AI integration & RAG
AI that answers from your business.
We connect AI models to your documents, records and systems, so answers come from your own data, cite their sources, respect who may see what and appear inside the tools your team already uses.
Illustration: a field of company documents; three of them light up and connect to a question about payment terms, which is answered with two cited sources.
01 Monday, 09:12
A simple question. Six places to look.
Brandt Metall calls about order #10482: which payment terms apply? Marta in account management knows the answer exists. It is in the signed contract, or a pricing sheet, or an email thread from March, the CRM, the ERP, or a colleague’s memory.
The answer exists. It is spread across systems, versions and people, and every search returns a slightly different one.
02 Step 1 · Connect
We connect the systems where your knowledge already lives.
Nothing is migrated into a new tool. Connectors read documents and records where they are, keep up as they change and carry each system’s permissions with them, down to every passage.
RAG doesn’t retrain a model on your documents. It reads them at the moment a question is asked.
03 Step 2 · Index and retrieve
Every passage mapped. Only the right ones retrieved.
- 01SplitDocuments are split into passages small enough for a model to read and cite precisely.
- 02MapEach passage is placed by its meaning, so related content sits together whatever words it uses.
- 03SearchThe question lands among the passages closest in meaning; keyword search checks exact terms like order numbers.
- 04Filter & rankPassages the person may not see are dropped. The rest are ranked, and only the best few reach the model.
Illustration: eighteen documents from six sources are split into passages, which spread into a space grouped by meaning (contracts and terms, orders and invoices, support history, product docs). The question lands near contracts and terms; of its five nearest passages, one from a salary spreadsheet is withheld because the person asking has no access, one is too weak a match, and three are ranked and passed to the model: the Brandt Metall agreement, the payment terms and the CRM note.
04 Step 3 · Answer
Answers written from your sources, and pointing back to them.
The model answers only from the passages it was given. Every claim links to the exact passage it came from, so anyone can check it in a click.
Example answer: order #10482 falls under Brandt Metall’s framework agreement (source 1, the agreement, page 4), so its standard terms apply: net 30 days from the invoice date, with a 2% discount for payment within 10 days (source 2, the payment terms, section 3.2).
And when the sources don’t say, it says so.
Asked something your documents don’t cover, the assistant doesn’t guess. It says what it found, what it didn’t, and hands the question to the person who can decide, with the sources attached.
Example: asked whether Brandt Metall can move to Net 60, the assistant answers that the documents don’t cover changing payment terms, that the agreement sets Net 30, and sends the request to Finance for approval.
05 Step 4 · Deliver
The answer arrives where the work happens.
In the CRM record, in the support desk as a drafted reply, in team chat or in your customer portal. No one opens another tool; the tools they already use get better.
Example: the same answer fills the payment terms field on Brandt Metall’s CRM record, drafts a reply to their support ticket for review, and answers Marta’s question in the finance channel.
06 Monday, 09:12, again
Same question. A different morning.
Marta answers Brandt Metall from the CRM record, with the contract one click away, and gets back to the work that needs her.
Before: the question goes through the shared drive, the inbox, the CRM, the ERP and a message to Finance, then waits, and ends with an answer of uncertain version. With the assistant: the question gets a cited answer in the CRM and the reply is sent; the rest of the morning goes to calling the customer back and preparing the renewal.
Answers stay consistent
Everyone gets the same answer, from the same current source, instead of whichever version they found first.
Experts get their time back
The people everyone asks are interrupted less, and spend that time on work that needs them.
New people find their feet sooner
What the company knows is searchable from their first day, not locked in colleagues’ memories.
Every answer can be checked
Sources come with every answer, and each question is logged with the passages it used.
Permissions hold
People only get answers from documents they are already allowed to open.
Knowledge stays current
Documents are re-indexed as they change, so answers follow the latest contract, not last year’s copy.
Built for production
Measured like any other production feature.
Before launch we test against real questions from your team, and we keep testing after it. AI runs on the same APIs, data models and infrastructure we build for everything else.
What we build
One retrieval layer, many places to use it.
- 01
Internal knowledge assistants
Answers for teams from contracts, policies, product documentation and records, inside the tools they use.
- 02
Customer support assistants
Drafted replies and self-service answers grounded in your help content and support history.
- 03
AI inside your product
Search, summaries and assistants built into the software your customers already use.
- 04
Document processing
Fields extracted from invoices, contracts and forms into your systems, with review where it matters.
- 05
AI steps in workflows
Classification, routing and drafting inside automated workflows that keep people in control.
- 06
Evaluation & monitoring
Test sets from real questions, quality checks before release and monitoring in production.
A strong fit
When this makes sense.
- Teams answering the same questions from documents every day
- Knowledge spread across drives, inboxes, CRMs and people
- Support or sales teams that need consistent, checkable answers
- Companies that want AI inside their own systems, not another chat tool
What is RAG?
Retrieval-augmented generation. Before the model answers, the system retrieves the relevant passages from your own documents and data, and the model answers from them. Answers stay grounded, current and citable without retraining a model.
Is our data used to train AI models?
No. RAG reads your documents at the moment a question is asked; it does not retrain the model on them. We work with providers and deployment options that fit your data requirements, including hosting in the EU or in your own infrastructure.
Which models do you use?
The one that fits the task, the cost and your data requirements: hosted models from the major providers, or open models deployed in your own infrastructure. The retrieval layer is independent of the model, so it can change later.
What happens when the answer isn’t in our documents?
The assistant says so and can route the question to a person, instead of guessing. We test for exactly this before launch.
Can it take actions, not just answer?
Yes, within limits you set: drafting replies, filling CRM fields, creating tickets or starting workflows, with review steps where they matter.
How do we start?
With one team and one set of questions. We connect the relevant sources, test against real questions from that team and put it in front of them; then extend to the next.
Start a conversation
Which questions does your business answer every day?
Tell us where the answers live today. We’ll show you what it takes to put them one question away.
We usually reply within one business day · contact@velantra.co