Specialism
ChatGPT implementation consultant for teams
Your team has already picked ChatGPT. The open question is whether it becomes part of how work gets done or stays a browser tab a few people open. A ChatGPT implementation consultant settles the workspace plan, decides which of your systems it may reach, builds custom GPTs for the jobs that repeat, and trains staff on their real tasks, all inside your own account and handed over documented.
The work
What does a ChatGPT implementation cover?
Five pieces of work, in order: moving the team onto a company workspace plan, connecting it to the systems you already run, building custom GPTs and shared projects for recurring jobs, training staff on their own tasks, and naming an owner for all of it afterwards. Seats without those five usually stall.
The plan, and the workspace it lives in. ChatGPT is sold as personal subscriptions, as shared workspace plans for companies (a business tier and Enterprise), and as an API for software you build. The common failure is a business running on a dozen personal logins: no central admin, no control over what gets connected, and chats, files and GPTs that leave with whoever leaves. We move you onto the plan that matches your identity, access and audit needs, and set up roles so a named owner, not one enthusiastic employee, administers it.
Connections to the tools you already run. ChatGPT can read from many common file stores and business tools through built-in connections, and on a workspace plan an administrator decides which ones members may switch on. The design question is not whether to connect everything; it is which connections your team needs, with whose permissions, and which should stay off because the data behind them is not something staff should be pasting into any assistant.
Custom GPTs for the work that repeats. A custom GPT is a configured version of ChatGPT with its own standing instructions, reference files and, optionally, actions — calls to another system's API, so the GPT can look something up or file a record. Proposal drafting, policy questions, call-note summaries, first-pass contract review: we pick the few jobs where the payoff is clearest, build a GPT or shared project for each, and write down what a good result looks like so people can check it.
Training on your tasks, not generic prompting tips. Short, role-based sessions using the GPTs your people will actually open, plus a written playbook: what ChatGPT is good for here, what it must be checked on, and what never goes into it. That last list matters more than any prompt technique, and most rollouts skip it.
Ownership after we leave. Someone on your side owns the workspace, the list of enabled connections, the shared GPTs and the playbook, and reviews them on a schedule. Without that, a rollout drifts: connections get enabled ad hoc, GPTs go stale, and the next model update changes behaviour nobody is watching.
| Route | Suited to | What to settle first |
|---|---|---|
| Personal subscriptions | One person evaluating ChatGPT on their own work | Not a company deployment — no central admin, and personal terms apply |
| Business workspace plan | A team that needs shared GPTs, central billing and admin control | Who administers it, and which connections members may enable |
| Enterprise plan | Organisations with formal identity, access and audit requirements | Single sign-on, user provisioning and the controls your security review expects |
| Custom GPT with actions | A recurring task that needs to read or write another system | Which API it calls, under whose credentials, and what it may change |
| OpenAI API | A model built into your own software or an unattended process | That this is a build, not a rollout — it belongs with agent development |
Data handling and plan limits are design inputs, not small print. Which terms govern your content depends on the plan you are on, and the controls available to an administrator differ between plans. We work from OpenAI's own published material, including its enterprise privacy commitments (opens in new window) and its introduction to custom GPTs (opens in new window), and frame usage policy against the NIST AI Risk Management Framework (opens in new window), so your IT and legal reviewers check a setup that the vendor and a public standard both describe. Plan names and tiers change, so we confirm the current lineup with you on the call. We do not quote plan costs on this page; OpenAI sets those and changes them, and we would rather check them with you than guess.
Choosing
Is ChatGPT or Claude the right assistant for your team?
If you have already chosen ChatGPT, this page is for you; if you have not, the choice deserves its own conversation. Both are capable general assistants. What usually decides it is what you already license, how each connects to your systems, and the data terms your reviewers accept, not a benchmark score.
This service starts after the product decision. Teams that have settled on Anthropic's assistant instead should read Claude AI implementation consulting, which covers the same five pieces of work on that platform. If the real question is which assistant, or which processes, deserve the investment at all, platform-agnostic AI consulting is the better first conversation, and we will tell you honestly if a rollout is premature. Plenty of organisations run more than one assistant; the point is to choose each deliberately rather than inherit whatever staff signed up for on their own.
Where it fits
When should a business build a chatbot instead of rolling out ChatGPT?
When the people asking questions are customers rather than staff, or when answers must come only from your own approved documents with citations. A rollout puts ChatGPT in front of employees. A retrieval chatbot is software you own, grounded in your content, embedded on your site or helpdesk, and measured on answer accuracy.
That kind of build is RAG chatbot development: retrieval-augmented generation, meaning the system looks up the relevant passages in your documents first and answers only from them. Separately, when a job has nobody at the keyboard — an email arrives, a form is submitted, a record changes, and the work should finish without anyone typing a request — you want an automated process that calls a model, which is custom AI agent development, not more ChatGPT seats.
Picking up a rollout that stalled
Many requests are not greenfield. ChatGPT was bought, a few people use it daily, most logged in twice, and nobody is sure what has been connected to what or which GPTs are still in use. The review is the same each time: which plan and terms you are actually on, who administers the workspace, which connections are enabled and with whose permissions, which custom GPTs exist and who maintains them, and what staff were told about sensitive data. You get a plain answer on what to keep, what to switch off, and the two or three workflows worth rebuilding properly first.
Questions
Practical questions about plans, data and custom GPTs
Which ChatGPT plan should a business be on?
For most teams, a company workspace plan rather than a collection of personal subscriptions. A workspace puts every account under central administration, which is what lets you control connected apps, share custom GPTs internally and keep work when someone leaves. Whether the business tier or Enterprise fits depends on your identity and audit requirements, so we read OpenAI's current comparison with you.
Does OpenAI train its models on our business data?
OpenAI's published enterprise privacy commitments say content from its business workspace plans and the API is not used to train models by default. Personal accounts sit under different terms and settings, which is one more reason staff should work inside the company workspace rather than a personal login. Your own counsel should read the terms that apply to you.
What is a custom GPT, and do we need one?
A custom GPT is a configured version of ChatGPT with its own standing instructions, reference files and, optionally, actions that call another system. You need one for a task your team repeats often enough that starting from a blank prompt wastes time or produces inconsistent results. Most teams need a handful, not dozens.
Can ChatGPT connect to the tools we already use?
Often, yes. ChatGPT offers built-in connections to many common file stores and business tools, and on workspace plans an administrator decides which ones members may enable. Where no connection exists for an internal system, a custom GPT action or a custom connector is a separate build that we scope on its own.
Are you an OpenAI partner?
No, and we will not display a badge we have not earned. We are independent: we work from OpenAI's published documentation, inside your workspace, on your plan, and you keep everything we set up. If a vendor partner designation is a procurement requirement on your side, tell us on the first call and we will say plainly that we do not meet it.
Free consultation
Tell us what is costing you the most time
Describe the process. You get a straight read on whether it is worth automating, roughly what it would take, and what we would tackle first — on a call that costs nothing.
- Under an hour, free, and no obligation
- Fixed price per stage, agreed before any work starts
- You own everything we build, documented and in your accounts
- If you do not need us, we will say so on that call
The form is the fastest way to reach us — we reply from a real person, usually within one business day.