Enterprise Knowledge Assistant
An answer engine over your own data — a RAG assistant that gives staff cited, current answers from the documents and systems you already have.
The problem
Your company’s knowledge is scattered across wikis, drives, tickets, and people’s heads. Staff waste hours hunting for answers, and a generic chatbot without your data just makes things up.
What it includes
The capabilities we build into this solution — engineered for production, not a demo.
Retrieval over your data
Grounded in your docs, wikis, tickets, and databases.
Cited answers
Every answer links its sources so staff can trust and verify.
Permissions-aware
Respects who’s allowed to see what — no data leaks.
Connectors
Integrates with the tools your knowledge already lives in.
Freshness
Stays current as your content changes.
Evaluation harness
Answer quality is measured, not assumed.
How we'd build it
The same method behind everything we ship: de-risked, measured, and in production in one quarter.
Feasibility
We validate the use case on your real data, define the accuracy bar and ROI, and give you an honest go/no-go — usually in weeks.
Build & evaluate
We engineer the system and score it against a labelled set until it clears the bar — quality is a measured number, not a promise.
Deploy
We ship to production, integrated with your systems and behind the right human-approval gates, with monitoring from day one.
Operate & improve
We watch it in production, handle drift, and expand scope as trust builds — feasibility in 6 weeks, production in ~90 days.
What you get
- A cited, permissions-aware assistant over your knowledge
- Less time lost hunting for answers
- Grounded responses you can trust
- A measured accuracy bar and connectors to your stack
Production AI we've shipped
We'd build your solution with the same discipline behind these real, in-production engagements — described by sector under NDA.
A production document classifier — 5,000+ documents/month, built to a ≥95% accuracy target and live in 8 weeks.
Read the case studyBank-statement automation across 21 banks with a measured 0.24% transaction-direction error rate.
Read the case studyAI-at-POS recommendations running against live in-store transactions across every location.
Read the case studyWant to scope Enterprise Knowledge Assistant?
Tell us the use case. We'll come back with a feasibility view and an honest go/no-go — a working prototype in about six weeks.
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Location
Barcelona, Spain • Lisbon, Portugal • Sheridan, WY, USA (incorporation)
Why Choose CONE RED?
- AI R&D lab & venture builder — production systems, not slideware
- Feasibility in 6 weeks, production in ~90 days
- Accuracy measured and evaluated — never invented or guaranteed
- Real engagements across financial services, healthcare & retail
- End-to-end support from strategy to deployment
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