RAG Knowledge Systems
Are your people searching across ten systems and still not finding the answer?
We build a company AI search engine that handles natural-language queries over your contracts, invoices, documentation, emails, and chat history. Returns answers with source citations. Your data stays with you.
Problem
Knowledge scattered across systems costs you time and money
Information lives everywhere and nowhere
Contracts in one system, invoices in the ERP, documentation in a wiki, history in a messenger. Finding a specific piece of information means clicking through five apps — and still missing it.
Tribal knowledge leaves with people
Whoever knew how that 2023 project worked is at another company now. Others reconstruct it from fragments — and often get it wrong.
Experts answer the same question over and over
IT, HR, legal, or a senior dev answer the same questions every day. An expensive expert hour wasted on repeating something they’ve said five times before.
What we deploy
Specific knowledge system patterns
We connect to your data sources, build a knowledge base, and put a chat with citations on top. Role-based access — users only see what they’re entitled to.
Contracts and legal documents
01"What are the termination clauses in our X contracts?" AI finds the answer in PDF contracts with a citation to the specific paragraph. Saves lawyers hours.
Technical documentation and know-how
02Wikis, runbooks, design docs, ADRs. Search across mixed formats (Markdown, Confluence, Notion, PDF, Google Docs).
AI assistant over invoices and business data
03"How much did supplier X cost us last year?" Q&A over structured and unstructured data from your ERP and CRM, always with a source citation.
New employee onboarding
04A chat that knows company processes, contacts, tools. New colleagues ask the AI instead of bothering everyone else.
Customer support copilot
05AI suggests support replies from previous tickets and user documentation. Reduces average response time and escalation rate.
Q&A over email and chat history
06"What did we agree with the client six months ago?" AI finds the relevant messages and summarizes the decision.
How it works
From audit to production deployment
Audit of your current state
We map your current workflow — what lives where, who handles it, where errors creep in. Output: a concrete list of what can be automated and an ROI estimate.
Pilot on a chosen flow
We start with one specific flow. We build the MVP, connect to your systems and run it in parallel with the existing process for comparison.
Iteration and expansion
We tune accuracy on your data. Gradually expand to more scenarios, users, sources. All under your control and approval.
Full production + monitoring
Pipeline runs 24/7 with automated monitoring, alerts and a monthly report. You focus on the business, we take care of the system.
Why us
RAG isn’t something we’re building for the first time
Our own RAG running in production
In UpTheMind.io we run RAG over PDF documents with vector search and source citations. We’ve solved chunking strategy, re-ranking and how to present citations to users — not theory, our own practice.
A year of building our own AI SaaS
We’re building UpTheMind.io — an AI learning platform with RAG, vector search and multi-language audio. Microservices architecture ready for production load.
No vendor lock-in
A self-hostable solution. You can run it in your own cloud, on-premise, or with us. Full control over your data (GDPR, sensitive business information).
Multi-provider AI strategy
We’re not tied to a single AI provider. Per-task routing, automatic fallback, cost optimization. We bring the same strategy to your project.
FAQ
What customers ask most often
How secure is our data? +
Fully self-hostable. Data stays with you (or with hosting we manage on your behalf — your choice). Embeddings are computed via your approved AI provider, or locally with no outbound calls at all. Role-based access — users only see what they’re entitled to.
Does it work with our formats (PDF, Word, email, Confluence)? +
Yes. Out of the box: PDF, Word, Markdown, HTML, email, Slack/Teams export, Confluence, Notion, Google Docs, SharePoint. Custom connectors for other sources we build as needed.
What happens when information is outdated or wrong? +
Each document carries a timestamp and version. AI cites the specific source so the user sees how old the answer is. When a document changes, the knowledge base is automatically reindexed.
How long does deployment take and how much does it cost? +
Audit is free. MVP with the first sources in 4–6 weeks. Operations are a monthly retainer based on document volume and query traffic. Concrete pricing comes after the audit.
Start here
Schedule a free consultation
30 minutes online. You show us how it works today. We leave with a concrete next-step proposal, ROI estimate and indicative pricing. No slide deck, no commitment.
What you get from the call
- A map of your current process and where the biggest ROI sits
- A concrete proposal of where to start and how to proceed
- Indicative pricing for the pilot and operational retainer
- Time-to-production estimate