An honest comparison
The closest comparison on this list, and the hardest one.
These firms do genuinely similar work to a genuinely high standard, at much larger scale. The honest difference is engagement shape and who they are built to serve, not a claim that we do AI better than they do.
Substantial specialist organisations with deep analytics benches, mature delivery practices, industry accelerators and in several cases their own platform IP. Built primarily to serve large enterprises, often as a multi-year analytics partner across several business units.
Much smaller by design. One pod, one metric per engagement, outcome-linked pricing, and a system handed over for your team to run rather than an ongoing analytics partnership.
This comparison is not who has better data scientists. They have excellent ones. It is whether you want an enterprise analytics partner or a small senior team accountable to one number.
8 weeks
Typical engagement, first scoping call to handover.
One senior pod, scoped to a single metric and priced against it, ending in a running system with its evaluation harness and the documentation your team needs to own it.
Statisticians, ML researchers and domain specialists in numbers a small pod cannot match. For genuinely hard modelling problems that depth matters.
Pre-built components and reference solutions that can take real time out of a build. That is a genuine advantage, not marketing.
Long reference lists with large enterprises, and the delivery maturity that comes from operating at that size.
What you give up
Their model is oriented to large, long programmes. A single well-scoped system may not be the engagement they are set up to sell.
Where accelerators or products are involved, ask what you own at the end and what happens if you leave.
At scale, delivery is staffed across seniority levels. The same question applies as anywhere: who is actually on your team.
One system scoped to one metric is our normal shape of work, not an exception squeezed in around larger accounts.
No accelerator licences and no platform IP in the middle of your system. The code, the harness and the docs are yours.
The engineers who scope your system build and operate it.
What you give up
On specialist research-grade modelling, a large analytics bench beats a small pod. If that is your problem, they are the better call.
We start from open tooling. Where a mature accelerator genuinely fits your use case, it can be faster.
We are a younger firm with fewer enterprise logos behind us. That is a fair thing to weigh.
A direct conversation about the problem, the metric, and whether we are the right shape for it. If another option on this page fits you better, we will say so.
