Skip to main content
Capilano AIby Quanteroun Solutions

Enterprise Knowledge Base & RAG

Give people and agents reliable access to company knowledge—with evidence and permissions intact

Turn governed company content into permission-aware search and grounded agent answers with citations, freshness controls, and measurable retrieval quality.

Best suited for
Organizations with fragmented internal knowledge and permission-sensitive content
Engagement shape
Knowledge readiness, retrieval pilot, and governed rollout
Enterprise Knowledge Base & RAG

The service in practice

Start with the work that needs to improve

An enterprise knowledge assistant is trustworthy only when it retrieves the right source, respects the caller's access, shows evidence, stays current, and declines when the knowledge is not sufficient.

Capilano AI builds governed knowledge bases and retrieval-augmented generation systems across documents, intranets, shared drives, data platforms, and business applications. We evaluate retrieval separately from answer generation so quality problems can be diagnosed and improved.

Why this work matters

Replace operational friction with a service your team can trust

What is getting in the way

Knowledge is fragmented and uneven

Policies, procedures, project files, product information, and expert notes live across systems with different formats and owners.

Permissions can disappear during indexing

A retrieval layer that ignores source access controls may expose information the user could not open directly.

Plausible answers hide weak retrieval

A fluent response can be unsupported, stale, or based on the wrong version unless evidence is measured and displayed.

What the engagement should change

A governed knowledge inventory

Approved sources, owners, access rules, content quality, refresh expectations, and exclusions made explicit.

Citation-backed retrieval

Search and answer experiences that preserve source links, metadata, permissions, and abstention behavior.

Measurable retrieval quality

Representative questions, relevance judgments, answer criteria, traces, feedback, and regression tests.

RAG sample

Ask a question and inspect the evidence

This interactive demonstration uses invented sample data. It does not upload a file, call a production model, or represent a client result.

Grounded answer

The production manager and finance lead approve together before a purchase order is issued.

Citation: Sample purchasing policy · §4.2

Where to apply it

Start with a bounded operating outcome

Each use case is scoped with data access, integration, evaluation, human approval, monitoring, and ownership from the beginning.

Policy and procedure assistant

Help employees find the current rule or process with citations and role-appropriate access.

Project and technical knowledge search

Retrieve decisions, specifications, lessons, and evidence across project repositories.

Service and support knowledge

Ground agents or staff in approved product, account, troubleshooting, and escalation content.

Knowledge layer for business agents

Provide shared, evaluated retrieval that multiple task agents can call under the user's identity.

How we deliver

Evidence before scale. Ownership before launch.

The method is intentionally practical: reduce uncertainty early, build the full operating path, and leave the service with people who can run it.

See our delivery approach
  1. 01

    Define the operating outcome

    Map the work, baseline, users, decisions, exceptions, risks, and evidence required to call the engagement successful.

  2. 02

    Prove the difficult assumptions

    Test the data, integrations, model or platform behavior, quality target, and human workflow before scaling the build.

  3. 03

    Build the complete service

    Implement identity, data, workflow, evaluation, telemetry, deployment, documentation, and recovery—not only the visible AI feature.

  4. 04

    Release with an owner

    Roll out in controlled stages, train the operating team, review production evidence, and convert confirmed failures into improvements.

Typical engagement

What your team receives

The exact scope follows the operating outcome and current environment. These are the core work products typically required to make the result useful and supportable.

Knowledge source and access model design

Content parsing, chunking, and enrichment

Hybrid or agentic retrieval architecture

Citation and permission enforcement

Retrieval and answer-quality evaluation

Freshness, feedback, and ownership workflows

Questions to resolve early

Frequently asked questions

Do we need a vector database?

Not always. We choose lexical, vector, hybrid, structured, or agentic retrieval based on the questions, content, filters, permissions, freshness, and operational environment.

Can the knowledge base respect existing permissions?

Yes, when the source and retrieval platform support the required identity pattern and access metadata. We test permission boundaries explicitly rather than assuming they are inherited.

How do you prevent hallucinated answers?

No technique eliminates every error. We improve reliability through retrieval quality, citations, grounded instructions, answerability checks, abstention, tool constraints, evaluation, and human review for consequential decisions.

How is content kept current?

Each source receives an owner and refresh method. We design incremental ingestion or live retrieval, deletion handling, version metadata, failed-sync alerts, and review of stale or low-quality content.

Apply it in context

Connect the service to an operating job

Use case

Enterprise search and grounded RAG

Give teams permission-aware answers grounded in approved company content, with citations, freshness controls, and measurable retrieval quality.

Industry

Real estate

Qualify inquiries, organize property and transaction documents, give teams grounded answers, and keep follow-up moving across brokerage, property-management, and development workflows.

Industry

Film & media

Organize production knowledge, extract metadata from operational documents, accelerate asset discovery, and coordinate repeatable production-office workflows with rights and approvals preserved.

Field notes

Read the implementation detail

All insights

Related services

AI Strategy & Modernization Assessment

Assess workflows, low-code automations, data, risks, and operating readiness, then define a prioritized path from experiments to scalable cloud delivery.

Custom AI Agents

Build task-focused agents that use approved knowledge, call business tools, follow guardrails, and hand work to people when judgment is required.

AI Voice Agent

Deploy natural voice agents for inbound calls, qualification, booking, support, dispatch, and structured follow-up with reliable human handoff.

Bring us the workflow—not a finished AI specification

We will help clarify the operating outcome, difficult assumptions, delivery path, and evidence required for a responsible investment decision.