Document processing
The system can identify document types and prepare relevant information.
AI solutions
AI where it genuinely helps.
AI is useful when it speeds up a specific task and its result can be reviewed. It is not the answer to every problem; a simple rule or conventional automation is often more dependable.
Let’s talk about your project01 / The situation
Plenty of text, little structured information
Reviewing documents, emails and long-form text can take significant time when the same details must be found or classified repeatedly.
AI can suggest, extract or summarise, but it is not infallible. We design the process so uncertain cases are visible and important decisions remain with people.
- Many similar documents
- Repeated data extraction
- Difficult internal knowledge search
- Manual classification
- Recurring long-text processing
02 / The solution
A focused AI function inside real work
We do not build a generic chatbot for everything. The task, source, expected output and review mechanism are defined together.
Structured extraction
Selected fields can become table or system-ready data.
Classification
Requests or documents can be suggested for predefined categories.
Internal knowledge search
Teams can search approved internal material using natural questions.
Text preparation
The system can prepare a draft for an authorised person to review and finalise.
Review gates
Uncertain or high-risk results are routed to a person automatically.
03 / Decision point
Not every task needs AI
We choose the simplest dependable solution.
AI may be justified when
- input is text or variably structured documents
- the same task occurs across many examples
- the output can be checked
- mistakes can be handled safely in the process
A simple rule is better when
- the decision can be described through exact conditions
- every output must be deterministic
- suitable data or lawful use is unavailable
- a mistake is unacceptable without mandatory human review
04 / How we work
Start with a small measurable task
We evaluate quality against real examples and acceptance criteria, not broad claims.
- 01
Task and risk
We define the input, expected result and consequence of an incorrect answer.
- 02
Test set
We assess quality and exceptions using fictional or lawfully usable samples.
- 03
Human review
We define what the system may accept and where approval is mandatory.
- 04
Integration and monitoring
The AI function becomes part of the workflow with visible errors and adjustable rules.
Connections
What can it work with?
AI is usually part of a document, automation or business system rather than an isolated tool.
- document store and upload
- internal business system
- approval process
- searchable knowledge base
- email and client requests
- reporting and data review
Complexity
What affects an AI solution?
Third-party AI usage charges may be separate from development cost.
- data volume and quality
- languages and document types
- required accuracy
- extent of human review
- privacy and confidentiality
- external model usage charges
Delivery and operation
AI inside a reviewable system
The function can be delivered as PACK and maintained through CARE or CLOUD. Model, data and provider limits are documented for the project.
Contact
Is there a text-heavy task that consumes time every week?
A small set of fictional samples can help determine whether AI, conventional automation or a combination is the right direction.
Let’s talk about your projectNot sure which approach fits? Start with Discovery.