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Services pages are usually four boxes with a few bullet points under each, and none of it tells you which box actually solves your problem. Forte Group has been at this for 25-plus years, mostly serving clients in North America. We're now bringing that same track record into the UK, which is part of why I took this role. Here's why the work is split into four practices, and it's a more useful explanation than the list itself.
The base of what we do is still software development: architecture, AI-enabled development pods, quality assurance, SRE and platform work, user experience, product management, cloud optimisation. It comes first because every AI or commerce project that's actually worked was built on production-grade engineering from day one, not a proof of concept meant to be rebuilt later. Skip that layer and whatever gets built on top inherits whatever was already broken underneath, just faster.
The AI practice covers three distinct things: AI-enabled products, AI-powered engineering, and AI-driven operations. They're not the same skillset wearing different labels. Building an AI feature into a client's product is a different job from using AI to speed up our own delivery, which is different again from applying AI to a client's back-office operations. Xceptor shows what happens when all three run properly, in the right order: 80% of their engineering team, 39 of 49 people, using AI daily within six months, story rework down from 30% to under 10%, and 170-plus SaaS instances now monitored autonomously, with exception handling that used to take days now taking hours. Their own SVP Engineering presented those numbers publicly, which carries more weight than a case study we wrote ourselves.
Data and analytics is its own practice rather than a subset of the AI one, because in most engagements the actual blocker isn't the model, it's the layer underneath it: strategy, architecture, engineering, modernisation, visualisation, migrations. A request for an AI feature before the underlying data is structured is usually the wrong first project, and we say so, even when it's not the answer someone came in expecting.
Intelligent Commerce, where ForteNext lives, is the newest of the four in how we talk about it, though not in how long we've been doing it: revenue cycle architecture, Salesforce integration, CPQ engineering, RevOps enablement, AI-augmented selling, custom revenue apps, and data and revenue intelligence built on the same foundation. It tends to get treated as an add-on by software vendors and as the whole point by Salesforce consultancies, and neither framing holds up well in practice. Revenue architecture and product engineering are the same discipline applied to different parts of the business. Split across two vendor relationships, the usual result is a well-built product sitting on top of a broken quote-to-cash process.
None of this matters if there's no way to start working with us without a twelve-month commitment, so how you engage splits into three models. Scaled Engineering is senior talent added to your existing team, sized to the problem, delivered from teams across the US, Europe and Latin America with genuine working-hours overlap into the UK and EMEA, not a handoff to a shift that happens to be cheaper. AI Innovation is the accelerator model, aimed at products, operations or commerce, for a team that knows roughly what it wants to build but not how to get there fast. Managed Services is full ownership of an engineering or product outcome, not just added headcount, for a client who wants a result handed over rather than a team to manage.
Which one fits depends on whether the actual gap is capacity, direction, or the bandwidth to manage the work at all. Most vendors only really offer one of the three, so that tends to be what gets sold regardless of which one you needed.
Every engagement begins the same way, whichever of the three models it turns into: map the highest-friction workflows, capture a baseline before anything gets built, and use that to reach an actual go or no-go decision rather than a sales deck dressed up as a discovery report. From there, whatever gets built runs on production architecture from the start and gets measured against that baseline, and only gets rolled out to other teams once it's proven.
The commercial terms are built to match. Month-to-month, straightforward time and materials, no long-term commitment required, ramping up or down as the plan changes. If we don't deliver on what we said we would, the client doesn't pay for the gap.
Every engagement runs on three people, not one relationship manager covering all three jobs. A Solution Architect owns technical governance and makes sure what gets built is the right architecture, not just a working one. A Delivery Partner owns end-to-end accountability for the outcome: whether it shipped and whether it worked. A Client Partner handles the commercial side and the relationship, so a technical disagreement doesn't get tangled up with a contract renewal. Splitting those three roles keeps each one honest about what they're actually accountable for.
If you're trying to work out where a specific problem fits: if the blocker is data, start there regardless of what was originally asked for. If it's engineering capacity, that's Scaled Engineering, not a managed service dressed up to look bigger. If the goal is a result without managing a team to get it, it's worth saying that upfront rather than defaulting to a staff-aug conversation because it's the easiest one to have.
If any of this maps to something you're actually dealing with, the first step is a Discovery Workshop: we map the workflows, identify the highest-impact opportunity, and define what success looks like before anything gets built.et in touch with Forte Group or with me directly, and we'll set it up.
83% faster delivery. Under 10% rework. See exactly how Xceptor got there.
