AI answers 48x faster than a human
A B2B SaaS with a heavy stream of tickets. We built an AI copilot that answers from their knowledge base — handling a request dropped from hours to minutes.
Where we started
The product was growing, and so was the flow of support and pre-sales tickets. Almost every reply meant searching the docs, the knowledge base and the ticket history.
The answers existed, but they were scattered across docs and wikis. One non-trivial request took an agent up to 40 minutes: find it, verify it, write it up.
The queue grew faster than the team, and response times kept slipping. New hires needed a long time to get up to speed before they could answer on their own.
The task: hand the search and the first draft to the machine and leave the final check to people.
The pain points
- Up to 40 minutes on one complex request
- Knowledge scattered across docs and wikis
- The queue grew faster than the team could hire
- Response times kept slipping
- New hires took too long to get up to speed
What we built
Brought the knowledge base and docs together into a single store for the AI
Set up AI that finds the answer in your data and checks its sources before replying
Claude drafts the answer with citations to the source documents
Simple requests close automatically, complex ones go to a human
Every new answer makes the system more accurate
How a request flows: from question to answer
Under the hood it's the same setup as our corporate knowledge base with AI.
A customer writes to support or pre-sales in plain words, at any hour.
The AI searches by meaning across the docs, knowledge base and ticket history, even when the question and the document use different words.
Claude drafts an answer with quotes from the documents. Each quote can be opened and checked.
The answer goes to the customer without a human, nights and weekends included. That's how 90% of requests get closed.
It goes to an agent together with the draft and links: they check it and send it.
The result in money
People stay on quality control — the machine handles the grind of searching and drafting.
A similar situation? We review your funnel in an hour and tell you what from this case you can repeat.
I want the sameTimeline and how we work
This is how we work with every client:
Free. We look at where the knowledge lives, which questions come up most and how much time goes into finding answers.
Sources, access rights, where the AI will live, success metrics.
We build the base from real documents and test it on real questions, tuning until the answers are right.
We onboard the team and, if needed, an AI agent for customers.
Included in the project. Success metrics go into the contract: if we miss them, we keep working for free or refund your money.
"The team was clearing the queue by hand and burning out. Now the copilot closes most requests on its own, and the team works on the things that actually need a brain."
What you can repeat in your company
A similar result is realistic where people spend time hunting for answers that are already written down somewhere. We don't promise numbers in advance: they go into the contract after the audit.
More on the corporate knowledge base with AI and AI in B2B sales pages. If answers need to go out in messengers, we connect the knowledge base to an AI agent.
When it works
- Many questions repeat, and the answers are already written down: in docs, wikis, a knowledge base.
- The base has an owner who keeps it current: the AI answers exactly as the documents say.
- It's clear which questions the AI closes on its own and which always go to a person.
- You've agreed up front which data can be passed to the model.
Questions about this case
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