Enterprise services

Advisory and delivery for regulated organizations.

We take on the work where the security review is the hard part, and where the team has to run it without us afterwards.

Three ways we engage

Enterprise

Advisory and delivery for financial services, family offices, and other regulated organizations where policy and audit are first-class requirements rather than an afterthought. The sector detail is on AI integration for financial services.

Developers

API-first and cloud-neutral delivery, documented for the people who inherit it. Web, iOS, Android, desktop, and voice, with wearables on the roadmap.

Heritage

Cloud architecture, business process management, service-oriented architecture, and integration. The practice this company was built on in 2011.

Engagement model

How an engagement runs

Four stages. The point of the sequence is that the expensive commitments come last, and the honest answer comes first.

1Briefing We want the process, the systems it touches, the policy that governs it, and the name of whoever has to sign off. Not a demo. What comes out is a written view of whether the work is worth doing, roughly what it would take, and whether we are the right team for it.

2Compliance review Before anything is built. Data classification, which providers may see what, where the audit record goes, and which actions require a human confirmation. In a regulated organization this is the stage that determines whether the project ships at all, so it happens early rather than as a gate discovered in month five.

3Pilot A defined cohort against a success rubric agreed in advance, in writing. A pilot without a pass mark is a demo with a longer runtime, and it is the single most common reason an AI programme consumes a year and produces a slide.

4Rollout and handover Programmatic rollout, then documentation, runbooks, and the training to operate it without us. Handover is a deliverable with an acceptance test, not a phase that gets compressed when the timeline slips.

What the first conversation actually produces

A written answer to four questions, and it is deliberately short.

Is this an AI problem? Often it is an integration problem, a data-quality problem, or a process problem with an AI-shaped budget attached. We would rather tell you in week one that the answer involves less AI than you expected than bill you for six months of finding that out.
What would it take? Shape, sequence, and the dependencies that sit outside our control, which are usually the ones that decide the timeline.
What has to be true before it can ship? The policy, security, and audit preconditions, named early enough to be worked in parallel rather than sequentially.
Are we the right team? Sometimes not, and saying so is cheaper for both of us than a discovery phase that arrives at the same place.

What we will not do

We do not take work that leaves a client dependent on us to operate it, and we do not ship an agent that can take a consequential action without a human confirmation and an audit record. Both positions cost us work occasionally. Both are why the product side of this company is credible.

The longer list, including why we will not describe country-based model routing as a data-residency guarantee, is on the financial services page.

Where to read further

See the AI practice and its reference architecture for what these engagements deliver against, how we work as a Massachusetts company for the entity and location questions, and why AI pilots stall at the security review if stage 2 above is the one you have already hit.

Start with the briefing.

Bring the system nobody wants to touch, the process that still runs on email, or the pilot that never got past the security review. Stage one costs you a conversation and produces a written answer, including the answer that says do not do this.