected document sources with scheduled re-indexing
- D.02Retrieval evaluation set built from questions your team actually asks
- D.03Chat interface on web plus Slack or Teams integration
- D.04Permission model mapped to your existing identity provider
- D.05Usage analytics showing what people ask and where answers fall short
By industry
Internal AI Copilots in your sector
Internal AI Copilots, applied to the workflows and rules of the sectors we work with most.
- 01
Internal AI Copilots for Law Firms
The firm's know-how is trapped in matters nobody can search.
- 02
Internal AI Copilots for Healthcare
Staff should not need to ask a colleague where the policy lives.
- 03
Internal AI Copilots for SaaS
Support answers exist. They are in a Slack thread from March.
- 04
Internal AI Copilots for Financial Services
Advisers should not be reading a 200-page manual mid-call.
- 05
Internal AI Copilots for Education
Academic staff lose hours to questions the handbook already answers.
Common Questions
01Does our data get used to train a model?
No. Your documents sit in your vector store and are retrieved at query time. We use enterprise API tiers with training disabled, and for stricter requirements we can run open-weight models entirely inside your own infrastructure.
02How do you handle documents people should not see?
Permissions are applied during retrieval, so restricted documents are never candidates for a given user's query. Filtering after generation is not good enough — by then the content has already reached the model.
03What if the copilot does not know the answer?
It says so. The system is tuned to prefer an explicit 'not found in your documents' over an invented answer, because a confident wrong answer costs more than no answer at all.
Related services
Often built alongside this
- Written by
- Usman Ali, Founder
- Last reviewed
Next step
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