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AI in a Box
Small team.
Big move.
Practical AI for smaller businesses competing against bigger teams.
An everyday enquiry
“Can you quote by Friday?”
Wednesday
A buyer asks for a quote. Sales knows the product; the latest price sits in a separate file.
Thursday
There's an exception. The one person who knows the answer is out seeing a customer.
Friday
The buyer chases. Someone finally replies. Nobody owns the follow-up.
The risk isn't a late quote. It's giving the buyer a reason to call someone else.
With AI in a Box
Get the answer out.
Keep the sale moving.
Draft it
AI assembles a draft from approved product and pricing information. It flags the exception for Sales, who checks the price and sends the quote.
Follow it up
Each morning, the account owner sees which quotes are waiting, what's missing and a suggested next action. They decide who to contact.
We use the systems your team already works in wherever they can do the job.
The business case
Does it pay its way?
Illustrative figures. Ten hours only matter if the team can use them. We measure reply time, rework and overdue quotes before and after — and count software and support costs.
Why Good Mischief
Small, on purpose.
You talk to the person doing the work, from the first session to after launch. No account manager relaying messages, no pitch team you never see again.
- No junior bench learning on your budget.
- No discovery phase padded to fill a quarter.
- No dashboard you didn't ask for.
The person who scopes your job is the person who builds it — and stays for the result. Big-company delivery discipline without the big-company overhead.
What we have actually built
Proof, not slides.
Between us we have spent a couple of decades building the unglamorous systems that big organisations quietly depend on, and a good few years putting AI into production where people actually use it. The work below is ours. We just think smaller businesses deserve it too.
Cloud that holds up under load
Pricing and risk platforms for organisations like Fitch Ratings and Bupa Global, including compute grids running across a hundred nodes. The sort of architecture nobody notices until it falls over, which is rather the point.
Quoting systems that move the numbers
Quoting engines and operational platforms that lifted conversion by 300%, cut back-office queries by 80% and supported a £100m asset business at JPS Avail. Less chasing, more selling.
AI in daily use, not in a demo
Agentic workflows and language processing used by more than 20,000 people, including work with Big 5 publishers through Prismatext. Built with humans checking the bits that matter, because sooner or later the model will be confidently wrong.
What you buy
A small first commitment.
- 01
Decide whether it's worth doing.
A paid working session on a real example. You leave with a baseline, a success measure, a fixed build quote and expected running costs. You can stop here.
- 02
Put two connected jobs to work.
We aim for three weeks once data, access and an owner are in place — agreeing who sees what, who approves outputs and what happens when AI gets it wrong.
- 03
Take it over.
Your team runs a case, catches a bad answer, updates a source and stops the workflow. You keep the accounts and instructions, with agreed support after launch.
Who we work with
Good tools, chosen on purpose.
We are not tied to one vendor and have no interest in selling you a platform. These are the partners and tools we work with because they do the job in production, which is a surprisingly short list once you get past the demos.
OpenELMs
Stack AI
Vesara AI
Workstuff.ai
Claude by Anthropic
Canva
Figma
Quilr
Iris by Ignition Data
Bring us the job that gets stuck.
Give us 30 minutes and one recent example — a delayed quote, a missed renewal, a report that eats Friday afternoon. We'll tell you what we'd try, how to measure it and whether it's worth the money.
Book 30 minutes →