Seven verticals, seven tailored approaches, from finance to public sector. Concrete use cases, not generic AI slogans.
AI use cases vary from industry to industry, different priorities for a bank than for a retail business. We bring concrete, working use cases to every vertical, not generic trend numbers.
Fraud detection, KYC, compliance RAG
Contract review, research, redaction
Product copy, recommender agent, customer bot
Content pipeline, multi-channel, analytics
Tutor agent, curriculum adaptation, admin auto
On-prem RAG, citizen-facing assistant
Proposal automation, internal knowledge agent
Sector use cases differ, but the logic of deployment does not. Most projects fail not on the model but on the privilege model, and on never measuring what was replaced.
We start with where repetitive, measurable effort sits. One well-chosen first use case is worth more than five parallel experiments.
What data the system touches, and on whose behalf. This is the critical point of most projects: an all-seeing service account bypasses years of access management in a single step.
In a closed circle with measurable output. The aim is to decide whether the use case pays back before live deployment.
Logging, quality control, feedback. Models and processes both change, so operation is part of the project.
The technical solution is similar, but the centre of risk differs. This determines where local deployment is required and where a stricter contract suffices.
| Sector | Main risk | Typical answer |
|---|---|---|
| Finance and banking | Client data leaving, decisions untraceable | Local deployment, full audit logging |
| Legal | Breach of privilege | Local deployment, closed network |
| Public sector | Data sovereignty, transparency | Local deployment exclusively |
| Education | Data on minors, parental trust | Local deployment, restricted data scope |
| Marketing | Strategy and customer lists leaving | Own infrastructure for confidential workflows |
| Professional services | Client materials mixing | Engagement-level privilege model |
| Retail | Disclosing another customer's data | Context restriction, prompt injection defence |
Technically much is shared, but the centre of risk and the regulatory environment differ by sector. For a law firm, privilege makes local deployment a baseline requirement rather than an option; for a marketing team a public service may be acceptable where no customer data is involved. Use cases also differ, and that determines where fast payback lies.
The seven listed are where we have a developed use case set, but the methodology is general. In such cases the use case assessment runs longer, because we need to find together where repetitive, document or data-heavy work sits in the organisation. This typically starts with a workshop rather than a proposal.
Three questions decide. One: can data leave the organisation without legal or contractual obstacles? Two: must it work on a closed network without internet? Three: is full logging and auditability in your own hands a requirement? If the answer to any is yes, local deployment is needed. In the public sector and legal work this is effectively always the case.
Use case assessment takes 2-3 weeks, the prototype 4-8 weeks, live deployment depends on system complexity. For a well-bounded first use case, 3-4 months from assessment to production is realistic. This works because the decision to continue is taken at the end of the prototype phase, based on measured results.