Artificial Intelligence
Answers from your own data
Ask in everyday words and the system finds the answer in your company data; no answer is used unchecked.
One question across three data sources; not a single row of company records is sent to the language modelLanguage modelAn AI like ChatGPT that reads text and answers in text..
One example
You type the question; the answer comes from your own data
Say the sales manager, instead of waiting for a report, types: “Which products sold the most last month?”
Without the system
Someone is asked, a report is prepared, and the answer arrives days later — by which time the question has changed.
With the system
The answer arrives in a few seconds. Your own database does the calculation; company records are not sent to the AI.
Question
Which products sold the most last month?
Answer
The three best sellers last month:
- 1 Product A 1,240 units
- 2 Product B 980 units
- 3 Product C 715 units
Sample answer. The records it was calculated from are shown underneath.
Services
Generative AI integrations
Working infrastructure
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AI working infrastructure setup
Ask your ERP data questions in plain language. The AI assistant is set up to know your company’s rules, memory and data.
- Rules and persistent memory: the assistant starts every session knowing how the company works.
- Company-specific skill packs and a read-only connection to the ERP and database.
- A verifier sub-agent: numbers and claims are checked before they are presented.
- Data privacy and cost rules, team training and monthly maintenance.
- Flow: free discovery call → workshop → setup (workshop fee deducted) → maintenance.
- Logo ERP
- read-only
- team training
Data and decisions
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AI advisory
Where to start, what is worth automating and what is not — worked through on the company’s own data.
- Processes are reviewed one by one, separating the step worth automating from the step that should stay in human hands.
- Every proposal is tested against “what does this concretely change” — and dropped if the answer is nothing.
- It can run as day-based work or a short engagement focused on one question, alongside your team.
- process analysis
- feasibility
- teamwork
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Turning scattered data into queryable data
Information trapped in PDFs, spreadsheets and irregular records is read, split into fields and turned into a structure you can query from a database.
- Whatever the source — a contract PDF, a spreadsheet with dozens of columns, free text — every extracted field stays linked to where it came from.
- Accuracy is measured by sampling and reported. “Probably right” is not a result.
- The result lives in your own database, and reports, screens and searches are fed from there.
- PDF · Excel
- SQL
- accuracy measurement
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In-house voice query assistant
An employee asks out loud and hears the answer back. The company’s own system does the calculation; its records never go to the language model.
- For the person who cannot use a keyboard — in the field, in a warehouse, in a vehicle: the question is spoken and so is the answer.
- The calculation happens in the database and only the result reaches the model; the query behind the answer can be shown on request.
- speech recognition
- SQL
- in-house
Findability
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A search box that understands intent
The customer no longer has to know the product name: they describe the need in their own words and still land on the right products.
- Someone typing “I want to replace the tiles in my bathroom” no longer sees an empty result just because your catalogue holds “bathroom” and “tile” separately.
- The likely customer phrasings are derived from your own content, product names and descriptions — not guessed.
- The search sits on top of your existing setup; the catalogue is not rewritten.
- e-commerce
- corporate site
- intent matching
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Tracking what was searched but not found
Every search that returns nothing is recorded, then split in two: which of them is a search problem and which is real demand.
- Empty searches are listed by product and phrase and ranked by how often they repeat.
- Some of those searches point at stock already sitting in your warehouse — the list is a direct signal for buying and selling.
- demand signal
- idle stock
- reporting
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Visibility in search engines and language models
How the company appears on the web and inside AI assistants is measured, the gaps are closed, and the result is measured again.
- Search no longer arrives only from Google: a language model has to be able to cite you correctly and not repeat wrong information.
- Structured data, a question-and-answer shape and machine-readable files are put in place, and every claim is closed with a measurement.
- This very site was built with the same method, and its measurement is visible on the Enterprise Software page.
- JSON-LD
- AEO
- measurement
Content and interaction
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Planned content production
A content calendar derived from the company’s own site and social presence: copy and images are produced and wait, ready for an approval.
- The calendar is built around search, answer and store visibility targets, so content follows a plan rather than an impulse.
- Images and videos are produced as well, and none of them is published without approval.
- Production cost is measured and reported, so what each piece of content cost stays visible.
- Built on the APIs of text, image and video generation models (Postimia).
- Postimia
- Gemini API
- approval flow
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AI video and voice-over
Realistic videos at the length you need, with properly licensed background music and an AI-generated voice-over on top.
- Script, footage, voice-over and music are assembled on one line, and the output is a single file ready to publish.
- AI-generated material is labelled the way each platform requires.
- video production
- voice-over
- editing
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Social cuts from a long video
A long recording is cut into pieces that stand on their own, each one adapted to the format of the platform it will be published on.
- Cut points follow the flow of speech, so no clip ends mid-sentence.
- Vertical and square formats, subtitles and a cover image all come off the same line.
- short video
- subtitles
- multi-format
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A voice and chat assistant for your site
An assistant that talks or chats with your site’s visitors and answers from your own content.
- Answers come from the site and the product data: an invented answer is the most expensive kind.
- It speaks: greets the visitor, takes them to the right page and highlights what it is explaining.
- It takes an interested visitor’s contact details and passes them on to you.
- Unanswered questions are recorded and show exactly where content is missing.
- Integrated with a real-time voice API and added to your site with one line of code (Vocalun).
- Vocalun
- Gemini Live API
- your own content
Who arrives here
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Ask your ERP data questions in plain language and get checked answers from your own records.
Companies setting up AI infrastructure -
“We want to do something with AI but we do not know where to start or what would actually work.”
Teams that want AI-assisted automation -
“We pay for ads, traffic arrives, sales do not. We are on Google, but people now ask a chat assistant — and there we do not exist.”
Anyone stuck on findability
Questions
Questions about this page
Can a decision support system be built without giving corporate data to an AI model?
Yes. The question is taken in natural language, converted into a database query, computed in the database, and only the result is turned back into a sentence. In a delivered installation the number of raw records reaching the model is zero.
How do you know the answer an AI gives is correct?
The query and the number behind the answer are shown, so the user can verify the same calculation in their own report. A setup that does not verify model output produces guesses, not decision support.
How do you become visible inside AI search assistants?
Language models have to be able to read and cite the page. That means content in question-and-answer form, structural markup (JSON-LD), and keeping the pages accessible to those models. It does not replace classical SEO; it sits on top of it.