Build · AI direction

“We know AI should be doing more of this, but we don't know what.”

Nearly everyone is here. The pressure to do something with AI is real, the shortlist of what to actually do is not, and nobody wants to spend six figures finding out.

What is actually going on

The reason it's hard to answer internally is that the good candidates don't look impressive. They're the repetitive, judgement-light, high-volume jobs somebody does every day: reconciling, re-keying, classifying, chasing, drafting the same message for the four hundredth time.

Meanwhile the ideas that get proposed in meetings tend to be the visible ones, a chatbot on the website or a copilot in the product, and those are usually the hardest to make reliable and the least valuable when they work.

So the real task isn't picking a model or a vendor. It's looking at where the hours actually go and finding the two or three places where a machine is genuinely better than the current arrangement.

What we do about it

A short, honest read on where it pays off in your operation, and then the build.

We look at where the hours go

A short read on your operation rather than a strategy document. What is repetitive, what is high volume, what is currently costing people their week.
You get a shortlist, with the boring parts included

What each one is worth, what it takes to build, and what it costs to run. Including the ones we think you should not do.
We build the first one properly

With evaluation and guardrails from the start, so you can tell whether it's working rather than whether it's impressive.
Then it gets operated

Models change underneath you constantly. Something nobody is watching degrades quietly, which is how most AI features quietly stop being used.

50+

Products shipped

5 years

Average client tenure

4.8

Rating on Clutch

$5,000

Retainers from, per month

Questions

What people ask next.

Do we need an AI strategy first?

No. Strategy documents about AI age in weeks. A short read on where the hours go, then building one thing that works, teaches you more than a roadmap will.

What if the honest answer is that AI does not help us much?

Then we say that. It happens, and it is a cheaper thing to find out from us than from a year of pilots.

Which models do you use?

Whichever is best for the job, and they get swapped as they change. Building against one vendor's specific behaviour is how you end up rewriting a feature when somebody else changes their pricing.

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