What it is for
- A stream of similar requests needs sorting
- A few fields must be taken from text for review
- A teammate benefits from a draft reply or a short summary
- There is a knowledge base that can be searched
Practical AI features
AI inside a website, bot or process — not instead of them.
We use practical features: search, classification, data extraction, drafts and summaries. This is not a fourth universal service and not a promise to replace employees.
Problems it solves
- The request topic is unclear, so routing starts too late
- Fields from an email are copied by hand
- An operator needs the point of a long text quickly
What the first version includes
What the first version includes
- 01One checkable task
- 02A limited set of data the model is allowed to see
- 03A response schema
- 04A step where a person accepts, edits or rejects the result
Demonstration layout
Where this appears on screen
AI is not a separate cosmos here. In the layouts it is classification in a queue and a document draft that a person must review.
Which features can help
Which features can help
Knowledge-base search
An answer grounded in chosen materials, not free invention.
Request classification
A topic and a next route according to an agreed scheme.
Data extraction from documents
A draft of fields, not a finished client card.
Draft preparation
Text a teammate can send, edit or reject.
Short summaries
The point of a long email or thread.
Operator support
A hint next to a person, not instead of one.
Possible integrations
- A website, bot or request queue
- A chosen AI API
- The client’s knowledge base
- A log of who accepted or rejected a draft
How development goes
- First we check whether ordinary automation is enough
- We fix a checkable result and the input data
- We add a human step
- We look at errors and model refusal
Timing
Timing depends on one concrete feature, the size of the context and the review rules. Without that, no date is set.
Price range
Price range
There is no separate fixed “AI price”. If the feature belongs to a bot, website or automation flow, we estimate it with that product — after a review of the task. This is not a public offer.
Main functions
- one checkable feature
- limited context
- a human step
Pricing
- the size of the knowledge base
- the number of classes and fields
- log requirements
What is not included automatically
- autonomous agents
- training a model from scratch
- a guarantee of accuracy
What we need from the client
- One concrete function: search, a class, a draft or a summary
- Which data the model is allowed to see at all
- Where a person accepts, edits or rejects the result
- Limits on sending data to an external API
What happens after launch
- We look at model errors and refusals on live examples
- We adjust the response schema rather than “retraining everything”
- If a rule solves the task better, the model is removed
How support works
- Support is counted with the website, bot or process that hosts the function
- A model or data-scope change is a separate decision
- There is no accuracy guarantee
What is not included automatically
- Fully replacing employees
- Universal artificial intelligence
- Automating any business without a process review
Limits
- A model can be wrong. That is why a person stays in the loop
- Not every dataset can be sent to an external API
- If an ordinary rule already closes the task, AI is not needed
Questions
Questions
Is this a separate service?
No. It is a feature inside a website, bot or automation flow.
Does this replace employees?
No. The model prepares a draft or a route; a person makes the decision.
Which data is used?
Only the data you explicitly allow for this feature. Contacts are not sent to ads in the background.
Why start with ordinary automation?
Buttons, statuses and rules are often enough. A model is added when a rule no longer copes.
What needs a separate check?
API access, the data set, legal limits, and where a person must stop.
Why is AI not always needed?
If topics are known in advance, a menu and a route without a model are cheaper and more reliable.
Where a person stays in control
Where a person makes the decision
Input → the model prepares a draft → a person accepts, edits or rejects → an action in the process. We do not launch the feature without that step.
- Input data
- Practical model
- Draft or class
- Review by a person
- Action in the process
Related layouts
Related layouts
The layouts show a possible solution. They are not a client portfolio or growth figures.
A person stays in the loop.
The model does not get the right to make important decisions unnoticed. The result can be checked, rejected and handled by hand.
Telegram
Message in TelegramIf the task is already clear, write in Telegram.
A few lines are enough: what you want to simplify and how it works today. Replies are usually sent between 09:00 and 18:00 MSK.