Custom AI Agents
An AI agent is only useful if it knows your business: your products, your policies, your systems and the point at which a person needs to step in. We design and build agents that work inside those limits, connect to the tools you already run, and leave a clear record of what they did and why.
Who it is for
This service fits teams that already know where the repetitive questions and lookups are, and want an agent that takes on that work without becoming a black box.
- Support and customer-success teams answering the same questions across email, chat and helpdesk tickets.
- Operations, finance or HR teams whose people spend hours searching documents, policies and spreadsheets for answers.
- Agencies that want to offer agent-based products to their own clients without building an AI team.
- Product teams that want an assistant inside their own software and need it designed properly from the start.
Problems it solves
- Customers wait hours for answers that already exist in your help centre or order system.
- Staff ask the same internal questions every week, and the answers live in five different places.
- Generic chatbots give confident wrong answers because they have no access to your real data.
- Previous AI pilots worked in a demo but had no escalation path, no logging and no owner.
- Knowledge leaves with the people who have it, because nothing captures how decisions were made.
What we build
- Support agent with human escalation
- An agent that answers customers from your knowledge base and live systems, such as order status or account details, and hands the conversation to a person with full context when confidence is low, the topic is sensitive or the customer asks for it.
- Internal assistant over company data
- A retrieval-augmented assistant (RAG) that searches your documents, wikis, tickets and databases, answers with sources, and respects the access permissions people already have.
- Multi-agent systems
- A coordinating agent that delegates to specialised sub-agents for research, data analysis, drafting or checking, with shared memory so work carries over between sessions instead of starting from zero.
- Guardrails, evaluation and logging
- Test sets built from your real questions, rules for what the agent may and may not do, and a log of every action so you can review, audit and improve it over time.
How an engagement runs
The same five phases as every project, adapted to this service.How we work
Discovery
We map the conversations or tasks the agent will take on, the systems it needs and where a person must stay in the loop.
Prototype on real data
Within the first weeks you get a working agent tested against your own historical questions, not a slide deck.
Build and integrate
We connect the agent to your channels and systems, add escalation and permissions, and write the evaluation suite.
Launch in stages
The agent starts with a limited audience or in draft-only mode, and widens its scope as the results hold up.
Support and improve
We review logs and failures with your team, update knowledge and prompts, and add capabilities as needed.
Good to know
You keep control
Every agent has an off switch, a defined scope and a named owner on your side. Nothing is sent to a customer that the rules do not allow.
Model choice is yours
We build agents so the underlying model provider can be changed, and we recommend the provider that fits your data and compliance needs.
Related use cases
Have a workload that an agent could take on?
Tell us what the team spends its time answering or looking up. We will tell you honestly whether an agent is the right answer.
Start a conversation