Platform
One working cycle: Ada learns, acts and improves
The modules describe what Ada does for your customers. This page describes where her behaviour comes from: five steps repeated every day, and mechanisms you can inspect instead of taking on trust.
Data processed in the European Union · a human approves every data change · records without raw conversation content
The five steps
Each step is a concrete mechanism in the product, not a stage on a slide. In the order they run.
01
Context: who Ada is at your company
Name, language, tone, voice, avatar, greeting, conversation starters, the catalogue of action buttons and the list of domains she may run on. Every customer has their own Ada, and the boundary between customers is enforced on each individual database query, not in the interface layer.
02
Knowledge: where answers come from
Articles with a draft, review, publish and archive cycle. Uploads of PDF, DOCX, TXT and Markdown. Import from a URL and a full site scan that returns for changes on its own. Ada sees published versions only, so a draft never answers a customer.
03
Guardrails: what Ada will not do
The coverage gate rejects an answer with no grounding in published content, before the customer sees a single word. Personal data in a question stops the turn before the model is called. An operation that changes data becomes a card for a human to click.
04
Action: what Ada does with the conversation
She answers in chat, in voice and through the avatar, keeps an e-mail thread going, books and runs demos, surfaces material from the catalogue, and hands the case to a human with the full context when the question goes beyond her scope.
05
Improvement: what remains after the conversation
The gap register collects unanswered questions and turns them into an editorial queue. Quality supervision scores answers beside the critical path. The audit log records every turn in append only form.
How Ada knows that
With an agent talking to your customers, the question that matters is why it said what it said. The answer sits in three places.
- A citation with the answer
- Every claim names the document it came from. The customer sees the source immediately, and you know which article needs fixing when an answer misses.
- A decision trace
- A record of what happened in the turn: codes, versions and measures. Without raw conversation content, prompts or tool outputs, because holding an agent to account does not require them.
- Versioned knowledge
- Articles keep a version history, so you can reconstruct what Ada knew on the day of a given conversation. That is the difference between an explanation and a guess.
What we do with your data
No claims we cannot back. Below are only things written in code and in documents you can read.
| Area | How it works |
| Separation between customers | The tenant scope is part of every database query. Somebody else's resource answers exactly like a nonexistent one, so the response never confirms that it exists. |
| Where processing happens | Database and files in Warsaw. We publish the list of parties processing data on our behalf, with locations and the basis for transfer. |
| Sensitive data | Document, account and card numbers are masked before anything reaches the log. That is pseudonymisation, and we call it that. |
| Data changes | The agent does not perform them. It issues a card that a human approves with a separate click, leaving a trace in the log. |
| Documents | The data processing agreement template, the subprocessor register and the AI systems notice are public, before any sales conversation. |
| Retention | Set for your account and enforced by a daily job, separately for each category of data. |
Technical detail and the full list of documents live on the security page, and the processing parties are listed in the subprocessor register.
What starting looks like
In practice the cycle above begins with one afternoon of work on your side.
1
You point at your content
A website address to scan and a few files you already have: terms, pricing, frequent questions, product descriptions. The rest arrives as the gap register shows what is missing.
2
We set Ada up
Name, language, tone, greeting, action buttons and the domain list. It usually takes one meeting, because the decisions are simple and the defaults work straight away.
3
You paste one line
The widget script into your page template. No site rebuild, no plugin and no changes to your code. From that moment Ada talks and books meetings.
In progress
These are the layers you ask about most often on larger rollouts.
Integrations with sales systems
Passing enquiries, bookings and conversation history into the system your team works in.
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Team calendars
Reading rep availability, so slots come straight from their own calendars.
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Agent workshop
Goals, guardrails and check scenarios in one place, together with a score of how well Ada meets them.
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Questions we get most often
How is the platform different from the modules?
Modules describe what Ada does for the customer: talks, books, replies, surfaces materials. The platform describes why she does it that way: where the knowledge comes from, what she is not allowed to do and what remains after the conversation.
Can I check why Ada answered the way she did?
Yes. Every answer names its source, every turn leaves a trace in the log, and articles keep a version history, so the state of knowledge on the day of the conversation can be reconstructed.
What happens when the knowledge base is missing something?
Ada says plainly that it is not in the materials and offers a human. The question lands in the gap register, so a gap becomes an editorial task instead of an invented answer.
How long does a rollout take?
The first conversation on your own content usually happens the same day you point at the materials and paste the script. The knowledge base matures over a few weeks, driven by the gap register.
Where is data processed?
The database and files sit in Warsaw. Parties processing data on our behalf, with locations and the basis for any transfer outside the European Economic Area, are named in the subprocessor register.
See the whole cycle on your own content
Thirty minutes: we load a slice of your materials, ask the hard questions and show what remains in the log after the conversation.