AI Implementation and Agents
AI agents built around your business, tested before they go live
Rhentech designs, builds and runs AI agents for London firms, starting with the work where AI pays back and finishing with an agent that is tested, logged and governed. The result is measurable time saved, without handing a model the keys to client data.
30 minutes, free of charge. We respond within one business day.
- Governance built inGuardrails, approvals and audit trails are designed before the first line of code.
- Measured qualityEvery release passes an evaluation harness, not a demo.
- Cloud or local modelsSensitive work can run on models you host, inside your own environment.
What does an AI implementation project include?
Eight workstreams, from the business case to ongoing management.
- 01
Use-case selection and business case
We start where AI can pay back: document-heavy work, enquiry triage, reporting and compliance checks. Each candidate is ranked by value, effort and risk, and only the strongest go forward.
- 02
Agent and workflow design
An AI agent is software that uses a language model to plan and carry out multi-step tasks with tools, such as searching documents, drafting replies or updating records, within limits you set. We design what it may do on its own and what needs a person to approve.
- 03
Integration with your systems
Agents are useful only when they reach your data. We connect them to Microsoft 365, document stores, practice and CRM systems through controlled, logged access with the least privilege the task needs.
- 04
Model selection, cloud or local
We choose models on capability, cost, data location and licence terms, not hype. That can mean managed models through Google Vertex AI, Azure or AWS, a routing layer such as OpenRouter for comparison, or open-weight models such as Qwen served locally with vLLM or Ollama on NVIDIA hardware.
- 05
Evaluation harness
An evaluation harness is a fixed set of test cases with expected results that every version of an agent must pass before release. It turns quality from opinion into a measured score and catches regressions when models change.
- 06
Security testing
Prompt injection is an attack in which instructions hidden in a document, email or web page make an AI system ignore its rules, and the OWASP Top 10 for LLM Applications 2025 ranks it as the first risk. We test agents against it, and against data leakage and excessive permissions, before go-live.
- 07
Human oversight and audit trail
Every agent action is logged with its inputs, sources and output, and decisions with real consequences go to a named person for approval. You can show a client or regulator exactly what the agent did and why.
- 08
Management and optimisation
After launch we monitor accuracy, cost and usage, update models as better ones appear, and extend what works. Management can run as a monthly retainer, on its own or alongside managed IT.
How does an AI implementation project run?
Part of the Turnkey AI loop: discover, build, manage, review, then repeat.
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Discovery
A working session with your leadership and the people who do the work today, followed by analysis of the process, the data and the risk. You get a ranked shortlist and a business case.
-
Implementation
We build in short iterations, each ending in a working agent tested against the evaluation harness. Progress is reported against each milestone, so you see value before the next stage starts.
-
Management and review
Monitoring, optimisation and quarterly review of value and risk. The loop then starts again with the next use case.
Platforms we work with
- OpenRouter
- Google Vertex AI
- Microsoft Azure
- AWS
- Ollama
- vLLM
- Qwen
- NVIDIA
- Runpod
What do leaders ask before building AI agents?
Not answered here? Ask it on the call.
What is an AI agent?
An AI agent is software that uses a language model to plan and carry out multi-step tasks with tools, such as searching documents, drafting replies or updating records, within limits you set. Unlike a chatbot, it acts as well as answers. That is why permissions, approvals and logging matter from the start.
Where does AI pay back first in a professional firm?
Usually in document-heavy work such as contract review, due diligence summaries and report drafting, in triage of enquiries and requests, and in routine compliance checks. AI is not yet suitable where zero error is required without human review, such as legal opinion or advice given to a client. We will say so when the right answer is to wait.
How do you stop an agent doing something it should not?
By limiting what it can reach, requiring human approval for consequential actions, testing it against prompt injection and data leakage, and logging everything it does. The evaluation harness re-runs every test whenever the agent or its model changes.
Where does our data go?
Where you decide. Agents can use managed models in your own cloud tenant, or open-weight models hosted on your hardware so prompts and documents never leave your environment. Each option is assessed against UK GDPR and your client confidentiality obligations before we build.
How is an implementation project priced?
Implementation is scoped as a project with milestones agreed in writing, after discovery has shown what is worth building. Ongoing management is a monthly retainer, and it can be combined with Rhentech managed IT and AI governance.
Free consultation
Put AI to work on the problems that cost you most.
A 30-minute call with a senior consultant about the processes you want to improve and what a first agent could do. We respond within one business day.
Or call 020 3886 3001 or message us on WhatsApp.
What happens next
- 01You tell us briefly what has landed on your desk.
- 02We respond within one business day to arrange a time.
- 03A 30-minute call with a senior consultant, and an honest view of what to do first.