SERVICE

Internal Knowledge Base AI Assistants (RAG Systems)

Make your company knowledge searchable in plain language.

4–6
week pilot
✓
GDPR-aligned
EU
data residency
Internal Knowledge Base AI Assistants (RAG Systems)

Why this service

Company knowledge is useless if no one can find it. A RAG system makes documents, manuals and internal threads searchable in natural language — without data leaving your environment.

What you concretely get

Every phase of the engagement produces a written or systemic deliverable. No slide-deck show, no promised "later".

📋

Discovery report

Current state, bottlenecks, baseline metrics and a concrete proposed focus. 12–18 pages, written.

🏗

Architecture diagram

Technical design: model choices, data flow, integration points, security. Readable and reviewable.

💻

Working production system

Deployed system in your environment — not a demo. Source code and configurations are handed over.

📖

Runbook and documentation

User guide, maintenance procedures, failure-mode checklist, escalation paths. Finnish/English.

🎓

Adoption programme

User training per role, internal "AI champion" coaching, 30/60/90-day milestones.

📊

Outcome reports

Monthly: where metrics have moved against the baseline, what adjustments come next month.

How we measure success

Metrics are agreed at discovery and the baseline is captured before implementation. These are the numbers we discuss at the 30/60/90-day check-ins.

  • Time-to-answer
  • Answer relevance (1–5 user rating)
  • Source-citation accuracy
  • Knowledge-base coverage by topic
  • Expert-escalation count
  • Weekly query volume (a proxy for user trust)

Use cases

01

Internal knowledge assistant

Query HR, IT or product info in plain language.

02

Technical docs

Engineers find answers in thousands of pages in seconds.

03

Customer portal

Scoped search experience for customers in their own docs.

04

Compliance search

Statutes and internal policy side by side.

05

Sales enablement

Pitches, comp comparisons and messaging in one search.

06

Onboarding search

New hires reach company knowledge quickly.

Our delivery process

1

Discovery

We map the current state, processes and biggest productivity leaks.

2

Use case selection

We prioritise the highest-value, lowest-risk combinations.

3

Pilot

We build a working version in 4–6 weeks, with metrics.

4

Roll-out

We harden it for production and integrate the SaaS stack.

5

Training

Hands-on workshops and an internal champion for the team.

6

Optimisation

We measure, tune and scale — AI keeps living.

Technologies used

Pinecone Qdrant Weaviate Elasticsearch OpenAI embeddings Cohere LangChain LlamaIndex PostgreSQL pgvector Azure AI Search

Frequently asked questions

A typical pilot completes in 4–6 weeks, with full production use in 2–3 months. We give a concrete timeline after the discovery call.

We design the architecture so your data stays under your control. We support self-hosted models and EU-region cloud, GDPR-aligned.

No. We build on top of your existing SaaS stack — we connect them, we don't replace them.

Adoption is part of every engagement. Without it, AI doesn't produce results — and that's already priced in.

Ready to get this in production?

We always start with a free 30-minute discovery call. No sales pressure.

Book the discovery call →