Neural networks for business
A neural network is useful where there is a lot of similar text and conversation: customer requests, calls, documents, applications. We start with the question of how many hours and how much money it should save.
There are two equally useless opinions about neural networks in business: that they will replace half of the staff, and that they are an expensive toy. In practice it is more down to earth. AI copes well with tasks where text or speech has to be understood, where something has to be found in a large volume of material and rephrased — and copes badly where someone has to take responsibility for a decision. The difference between a successful and an unsuccessful rollout almost always lies in which side of that line the task was put on.
What we do
- We work out the payback before the start: the volume of requests, the time they take, the expected share of automation
- We pick the model for the task and the budget, including options in a cloud region you choose
- We build a knowledge base from your materials, so that the answers come from them
- We build it into the channels you already have: website, messengers, telephony, CRM
- We check the quality on real examples before letting it near customers
- We set the boundaries: what the AI does on its own and what it always passes to a person
How it works
One function, one input, one result — and a person who makes the decision.
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01
We find a repetitive operation
We look at where staff do the same thing with text, documents or conversations over and over, and count how many hours a month it takes.
hours, not impressions -
02
We prepare the material
We collect what the model will work on: price lists, policies, typical requests, examples of right and wrong answers.
your documents -
03
We build the solution
We pick the model for the task and the budget and write the wrapper and the rules: what to do with an uncertain answer, where a person is required.
a model fitted to the task -
04
We test it on your data
We run it on the real flow and compare it with how an employee does the job. You can see both the strong points and the places where the model makes mistakes.
checked against a person -
05
We put it into the process
The result goes where people work with it: the CRM, the messenger, a spreadsheet. The quality of the answers stays under observation.
into the working flow
How a neural network differs from an AI agent
Different tools for different tasks. Trying to replace one with the other is expensive both ways.
Neural network
Input → processing → result.
- Performs one specific function
- Does not decide what to do next
- Does not move around your systems on its own
- A person checks and applies the result
- Goes live in weeks and costs less
- Texts, classification, recognition, forecasting, summarisation
AI agent
Goal → plan → actions up to the result.
- Plans the chain of steps itself
- Uses tools: APIs, CRM, databases, email
- Makes a decision at every step
- Takes the process to the end without a person stepping in at each stage
- Needs permissions in your systems and clear boundaries
- A request from arrival to closing, screening, support
What it looks like in real projects
Our products in which targeted models do the work.
- LexIDE — a workspace for a legal practiceThe assistant answers from the materials of a specific case and shows which file the answer came from.
- Infinite AI Text RecognizerRecognition of multi-page documents in a single pass, locally and without the cloud.
- Estato — valuing properties against the marketThe model calculates a fair price from comparable properties and shows the basis of the calculation.
What the business gets
Time saved. An employee stops doing by hand what takes seconds: sorting requests, a summary of a document, a draft reply. We count the hours before the rollout, not after
Lower costs. A growing flow of requests stops meaning a growing headcount — new people are needed only where a person really cannot be replaced
Processing speed. A reply to a customer or the review of a document takes minutes instead of hours, and nights and weekends stop being downtime
Consistent quality. The model does not get tired by the end of the shift: the hundredth document is processed just like the first
Where it does not help
This list matters just as much: half of the failed rollouts start with a task from it.
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01
Where responsibility is required. Legal conclusions, medical recommendations, financial promises — the cost of a mistake is higher than any savings
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02
Where there is little data. Without materials, the model will answer in general words: the knowledge base first, then the AI
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03
Where ordinary automation copes. Moving data between systems does not need a neural network and is more reliable and cheaper without one
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04
Where one hundred per cent accuracy matters. The model makes mistakes: if a mistake is unacceptable in principle, a different tool is needed
What it runs on
We are not tied to one vendor — and that is part of the savings.
We choose the specific model for the task and the budget: not everything needs the most expensive one. If the data must not leave your premises, we deploy an open model on your own infrastructure or in a cloud region you choose.
Integrations
Not a closed list: if your system has an API, we can most likely connect to it too.
Tell us what needs to be done
We reply within one working day. Scoping and the estimate are free.