Most AI consulting ends in a 120‑page deck and a PoC that never leaves the sandbox. Ours doesn't. We evaluate where AI saves you money or creates revenue, then we build it. One team from feasibility audit to production system, so the roadmap survives contact with engineering reality.
Pixelfield is for CTOs and Heads of Product at scaleups and mid‑market companies (50 to 500 people) who know AI is on the roadmap but lack the internal capacity to scope, architect and ship it. The seniors who pitch the work are the seniors who write the code. No bait and switch. No handoff to a separate delivery partner.
50+ AI features shipped to productionSame senior engineers, scope to codeDiscovery from £5K, fixed‑price phasesYou own all code, models and docs
AI is on the roadmap or the board's list of priorities, but no one internally can assess feasibility, estimate cost or decide which use case to fund first. You don't need another strategy deck. You need a technical team that will audit your data, price three realistic options and tell you which one to ship. That is what the first four weeks with us look like.
You paid a previous consultant for a roadmap. It's been on a shelf for six months because the team who wrote it doesn't build. We start from the deck, stress‑test the assumptions against production reality, and turn the surviving use case into a working system. Often the original scope shrinks. That is usually a good thing.
You've tried ChatGPT, Copilot, a vendor PoC or an internal prototype, and nothing has made it into production with real users. The gap between "technically validated" and "running in production" is where AI value gets lost. We cover that gap directly: integration, observability, governance, SLA and the operational layer most PoCs skip.
Your CFO has blocked out £150K to £500K for an AI programme and you want an external engineering view before the money moves. Sometimes we greenlight the scope. Sometimes we redirect it. Either way you get a technical second opinion grounded in what it would cost our own team to ship, not what a Big 4 practice would quote for a 12‑month engagement.
Stakeholder interviews with product, engineering, data and compliance. A hands‑on audit of your data (schemas, quality, lineage, access), your infrastructure (cloud footprint, identity layer, observability) and your regulatory environment. We talk to three to eight people, not fifty. We do not ask you to fill in a 200‑cell maturity spreadsheet.
You receive: a feasibility report, a use case map, a priority matrix, and a recommendation on which use case to scope next. Some clients stop here with a clear picture of what's possible. Most move into scoping.
For the top use case we design the architecture, pick the model and vendor, map the integration path, scope the build and price it. Everything is written so your internal team or another vendor could execute it. No dependency on us to read the document.
You receive: an architecture proposal, a week‑by‑week build plan, a fixed‑phase cost estimate, a risk register and an integration blueprint covering APIs, data contracts and security boundaries.
Design, engineering, testing and integration. Iterative, with a client review every one to two weeks. The same senior engineers who ran discovery write the production code, under an AI engineering lead (Michal Vavra). No handoff to a separate delivery team, no offshore pool, no junior churn.
You receive: a working AI system in staging, then production. Evaluation harness, observability, audit logging and documentation ship with the system, not after.
Deployment, monitoring setup, documentation and a handover session with your team. Post‑launch, you can take the system in‑house or keep us on a monthly retainer for drift detection, retraining, cost tracking and SLA‑backed incident response.
You receive: a live production system, a monitoring dashboard, runbooks, a documented retraining pipeline and, if you want it, an ongoing relationship with the engineers who built the thing.
Direct answers to the objections we hear on every <strong>scoping call</strong>.
Honest comparison: a senior AI engineer in the UK costs £120K to £180K fully loaded, and a functioning team needs three to five people minimum, plus MLOps and compliance coverage. That is a 12‑month hiring effort in a tight market. Consulting is the faster path to a first validated AI system in production, typically two to three months end to end, after which you can hire with a clear picture of what the team actually needs to do. We often recommend a hybrid model once the first system is live.
Three things. We build what we recommend, same senior engineers from scope to code, no handoff to a separate delivery practice. Mid‑market pricing and timelines, discovery from £5K, time to live system from two months, not 12‑month enterprise programmes starting at £250K. You own all the IP, code, models, training pipelines and documentation, with no vendor lock‑in.
Because the engineers who write the roadmap also write the production code. If the plan doesn't survive engineering reality, we catch it during scoping, not six months into a build. The scope and architecture document we deliver in week three is written to be executable, not persuasive. Most clients move straight into build with us; some take the document and execute internally. Either way, the output is something that can be built.
Discovery starts from £5K and runs two weeks. Scope and architecture is typically one additional week, priced as a fixed phase. A strategy‑to‑production engagement for a first AI use case runs 8 to 12 weeks total, with costs scoped in the proposal after discovery. Post‑launch support is monthly and priced in bands. The fastest we have taken a client from first call to a live production system is two months.
You own all of it. Code, models, fine‑tunes, data pipelines, documentation, deployment configurations. No rented layer we hold back. After go‑live, you can hand the system to your internal team with a documented runbook and a training session, or keep us on a monthly retainer for drift detection, retraining, cost tracking and SLA‑backed incident response. Not a dependency. A choice.
When you already have a capable AI engineering team and you just need extra hands, that is staff augmentation, not consulting. When you can only afford a strategy engagement and have no plan to build, because a roadmap without build capacity is shelf‑ware. And when the underlying business process isn't ready to change, because AI amplifies the direction you're pointed in, it does not create one. We will disqualify these situations on the scoping call.
Scaleup and mid‑market is where we do most of our AI consulting work, companies between 50 and 500 people with a product or operations function that would benefit from AI but no existing AI team. We also work with enterprises on specific, scoped initiatives. Big 4 engagements start at £250K and take 6 to 12 months. Our discovery starts at £5K and our first production system ships in 8 to 12 weeks.
