AI that works in production
— not just pilots.
Outpace AI embeds frontier AI engineers directly inside your most complex enterprise environments. We don't hand you a framework. We build, govern, and operationalise alongside your team — until the systems are real and the outcomes are measurable.
6–12
Months per enterprise engagement
3-phase
Structured delivery from discovery to operations
Governance-first
Controls and auditability built in by design
The deployment gap
Why enterprise AI fails before it reaches production
Organisations have access to frontier models. The bottleneck is everything that comes after — infrastructure, access controls, integration, and the hard work of embedding AI into how people actually operate.
Infrastructure complexity is underestimated
Legacy permissions, siloed data environments, and identity architecture turn model deployment into a multi-quarter integration project. Most implementation partners leave when it gets hard.
Controls and auditability are afterthoughts
Boards, CISOs, and audit functions are asking hard questions about AI model usage, data access, decision trails, and system accountability. These requirements cannot be bolted on after deployment.
Pilots don't survive contact with production
POCs built outside real constraints — without real data, real permissions, real load — routinely collapse when moved into production environments. The problem is the gap between demo and operations.
“Security models, permissions, governance, compliance requirements, operational controls, and legacy infrastructure are core constraints — not edge cases — in real enterprise AI deployment.”
OpenAI on Forward Deployed Engineering · May 2026
The FDE answer
Forward Deployed Engineering solves this by putting specialised engineers inside the complexity — operating where the infrastructure is real, the stakes are high, and the outcomes are measurable from day one.
Our approach
Governance-First FDE — the Outpace AI difference
Most FDE shops are strong on engineering execution. Outpace AI layers governance architecture throughout — because in complex enterprises, how AI is deployed is as important as what it does.
Engineering-led deployment
- —Embedded engineers, workflow redesign
- —Strong on model integration and tooling
- —Governance handled by client team post-deployment
- —Audit trails and access controls as a separate workstream
- —Outcome metrics focused on technical performance
- —Knowledge transfer at end of engagement
Governance-First deployment
- ✓Embedded engineers plus governance architects
- ✓Model integration built for auditability from day one
- ✓Access controls, data classification, and audit trails co-designed with deployment
- ✓Board-reportable AI governance posture as a standing deliverable
- ✓Outcome metrics span technical performance and risk posture
- ✓Capability uplift embedded throughout — not a handover event
Security by design
AI access controls, data classification, and identity architecture are co-designed with the deployment model — not retrofitted.
Auditable by default
Every production system ships with decision trails, model versioning, and intervention playbooks your audit and risk functions can act on.
Board-reportable outputs
Every engagement produces AI governance posture reporting your CISO, CRO, and board can act on — not just engineering artefacts.
Delivery structure
Three-phase engagement model
Each enterprise FDE engagement follows a structured sequence designed to move from discovery to durable production systems — with governance architecture running in parallel across all phases.
Technical discovery and deployment architecture
The FDE team embeds into your environment to map the current state: data flows, identity and access architecture, existing AI tooling, integration points, and internal controls. We identify the highest-value use cases — the ones where AI in production will produce measurable, durable outcomes — and design the deployment architecture before a line of code is written. This phase produces a full technical blueprint alongside a controls and auditability gap assessment.
Production deployment with embedded engineers
The core phase. FDE team members operate embedded on-site or in a hybrid cadence — working directly with your engineers, data teams, and operational staff to build, test, and deploy AI systems into your production environment. This is not POC work. We build against real data, real permissions, and real infrastructure. Governance controls are instrumented in parallel, producing a live posture dashboard as systems go live. A formal evaluation framework is established in this phase to validate production performance against agreed baselines.
Handover, capability building, and durable operations
Systems in production are only valuable if your team can own and evolve them. This phase transitions operational accountability to your internal team through structured knowledge transfer, runbook documentation, and targeted capability uplift for your engineers. We instrument monitoring and alerting, establish model performance baselines, and define the criteria for when systems require intervention. Post-engagement support is structured as an ongoing advisory retainer or a defined review cadence.
