SENSETI
Language EN
Contact us
Business digitization for AI

Business digitization for AI

We turn scattered know-how into a knowledge base where every fact is labeled by source.

Get a free consultation How we work

Assessment at a fixed price — credited toward your pilot.

Example: a register of system objects
  • Document “Customer order” verified source: system settings
  • Status “Awaiting shipment” reported source: interview, warehouse
  • Reference table “Dealer price groups” inferred by AI source: change log, not yet confirmed
  • Payment terms for dealers unknown empty until the CFO answers

Checking an AI answer

…move the order to status “Reserve-2”

not in the register, rejected

Why AI makes things up about your business

A model doesn’t know your processes; it only reads what it’s given. When knowledge lives in one person’s head, in chats or in business system settings, AI fills the gaps with plausible fiction. Digitization makes that knowledge structured, verified and searchable, so AI answers about your processes and shows where each answer came from.

When you need this

Knowledge sits with one person

Only one person, often an external specialist, can explain how a system or process works. Their vacation or departure brings work to a halt.

AI invents answers about your processes

A chatbot confidently names documents, statuses or commands you don’t have. Only that same expert can check the answer.

New hires take months to get up to speed

Instructions are outdated or missing, so onboarding depends on asking colleagues.

Your system’s logic lives in its settings

Process rules are configured in the interface rather than written in code. A configuration export explains little to people or to AI.

What’s included

Interviews and process map

Structured interviews with the owner and key people, area by area. The owner can complete some of them alone, at their own pace, in a prepared AI project.

Glossary of terms and roles

What you call your documents, statuses, customers and positions. Without a glossary, AI confuses your internal terms with generic ones.

Register of business system objects

A list of real documents, reference tables and states, each labeled verified, reported or inferred. If it isn’t in the register, AI isn’t allowed to name it, and the checks catch any slip.

Versioned knowledge repository

Knowledge is kept in files with change history, not “inside the model”. You can see who changed a fact and when.

Finding stuck states and dead ends

We look for states a document can’t leave and loops like “A needs B, but B needs A”. A person reviews every finding.

Checks for made-up answers and a test question set

Automated checks compare AI answers against the register. A set of test questions built from your cases shows whether the knowledge base actually works.

How we work

In the consultation we pick the first area. Then we define the knowledge sources and people and prepare an isolated copy of the data, with read-only access to live systems.

What you get
  • A source list, an interview plan and a safe working environment.
To move on

The working environment is ready, and we’ve agreed which data may be processed.

We interview people in the first area and build the process map, the glossary and a map of what we know and what we don’t.

What you get
  • A documented area with gaps clearly marked.
To move on

The area owner has confirmed the as-is description.

We build the register of system objects with source labels, move knowledge into the versioned repository and search for stuck states.

What you get
  • A knowledge repository for the first area and a list of candidate dead ends.
To move on

Every fact carries a source label. Only verified facts reach conclusions for the owner.

We run the test question set plus traps for made-up answers, which are questions about objects that don’t exist in your system.

What you get
  • An acceptance record with the outcome of every question.
To move on

The thresholds agreed before the start are met. Traps for made-up answers count only if all are passed.

We extend the method to other areas and update the knowledge base whenever processes or the system change.

What you get
  • A knowledge base that doesn’t go stale as the system evolves.
To move on

Someone on your side owns the knowledge base, and the update routine is agreed.

In the consultation we pick the first area. Then we define the knowledge sources and people and prepare an isolated copy of the data, with read-only access to live systems.

What you get
  • A source list, an interview plan and a safe working environment.
To move on

The working environment is ready, and we’ve agreed which data may be processed.

We interview people in the first area and build the process map, the glossary and a map of what we know and what we don’t.

What you get
  • A documented area with gaps clearly marked.
To move on

The area owner has confirmed the as-is description.

We build the register of system objects with source labels, move knowledge into the versioned repository and search for stuck states.

What you get
  • A knowledge repository for the first area and a list of candidate dead ends.
To move on

Every fact carries a source label. Only verified facts reach conclusions for the owner.

We run the test question set plus traps for made-up answers, which are questions about objects that don’t exist in your system.

