Custom GPTs & assistants
Trained on your documents, tone and process, so answers sound like your business rather than generic filler — and cite where they came from.
Agents, assistants and automation trained on your operation — not a chatbot bolted onto a website.
Most AI projects fail in the same place. Not the model — the plumbing. The assistant is impressive in a demo, then meets real data, real edge cases and real staff who do not have time to fight it, and quietly stops being used.
We start from the opposite end: find a task that genuinely eats hours, understand how it is really done today (including the shortcuts nobody documented), and automate that. Narrow and reliable beats broad and impressive.
We will also tell you when AI is the wrong tool. A lot of what gets pitched as an AI problem is a spreadsheet that needed fixing, or a process that needed one decision made. That advice is free, and we give it often.
Narrow and reliable beats broad and impressive. Every time.
Built around a task you can name and an outcome you can measure.
Trained on your documents, tone and process, so answers sound like your business rather than generic filler — and cite where they came from.
The repetitive middle of a process: triage, routing, summarising, drafting, data entry. The parts that are dull, high-volume and error-prone.
Connecting a model to your actual knowledge — policies, tickets, product information — so it answers from your material instead of guessing.
Checks on whether the thing is actually right, plus limits on what it is allowed to do. This is the part most projects skip and later regret.
Teams with a repetitive, high-volume task and the data to support it. If the goal is mainly to be able to say you use AI, we are probably not the right studio — and that is a genuinely fine reason to go elsewhere.
The pattern is always the same: a repetitive task, a lot of it, and a person who would rather be doing something else.
“We answer the same forty questions every week.”
Support, HR or sales fielding the same queries endlessly. This is the clearest win available — the answers already exist in your documents, they are just not reachable at the moment someone needs them.
“Someone reads every submission and sorts it.”
Triage: applications, tickets, enquiries, invoices. High volume, low judgement for most of it, with a small number of genuinely tricky cases that should still reach a person. Automate the routine and route the rest.
“We tried a chatbot and turned it off.”
Almost always because it answered confidently and wrongly. The fix is grounding it in your actual material and making it cite sources — plus letting it say it does not know, which is the feature most implementations skip.
This field is full of overclaiming. These are the limits as we see them.
It can, and pretending otherwise would be dishonest. What reduces it to an acceptable level is grounding answers in your own documents, requiring citations, and keeping a person in the loop wherever being wrong would actually cost something. Where that is not achievable, we will tell you the task is not a good fit.
Not with the setups we build. This is a configuration and contract question, and it is one we settle explicitly at the start rather than leaving to a default setting.
For most of this, no. Retrieval-based assistants work from documents you already have. Genuine model training needs far more data and is rarely what a business actually needs — it is just what gets talked about.
We agree upfront what “working” means and how it is measured, so that question has an answer other than opinion. If it does not clear the bar, the honest outcome is to stop, and we would rather reach that in week three than month six.
Describe it in plain terms. If automation is not the answer, we will say so.
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