Internal Knowledge Assistant
An LLM-powered Q&A assistant on the business's internal document corpus — team-facing chat interface, permission-aware, with answer-with-citation so users can verify before acting.
- Engagement
- 6–10 week build · ongoing model maintenance
- Built for
- COOs · Heads of operations · Knowledge management leads
Established businesses accumulate institutional knowledge that's hard to access — policy documents nobody remembers exist, runbooks scattered across SharePoint and Confluence, prior-project records that would answer the current project's questions, customer-history records that the service team can't find in time.
What this is
The team-facing knowledge access layer. Three planes:
- Corpus ingestion. Per-source connectors. Indexing with embedding-based retrieval. Per-engagement fine-tuning on the business's terminology and document patterns.
- Permission-aware retrieval. Document-level access controls integrated with the business's existing identity infrastructure. Users see only what they can see.
- Grounded Q&A. Answers cite source documents. Hallucination bounded by retrieval — no answer without backing.
How it's built
Commercial-API LLM (Claude-class, GPT-class) for the inference layer, or on-prem Llama-class for businesses with that requirement. Embedding-based retrieval (commercial API for the common case, on-prem dense-embedding deployment for the sensitive case). Permission enforcement at the retrieval layer, audited per engagement.
What you get
- The connectors into the business's document systems.
- The retrieval-and-answer infrastructure.
- The permission-aware deployment.
- A team-facing chat interface (Slack, Teams, web — your choice).
- Hallucination-rate measurement and quarterly tuning.
Engagement is shape, not list.
Length and price are functions of the data and the destination. The shape below is the typical engagement.
- Length
- 6–10 week build · ongoing model maintenance
- Lead
- Bogdan
- Cadence
- Async, weekly
- Bar
- Production
Scoped during the discovery call against the actual data and the operation it integrates with.
Principal engineer. Architecture and most code ships through one keyboard.
Written updates between, calls when the decision needs the room.
Async correctness, capacity under burst, observability at every boundary.
Products this composes with.
Same suite, or vertical-specialized versions in another.
- Same suite · AI Implementation Suite
Document Intelligence Engine
A document-AI pipeline that ingests the business's document corpus, classifies by type, extracts the relevant fields, routes to downstream systems with confidence-graded review queue.
- Same suite · AI Implementation Suite
Customer Support AI
An AI layer over the existing ticket stack — routes tickets by category and urgency, drafts initial responses for routine queries, flags edge cases for escalation, surfaces context from prior interactions and product documentation.
- Also in · Family Office Suite
Conversational Portfolio Assistant
An LLM-powered natural-language interface on top of the Multi-Entity Consolidation Platform — accepts questions in plain English, queries the underlying data, returns answers with the supporting numbers and audit links.
What buyers ask about this one.
How is this different from ChatGPT Enterprise or Copilot for the same use case?
ChatGPT Enterprise and Copilot are excellent for the common case (Microsoft 365 corpus, Google Workspace corpus). The differences this product offers: connectors into systems beyond the canonical Microsoft/Google universe (custom databases, internal applications, niche document management systems), per-engagement fine-tuning on the business's terminology, and a permission architecture that integrates with the business's existing access controls.
What about hallucination?
Every answer is grounded in cited source documents. The assistant doesn't generate answers without retrievable backing; where the corpus doesn't contain the answer, it says so rather than making one up. We measure hallucination rate per engagement on a held-out question set.
How is permission handling done?
Document-level permissions enforced at retrieval — the user only sees answers retrievable from documents they have access to. Integrates with the business's existing access controls (Active Directory, Okta, custom-role systems). The LLM doesn't bypass the access boundary.
What systems can it ingest from?
Standard document management (SharePoint, Confluence, Google Drive, Notion). CRM (Salesforce, HubSpot). Ticket systems (Zendesk, Jira, Linear). Custom databases via direct connector. Per-engagement, the connector set is scoped to the business's actual systems.
Pricing?
Scoped to corpus size and user-base size. Discovery call covers both.
If the deliverable matches the gap, the next step is one call.
We'll scope length and price against your data and the operation it integrates with. No retainer, no fishing.
Bogdan and team · async-first · OP—2026