AI implementation, not AI experimentation
Most AI projects fail not because the technology doesn't work, but because it was applied to a step nobody needed help with, in a place nobody was working. We implement into the process as it actually runs.
The demo is easy. The implementation is the work.
A model that performs well on a clean example says very little about whether it will hold up on your actual documents, your actual exceptions and your actual volume.
Impressive on prepared data. Lives in a separate tool. Requires someone to change how they work in order to use it. Six months later the conclusion is that AI didn't work here.
Built around the real process, on real data with its real mess. Sits inside the tools the team already uses, so the improved way of working is also the easiest one. Measured against what it was supposed to fix.
How we implement
Five steps, in this order. The order matters more than the steps — each one depends on the answer to the one before it.
- 01
Understand
The process, the data and the outcome.
What the work actually involves today, what information it depends on, where that information lives, and what a better result would look like. We start from the business process, not from the model.
- 02
Structure
Turn documents and knowledge into structured intelligence.
Most business information is unstructured — documents, spreadsheets, email, things people know. Before AI can work with it reliably, it has to become structured, typed and traceable. This step is usually where the real work is.
- 03
Build
Design and implement the system.
The workflows, models, interfaces and checks that do the work. Built to the shape of the process rather than the other way round, and no more complex than the problem requires.
- 04
Integrate
Connect it to the tools already in use.
A system that lives beside the business gets ignored. We put it inside the tools your team already works in, so the improved way of working is also the path of least resistance.
- 05
Improve
Measure accuracy, reliability and business impact.
Whether the output is correct, whether it holds up on real data, and whether the process is actually better. If a step is not carrying its weight, it changes or it goes.
Implementation examples
Systems we have built into existing business processes. Each began as a specific operational problem rather than a decision to use AI.
Quotation Maker
Turning scattered product, pricing and specification information into a consistent quotation, without retyping it every time.
- Document generation
- Structured data
- Sales workflow
Tender Watchdog
Continuous monitoring of tender sources, so relevant opportunities surface instead of being discovered too late.
- Monitoring
- Document understanding
- Alerting
Tender Filtering
Separating the few tenders worth reading in full from the many that are not.
- Classification
- Relevance ranking
- Decision support
OCR / Document Intelligence
Turning documents into business data — not just readable text, but structured information a process can use.
- OCR
- Document understanding
- Data extraction
What makes an implementation trustworthy
A business process can only run on an AI output if that output can be checked. These properties are designed in, not added afterwards.
Source grounding
Outputs are derived from your data and documents, not from what a model recalls about the world.
Traceability
Every extracted value keeps a reference to where it came from, so any figure can be checked against its source.
Validation
Results are checked against the rules, requirements or constraints they are supposed to satisfy.
Structured outputs
Typed fields rather than free text, so downstream systems can consume them and errors surface as errors.
Data quality
Messy, inconsistent and incomplete inputs are the normal case. Systems are built expecting that, not assuming clean data.
Human review
Anything ambiguous is flagged for a person rather than guessed at. Judgement and approval stay with your team.
Build with Prakreon.
Start with one process that takes up your team’s time. A free 30-minute call is enough to tell whether it is worth measuring.