Feasibility assessment
Which of your processes genuinely lend themselves to automation and which are better left manual.
AI is useful where there is a lot of repetitive work with text or data: answering the same questions, extracting information from documents, classifying enquiries, drafting. These are tasks that consume hours a week and require no judgement.
We will be equally direct about the limits. Language models are confidently wrong — they invent plausible-sounding details. So we do not deploy them unchecked where an error is expensive: medical advice, legal interpretation, financial commitments. We design so a person verifies before the output reaches a customer.
We start from a specific task with a measurable effect, not from deploying AI for its own sake.
What it includes
Chatbot on your own data
An assistant that answers from your documentation and FAQs rather than general knowledge, and cites its source.
Document processing
Extracting data from invoices, contracts and forms into a structured shape, ready for your system.
Classification and routing
Automatic sorting of incoming enquiries by topic and priority, so they reach the right person.
Human review
Where an error is expensive, output passes through review. That is not a compromise — it is part of the design.
How it runs
01 — Task selection
We look for a specific process with measurable time or cost, rather than a general goal of "using AI".
02 — Pilot
A small working version on your real data, to see whether the effect is sufficient.
03 — Deployment
Connecting to your systems, access permissions and a procedure for checking output.
04 — Monitoring
Tracking quality over time. Models and data change, and the solution needs maintenance.
What We Do
01
AI chatbots
02
Process automation
03
Data analysis
04
Custom AI models
05
AI strategy consulting
Ideal For
·Tech companies
·Customer support
·Data-heavy operations
·Innovation leaders
Related Services
FAQ
In the projects we do — no. The repetitive part gets automated so time is freed for work that needs judgement. A chatbot answering the ten most common questions frees the team for the cases where the customer genuinely needs a person.
Settled before we start and put in writing. There is a difference between a solution that sends data to an external provider and one that runs in a closed environment. With sensitive data — health, financial, personal — we choose a GDPR-appropriate architecture and tell you clearly where every part of the information goes.
That is why we design with boundaries. The assistant answers only from your documentation and cites the source; when it has no basis for an answer it says it does not know and hands off to a person rather than inventing one. For tasks where an error is expensive, output never reaches the customer unchecked. Nobody can guarantee zero errors — what can be guaranteed is that an error has nowhere to escape uncontrolled.