Copilot, Claude and ChatGPT agents built around your use cases
We build AI agents on Microsoft Copilot, Claude and ChatGPT, the tools your business already uses.
We start with a specific use case, build an agent to handle it, keep people reviewing the output, and train your team to use it.
Tell us the task you have in mind. Your free call is with Qasim Ahmed, our founder. For UK organisations, not individuals.
Trusted by
Teams we've worked with to build practical AI capability.
Day Seven has delivered AI enablement work for GXO, SharkNinja, Velociti, Studiospace, Archer Hotel Capital and Yorkshire Cancer Research.
This is company-level experience, not a claim that each organisation bought the particular service described on this page.
What is an AI agent, for a business?
Short answer: an AI agent is an AI system set up to carry out a defined task, such as drafting, sorting, looking up or routing information, with clear limits and a person responsible for the result. It is not a replacement for a team. It is a narrow tool for one job, built on a platform you already pay for, that people check and improve over time.
Starting point: agents work best when people already use the platform well. See Microsoft Copilot training, Claude training and ChatGPT training.
Chooser
Custom GPT, Copilot or Claude agent, or a bespoke build?
Not sure which platform fits? We usually build on the one your business already pays for. Use this as a first pass, then we confirm it on the call.
| If this sounds like you | Custom GPT in ChatGPT | Copilot or Claude agent | Bespoke build |
|---|---|---|---|
| Where your team works | Teams already on ChatGPT | Teams on Microsoft 365 (Copilot) or on Claude | Inside your own product, website or systems |
| The task | One defined task with clear instructions, approved knowledge and limits | A defined task inside the tools people already use, such as drafting, reviewing, summarising or looking things up | Something the off-the-shelf platforms cannot do, such as forecasting, image checks or document extraction at volume |
| Who uses it | Your own staff | Your own staff | Staff, customers or both |
| What we do | Build it, set the limits, train the team | Build it, set the limits, train the team | Scoped as a separate project with milestones, see custom AI development |
Platforms
The three platforms we build agents on
Copilot agents for Microsoft 365 teams
If your business runs on Microsoft 365, we build Copilot agents, including agents in Copilot Studio, around a specific task, so they work inside the tools your people already use.
Good fits are tasks where the answer already lives in your Microsoft 365 content and people spend time finding or reshaping it.
Starting point: Microsoft Copilot training and the Copilot rollout guide.
Claude agents for specific workflows
We build Claude agents for defined tasks such as drafting, reviewing and summarising, set up with clear instructions and approved material.
Good fits are writing-heavy tasks with a clear standard, such as reviewing a document against a checklist or drafting a first version in your house style, where a person still makes the final call.
Starting point: Claude training and the Mastering Claude workshop.
ChatGPT agents and custom GPTs
For teams on ChatGPT, we build custom GPTs and agents around a task, with clear instructions, approved knowledge and limits on what they should do.
Good fits are repeatable tasks where everyone should get the same quality of answer, such as a first-line answer from approved guidance, or a structured draft from a short brief.
Starting point: ChatGPT training.
How we build
From one defined task to an agent your team trusts
Every agent follows the same five steps. Milestones are agreed with you during scoping.
Text version of this diagram
- Define: projects start with a defined business problem and the data available to support it.
- Scope: milestones, review checkpoints and integration needs are scoped with your team.
- Build: the agent is set up with clear instructions, approved material and limits on what it should do.
- Validate: we build in validation and human review, because AI outputs can be wrong.
- Train and improve: we train the people who will work with it. What reviewers find feeds the next version.
Before you start
What makes a good first task for an agent
The best first agent does one narrow job that people already do often, by hand, to a standard you can describe. If you cannot say what a good result looks like, an agent will not be able to either.
- It happens often. A weekly or daily task is worth automating. A once-a-year task usually is not.
- The source material exists. The answer lives in documents, guidance or data you already hold and are allowed to use.
- A person can check it quickly. Reviewing the agent's output takes less effort than doing the task from scratch.
- A mistake can be caught. Start where an error is spotted before it reaches a customer, not after.
If you have several candidates and need to choose, AI strategy support helps leadership teams prioritise where AI should help first.
Data safety and wrong answers
What happens when an agent gets it wrong?
AI outputs can be wrong, and they can sound confident when they are. So we design for it rather than hoping it will not happen.
- Approved material only. The agent answers from material you have approved, not from whatever it can find.
- Clear limits. Instructions say what it should not do, and when it should hand over.
- Uncertain cases go to a person. When the agent cannot answer from approved guidance, the question is routed to staff.
- A person responsible for the result. Someone named reviews the output, especially for anything customer-facing, legal, HR or financial.
- Data stays within your rules. The agent sits inside your AI policy: approved tools only, and no data that the policy keeps out.
If you do not have those rules yet, start with AI governance and AI policy support.
Illustrative use cases
What an agent can take off your team's plate
These examples show possible applications, not client projects or results.
Document triage
Internal knowledge assistant
Customer questions
Adoption
We build it and train your team
An agent only helps if people trust it and use it. We train the people who will work with it: what it is for, what it is not for, how to check what it produces, and what to do when it is wrong.
For wider habits across teams, see AI adoption support. To give a team a working prototype at pace, see AI hackathons.
Frequently asked questions
More from Day Seven
This is part of our AI consultancy. See also AI strategy, AI adoption, AI governance, AI training and the free AI Readiness Scorecard.