AI-Powered Applications
AI is no longer an add-on but a natural part of the product. From text generation to image processing, smart search and recommendations to decision support, we weave AI capabilities naturally into your application.
AI is no longer an add-on — it's a natural part of the product
An AI feature doesn't stand in the air; it needs an orderly, typed, single-source data model underneath it. That's exactly what we built while putting 46 sector products on the same core: customer, booking, order and reporting data lives in one structure, not scattered tables. When a feature like smart search, recommendations or summarization gets added, the ground it stands on is already there.
We'd rather be honest: we don't have a separate, branded 'AI product' out in the market. What we bring instead is two things — solid data-architecture discipline, and experience adding large-language-model/RAG integrations into production code in the right place — as an adapter, not embedded in the core. From text generation to image processing, smart search to decision support, we weave these capabilities into your application naturally.
- A single-core, AI-ready data model
- LLM/RAG integration added as an independent adapter
- Data-architecture discipline from 46 products
- No inflated promises — an honest capability framework
6 benefits of adding AI in the right place
Faster search & discovery
Meaning-based search instead of keyword matching; the user finds what they're looking for on the first click, not the third.
Personalized recommendations
Recommendations shaped by the user's past behavior; a ranking specific to that person, not a generic list.
Automatic summarization & reporting
Distills a long document or a pile of reports into a readable summary; the wait before a decision shortens.
Document/image understanding
Turns data from an invoice, form or photo into a structured record without manual entry; the error margin drops.
Chat assistant & automated response
Handles frequent questions and simple requests without waiting for a human; hands off to a person correctly when things get complex.
Decision support, not decision-making
AI output offers a suggestion; a human makes the final call — critical business decisions stay traceable and justified.
6 AI services
Scope narrows or widens by project; all of it draws from the same discipline — the data model first, the feature second.
Large language model (LLM) integration
We connect a provider's API to your app as an independent adapter, not embedded in the core.
Smart search & recommendation systems
We build embedding-based similarity search and a recommendation engine.
Document/image understanding & content generation
We turn unstructured data (documents, images) into structured records and generated content.
Chat assistant & automated responses
We build an assistant that talks with your product's data — not a generic chatbot, but a layer tied to your data.
Customization with your own data (RAG)
The model answers using your documents and database, not generic internet knowledge.
Decision support & anomaly detection
We build a layer on top of your report/grid data that flags unusual patterns.
The question we ask before adding AI: is the data ready?
The data model first, the feature second
Adding AI on top of scattered spreadsheets and free-text fields is building a floor on a cracked foundation. So we ask first whether the data is ready: is business data in typed columns, are there duplicate records, are fields consistent. The typed-schema discipline we've built across 46 products usually makes the answer 'yes'.
- Business data in typed columns, jsonb only for genuinely free-schema data
- A single-source data model, no duplicate records
- A short data-readiness pass before AI
Low-risk integration via an adapter architecture
We don't embed an AI provider into the core; it's added as an independent adapter. If the provider changes, pricing changes, or a model's capability falls short, only that adapter is updated — the rest of the application is unaffected. This is the same principle as our gating engine: when the source changes, only the adapter changes.
- A provider-independent adapter architecture
- A model/provider change doesn't touch the core
- Critical decisions always remain subject to human approval
Realistic AI use cases by sector
How can AI be used in healthcare products?
Turning free-text notes in a patient file into a structured summary, flagging an unusual value in test results; the final medical decision always stays with the physician.
Where does AI help in retail and marketplaces?
Auto-generating product descriptions from catalog data, ranking search results by meaning, suggesting reorders based on demand forecasting.
What can AI do in logistics?
Beyond route optimization, forecasting delivery time from historical data and flagging an unusual delay pattern early.
What kind of contribution does AI make in HoReCa?
Summarizing menu performance from sales data, generating a reorder alert before stock runs out; it speeds up reading the data behind the till, not the human decision at it.
Is AI used in real estate and enterprise products?
Auto-summarizing a listing description, matching client and portfolio by semantic similarity, auto-classifying an incoming document (EDMS).
Are these use cases running live today?
No, they aren't turned on as standard in individual products today — these are realistic examples our data model can support today. We determine together which one is the priority for you.
How we build an AI feature
Assessment
We assess together whether your data model is AI-ready and which feature carries real value.
Discovery
We clarify the right model/provider, data-privacy constraints and the integration point.
Development
We build the AI adapter without touching the core and wire it end to end with your real data.
Testing
We validate output accuracy, edge cases and the cost/performance balance under real usage.
The technology we build on
- Provider API
- RAG
- Embeddings
- Similarity search
- .NET 9
- PostgreSQL
- Next.js
- Flutter
Let's find together which feature carries real value
We'll listen to your data model and priorities and map out where to start, together. The first conversation is non-binding.