What you get
  • An acceptance record with the outcome of every question.
To move on

The thresholds agreed before the start are met. Traps for made-up answers count only if all are passed.

We extend the method to other areas and update the knowledge base whenever processes or the system change.

What you get
  • A knowledge base that doesn’t go stale as the system evolves.
To move on

Someone on your side owns the knowledge base, and the update routine is agreed.

What the method rests on

The repository holds the knowledge, the model reads it

A model doesn’t remember; it reads. So knowledge lives in a versioned repository you can check, correct and hand to a different model.

Checks catch made-up answers

Even a good knowledge base won’t stop made-up answers by itself. Automated checks against the register and a test set with traps are what catch them.

An honest gap instead of a guess

Whatever nobody knows goes on the “we don’t know” map. A flagged empty cell is more honest than a plausible number.

Your vision first, our questions second

The owner briefly describes how they see the business and the task. Only then do we design the interviews, so we don’t collect knowledge nobody needs.

What you get

Process map and glossary

How your areas really work and what you call things.

Known/unknown map

An honest list of gaps, with who still needs to be asked about what.

Source-labeled object register

Real documents, reference tables and states in your business system, each marked with how we know it.

Knowledge repository with change history

A structured base that doesn’t depend on any particular model.

Candidate dead ends

Stuck states and closed loops in your processes, reviewed by a person.

Test question set and acceptance record

Questions built from your cases, traps for made-up answers and the result of every check.

Case studies

Trading company, EU (import and distribution)

In progress
Task
A non-standard business system where about 80% of process logic lives in the settings of its own process engine rather than in code. All knowledge of it sat with one external specialist. An earlier attempt to feed the configuration to AI as a flat folder export produced made-up answers.
What we did
Structured interviews by area: the owner completed 3 of 8 areas alone in a prepared AI project and handed over 13 documents. Then came a known/unknown map, a register of real system objects with source labels, and automated checks that catch invented commands. The model works with a copy of the system kept apart from the live one.
  • Acceptance was defined before the start: 8 blind tests, including a trap for made-up answers that must be passed 5 out of 5 with no partial credit.

Project in progress; no impact figures yet.

Sports club, about 160 members

Task
Gather knowledge about the club in one place so AI can support the owner’s day-to-day decisions.
What we did
Business digitization through 10 key questions, an AI workspace for the owner, and training for the owner.
  • Knowledge about the club is structured around 10 questions and connected to the owner’s workspace.

In progress; no impact figures claimed.

Get knowledge out of heads and into a system

In a consultation we’ll look at where your company’s knowledge lives and which area to digitize first.

Book a consultation

FAQ

A flat folder export gives the model text with no structure, and it fills the gaps with fiction. We build a register, a glossary and a source-labeled repository, and automated checks catch made-up answers.

That’s a normal starting point. We gather knowledge through interviews, and anything nobody knows goes on the “we don’t know” map instead of being filled in.

No. Non-standard and legacy configurations, where logic lives in settings rather than code, are exactly what we work with. That’s where digitization matters most.

Mostly the owner and the person who knows the system, for interviews and reviewing descriptions. The owner can do part of the interviews alone in a prepared AI project, whenever it suits them.

In a versioned repository hosted inside your environment or in a cloud you approve. You have access to the repository and its change history.

Nobody can guarantee zero made-up answers. We make sure checks catch them through a register of real objects, automated comparisons, source links and a test set with traps.

A pilot on one area takes about 4–6 weeks after the consultation. If there are several areas or many systems, we recommend a readiness assessment first, about 2–3 weeks. We estimate the remaining areas after the pilot, once the real pace is clear.

Other AI for Business services

AI consulting and assessmentAI integration into systems
AI agents and automationAI training for teams
Secure AI adoption

Contact Us

Send a message to our team to find out how we can help you

Andrii

Head of AI for Business

We reply within 1 business day

First Name*
Last Name*
Email Address*
Phone Number
Company Name
Select a country
Ukraine
Poland
Germany
Czech Republic
Slovakia
Romania
Bulgaria
Hungary
Austria
Switzerland
United Kingdom
France
Spain
Italy
Netherlands
Belgium
Sweden
Norway
Denmark
Finland
Estonia
Latvia
Lithuania
USA
Canada
Israel
UAE
Other