AI Implementation
Helping organisations start their AI journey, build the architecture underneath it, and ship AI agents that are actually efficient and usable — not a demo that never reaches production.
This is early — there's no fixed pricing yet the way there is for Wero Ready. What's below is the delivery model itself: how the work actually gets structured. Get in touch if it's a conversation worth having now.
Why most AI initiatives stall
Most organisations don't fail at AI because the technology doesn't work — they stall because a working demo and a dependable production system are two different problems. The gap between them is architecture, evaluation, and integration work that rarely gets budgeted for up front. A delivery model exists to close that gap deliberately, instead of discovering it after the pilot has already burned its credibility.
Five phases, in order
Each phase depends on the one before it — architecture without a clear starting point drifts, and agents without solid architecture become unmaintainable fast.
Figuring out where AI actually fits, before any code gets written.
- Audit of existing data, systems, and workflows
- Use-case shortlist, ranked by feasibility and actual leverage — not novelty
- A go/no-go recommendation before anything gets built
The infrastructure and integration decisions that determine whether a system is maintainable in a year, not just impressive in a demo.
- Build-vs-buy and model selection — hosted vs. self-hosted, general vs. fine-tuned
- Data and context layer: retrieval, grounding, access control
- Integration points with the systems already in place
- Evaluation approach defined before development starts, not after
A scoped pilot against real data, measured before anyone commits further budget.
- Built against real data, not a synthetic demo
- Measured against the success criteria set in phase 1
- A clear decision point before wider investment
Agents built to do a specific job well inside existing workflows — not a bolted-on chatbot.
- Guardrails, monitoring, and human-in-the-loop escalation where it matters
- Cost and latency tuned for actual usage, not a best-case demo
- Built into the tools the team already uses, not a parallel platform
What keeps a system trustworthy after launch, not just at launch.
- EU AI Act risk classification and compliance, handled from day one rather than retrofitted
- Ongoing evaluation as usage and models change — see our agent testing methodology
- A path to the next use case, not a one-off project
Keeping agents reliable while everything under them changes
Models, embeddings, vector databases, prompts and knowledge bases keep moving after launch. We set up evaluation so that each of those changes is validated by tests that already exist, not by a new testing project every time. It works with hosted or open-weight models, regardless of the agent framework.
Test cases as data
Retrieval, behaviour, safety and cost cases written once, as outcomes and facts rather than wording, so they survive model and pipeline changes.
Component contracts
One reusable contract per swappable part (model, embeddings, vector database, AI gateway, judge), so a replacement is accepted on evidence.
A regression gate
Every change is compared with a recorded baseline in CI. It ships when it is no worse than what runs today, and the report says why.
Guardrails at the gateway
Compliance rules in your AI gateway, editable without a redeploy, enforced by the agent itself and verified by the test suite.
Human review where it matters
Review and intervention points in the agent, with every edit re-checked against the same rules and logged.
Change procedures
Written steps for model upgrades, re-indexing, prompt and rule changes: what to run, how to decide, and how to roll forward.
The method is documented on our agent testing methodology page. What it caught when we pointed it at a real model is in the blog post.
Four principles
Grounded in your data, not a black box — every answer traceable to a source.
Human-in-the-loop where the cost of being wrong is high.
Built for the systems you already run, not a parallel platform.
Evaluated before it scales, not after it breaks.