Enterprise AI projects don't fail at the model. They stall at the integration boundary, where Legal, Security and Finance discover the system can't actually ship. We scope the data access layer, compliance structure and cross-system integration before writing production code, so the launch isn't blocked by the things nobody owned.
We're a London-based AI engineering team working with CTOs and Heads of Engineering at mid-to-large enterprises (500+ employees) across the UK, Europe and the US. 50+ AI features shipped to production, a named technical lead on every engagement, and architecture decisions made in writing rather than handed off.
Compliance-first scopingProduction boundary engineered up frontNamed technical lead, no handoffEU AI Act-ready architecture
The first question we ask is who owns the system when it breaks. If the answer is unclear, the AI shouldn't be in production. We define ownership, on-call rotation, runbooks, escalation paths and rollback procedures as part of the scope, not as a Phase 2 nice-to-have. Your team gets a system they can actually run.
Pilots get stuck because nobody scoped what 'production' actually means inside your org: sign-offs, vendor reviews, security exceptions, IAM tickets. We map every gate in your release process and engineer them into the timeline. Eight to twelve weeks to a system that's actually live, not another demo with a 'next steps' deck.
Most enterprise GenAI pilots cap out at prompt engineering and a thin chat layer. The next bar is tool use, retrieval over your real data, transactional state and observability. We design at that bar from the start, so the system you ship has somewhere to grow instead of being thrown away in twelve months.
Your contract includes full IP ownership of source code, infrastructure-as-code, prompts, model weights or fine-tuning recipes where applicable, and clean handover to your team or another vendor. We document the system so you can take it in-house at any point. The only reason you stay is because the work is good.
We map the use case against your data, IAM, integration surface and regulatory exposure before quoting the build. Discovery includes interviews with Engineering, Security, Legal and the business owner, because all four have to sign off for the system to ship.
Output: an architecture proposal, an integration plan, a compliance plan and a fixed-scope quote for the build.
Where the integration risk is high, we run a time-boxed PoC on your data, your APIs and your IAM, not a sandboxed demo. The PoC is designed to fail fast on the things that usually kill enterprise AI: undocumented schemas, rate limits, permission propagation, retrieval quality on messy production data.
We will tell you to stop if the economics don't justify the build. We have done this on our own engagements.
Build runs as a small senior team (named technical lead Michal Vavra, AI engineers and integration engineers) embedded with your stakeholders. Each release is gated on model evaluation, regression on a fixed eval set and human review for any irreversible action.
We ship to production behind feature flags, with observability and rollback in place from week one.
At launch we wire in model monitoring, drift detection, cost telemetry, audit logging and incident response. You receive runbooks, architecture documentation and a handover session with your engineering team.
Ongoing support is a monthly retainer, not a lock-in. Take the system in-house whenever you're ready.
Frequently asked <strong>questions</strong> about enterprise AI solutions, governance and integration.
Across recent industry reporting, roughly 70% of enterprise AI pilots stall before production, and the failure point is rarely the model. It's the integration boundary: data access, IAM propagation, audit requirements, vendor sign-off, change management. We scope all of that during discovery so the launch isn't blocked by the things nobody owned.
We don't sell decks. The deliverable from discovery is an architecture proposal, an integration plan, a compliance plan and a fixed-scope quote for the build. The same team that scopes the work also builds it. No handoff, no re-scoping when implementation starts.
Engagements are led by Michal Vavra, our AI engineering lead, with a small senior team of AI and integration engineers embedded with your stakeholders. You have one named technical owner from discovery through production. No account managers in the build channel.
We design governance into the architecture: risk classification, model cards, human oversight, audit logging, incident response. For UK and EU clients we map the system against the EU AI Act's GPAI obligations (which start carrying penalties up to €35M or 7% of global turnover from August 2026), UK GDPR, DPA 2018 and any sector frameworks you operate under (FCA SYSC, NHS DSPT, PCI DSS).
An agent with a scoped surface area: it can read and write to a defined set of systems via OAuth-scoped permissions, it logs every action to an audit trail, irreversible actions go through human approval, and there's a kill switch. We tell you up front which workflows are agent-shaped (deterministic ground truth, reversible state, low-stakes errors) and which should stay as conventional automation.
Yes. Standard contract is full IP ownership for the client: source code, infrastructure-as-code, prompts, model weights or fine-tuning recipes where applicable. We document the system so it can be handed over to your team or another vendor at any point.
If you want a strategy deck without a build path, we're not the right fit. Most management consultancies are better at that. If you have no executive sponsor, no data access, or you want a sandboxed pilot with no production timeline, discovery will save us all time by saying so. If your problem is genuinely solved by an off-the-shelf SaaS tool, we'll tell you.