Enterprise engagement
How we staff and structure an enterprise FDE engagement
Enterprise engagements deploy a cross-functional embedded team with defined roles and a clear operating cadence. Every engagement is scoped to your specific environment — no off-the-shelf delivery.
| Role | Responsibility | Presence |
|---|---|---|
| Lead FDE | Owns technical delivery, production deployments, engineering integration | 4 days/wk on-site |
| Governance Architect | AI controls design, access architecture, audit trails, board-level reporting | 2 days/wk |
| AI Integration Engineer | Data pipeline, API layer, identity and access controls, system integration | Full-time embedded |
| Evaluation Specialist | Model evaluation design, performance benchmarking, reliability testing | Phase 2 onwards |
| Engagement Director | Stakeholder management, executive reporting, commercial governance | Weekly steering |
Engagement intake criteria
- Defined senior sponsor (CTO, CISO, or CDO) with board mandate for AI deployment
- Existing cloud infrastructure and data platform with accessible APIs
- Active use case with measurable business impact — not pure exploration
- Internal technical team available for knowledge transfer and integration support
- Audit, risk, or board accountability requirements that make governance-embedded deployment essential
Minimum duration
6 months
Typical enterprise engagement: 9–12 months
Delivery model
Hybrid embedded
On-site presence plus structured remote support
Commercial structure
Fixed + Milestone
Base retainer plus outcome-linked milestone fees
Reporting cadence
Weekly + Monthly
Engineering standup weekly, governance posture report monthly
Post-engagement
Advisory retainer
Ongoing support, model evolution, and governance review
Outcomes framework
What we commit to measuring
Every Outpace AI FDE engagement is instrumented across three pillars. Metrics are agreed at kick-off, baselined in Phase 1, and reported throughout the engagement lifecycle — not summarised at the end.
Deployment Velocity
Governance Posture
Business Impact
We define success before we start
Outcome metrics are agreed and documented in the scoping document before Phase 1 begins. If baseline data does not exist, our team instruments measurement as the first deliverable. You receive a reporting pack at the end of every month — live data from your own environment, not a slide deck constructed after the fact.
Where we deploy
Environments we work in
Outpace AI FDE engagements operate in high-stakes environments where AI deployment demands more than engineering skill — it demands operational judgement, controls architecture, and systems that survive scrutiny.
Financial Services
Banks, insurers, asset managers operating under stringent audit, risk, and accountability frameworks.
Telecommunications
Large telcos with complex network operations, customer data at scale, and hybrid legacy/cloud-native infrastructure.
Government & Public Sector
Agencies deploying AI into high-accountability environments where explainability and public trust are non-negotiable.
Energy & Infrastructure
Critical infrastructure operators where AI must meet the highest standards for reliability, security, and continuity.
Healthcare
Health systems and payers deploying AI where data sensitivity, clinical accountability, and legacy integration define constraints.
Enterprise & Industrial
Large enterprise and industrial organisations with complex multi-system environments and board-level AI accountability.
Why Outpace AI
What makes our FDE practice different
The FDE model is gaining traction globally. What distinguishes the best practitioners is not just engineering depth — it is the ability to operate inside institutional complexity and deliver systems that last.
We build for production, not demonstration
Every system we deploy is built against real infrastructure, real data, and real constraints from week one. We do not run a separate POC track and then rebuild for production.
Governance is embedded, not appended
Our Governance Architect is in the room during system design — not brought in at the end to sign off. Access controls, audit trails, and board reporting are built into the architecture.
We commit to measurable outcomes upfront
Outcome metrics are defined and agreed before Phase 1 starts. You have a clear view of what success looks like — and a monthly reporting pack that shows whether you are tracking toward it.
Your team leaves more capable
Capability uplift is a first-class deliverable, not an afterthought. By the time our team exits, your engineers understand the systems they own — and your organisation is less dependent on external support.
Start the conversation
Ready to move from pilot to production?
Book a 60-minute technical brief with our Lead FDE. We will review your current AI deployment state, identify the highest-value use cases for embedded engineering, and outline what a scoped engagement would look like for your environment.
Technical briefs are available for enterprise organisations in financial services, telco, government, and critical infrastructure. Engagements are scoped individually — no off-the-shelf proposals.