A decision repeats
People repeatedly review, classify or search information using identifiable criteria.
APPLIED AI · PUBLIC, PRIVATE OR HYBRID
AI on your data. With control in your hands.
We apply AI to specific processes and choose the right architecture —public, private or hybrid— based on the data, risk and integration required. We validate utility and limits before scaling.
- Specific use case
- Data and boundaries
- Validate before scaling
When it is worthwhile
When AI improves a measurable decision or task.
We start from the process and its operating cost, not from a technology looking for somewhere to fit.
Useful information exists, even if scattered
Documents, systems or internal knowledge can provide context with suitable permissions and traceability.
The task supports gradual assistance
AI can enter a bounded part of the workflow without automating it end to end.
Privacy and control matter
The required level of control determines what data may leave your environment and whether a public, private or hybrid solution is appropriate.
Problems we address
Four areas where AI can provide operational utility.
We do not offer an endless use-case catalogue. We bound a task, its users and the evidence required to know whether it works.
Domain 01 / 04
Knowledge and search
Contextual access to documentation and internal sources with references and permissions.
Input"What renewal terms are in the contract with vendor X?"
SourcesContracts · Annexes · Sales email
ResultAn answer referencing the exact clauses and their current version.
Domain 02 / 04
Classification and extraction
Assisted reading of documents, messages or records to structure relevant information.
InputScanned invoice (PDF)
DetectedVendor · Amount · Date · VAT · PO number
ResultA structured record, ready for the ERP.
Domain 03 / 04
Process assistants
Support for tasks with tools, rules and clearly defined decision points.
InputNew customer onboarding request
VerifiedTax data · Risk · Duplicates
ResultA prepared case and a draft ready for human review.
Domain 04 / 04
Supervised automation
Execution of repetitive steps while retaining human review where risk requires it.
InputIncoming incident queue
TriageCategory · Urgency · Owning team
ResultA proposed assignment; risky cases are routed to review.
Domain 01 / 04
Knowledge and search
Contextual access to documentation and internal sources with references and permissions.
Domain 02 / 04
Classification and extraction
Assisted reading of documents, messages or records to structure relevant information.
Domain 03 / 04
Process assistants
Support for tasks with tools, rules and clearly defined decision points.
Domain 04 / 04
Supervised automation
Execution of repetitive steps while retaining human review where risk requires it.
Feasibility before scale
Validate utility, privacy and integration in one path.
A useful PoC is not an isolated demo: it should test behaviour with realistic context and reveal the limits that shape production.
Case and success criteria
Define which task improves and how sufficient assistance will be judged.
Data and context
Review availability, quality, permissions and required preparation.
Privacy and risk
We determine what data can use external services, what must remain controlled and which architecture is proportionate.
Integration and oversight
Place the capability within the process, with inputs, outputs and human validation.
PoC and evidence
Test the bounded case, document limits and decide whether deployment is justified.
Define which task improves and how sufficient assistance will be judged.
Review availability, quality, permissions and required preparation.
We determine what data can use external services, what must remain controlled and which architecture is proportionate.
Place the capability within the process, with inputs, outputs and human validation.
Test the bounded case, document limits and decide whether deployment is justified.
The architecture —public, private or hybrid— follows data sensitivity, risk, performance, operations and cost.
What the client receives
Evidence for a decision and a technical route if the case works.
Delivery clearly separates what has been validated, what remains open and the conditions required to operate.
Use case and success criteria
Data and integration architecture
Functional proof of concept
Risks, limits and oversight
Deployment and evolution plan
Frequently asked questions
Before applying AI.
Do you only work with private AI?
No. We choose between public services, private deployments and hybrid architectures based on the data, risk, cost and integration required.
What exactly does private AI mean?
It means designing data handling, access and deployment to keep data and operations under the level of control the use case requires.
Can you guarantee accuracy?
No. We define evaluation criteria, measure behaviour in a bounded case and design limits or oversight to manage possible errors.
Must our data be perfectly prepared?
No, but its sources, permissions and quality must be understood. Discovery identifies the preparation needed before validation.
Can a PoC move directly to production?
That should not be assumed. Production requires integration, security, observability, cost, performance, oversight and operations beyond the functional test.
START WITH THE PROBLEM
If you have a specific process, assess whether AI merits the investment.
Tell us the task, available data and what outcome would create value. We will begin by bounding the case and its success conditions.
No automation promises before understanding the process.