Blended consulting and AI engineering, inside your teams.
Moving from a promising demo to a production-grade AI system takes more than API access. It takes engineers who can build, advise and adapt quickly while keeping the customer outcome and product quality in view.
Deploy and manage frontier AI platforms
Stand up Claude and the surrounding platform inside your environment, then run it as a production system rather than a pilot.
Drive user adoption and productivity gains
Work with the business users who will live with the system, so the deployment is used, measured and improved.
Own reliability, scale and performance
Evaluation, monitoring and guardrails from day one, with the same engineer accountable when something drifts.
Stay embedded as a long-term partner
Remain with the team through rollout and iteration, and hand over people who can carry it forward.
What does our FDE do?
A snapshot from our FDE services to customers.
- 01
Platform security & governance
Identity, data boundaries, audit trails and policy enforcement, so the platform passes the reviews it will face.
- 02
Observability & platform integration
Tracing, evaluation and cost telemetry wired into the tools your operations team already watches.
- 03
Connectors & vendor integration
Enterprise systems connected through APIs, retrieval, tool use and MCP-style architecture.
- 04
Skills & prompt engineering
Reusable agent skills, prompts and orchestration patterns built for the customer's own workflows.
- 05
Knowledge management
The document and data sources a model actually needs, curated, permissioned and kept current.
- 06
Enablement & onboarding
Business and IT teams trained on the system, with playbooks that outlast the engagement.
Who actually needs an AI FDE?
The need looks different depending on the size of the organisation. Three segments, three very different reasons.
01
Large enterprises and corporates
Dozens of AI pilots, fragmented IT teams and no one owning outcomes. An FDE turns scattered experiments into governed, production-grade deployments.
02
Small and medium businesses
No AI team to hire and no budget for one. An FDE shows up as a fractional expert and leaves something that keeps working.
03
ISVs and software vendors
Racing to ship AI features before the roadmap goes stale. An FDE gets the first agentic feature into a customer's hands fast.
The common thread
Capability without an owner. An FDE sits inside the team, ships the first real deployment and leaves behind people who can run it. AI is evolving by the week. The question is whether your team is built to keep up.
Not every AI FDE is built for the job.
Staff-augmentation agencies are supplying cloud engineers, DevOps engineers and software developers as "FDEs". A real AI FDE is a different breed.
The cost of getting this wrong
The wrong FDE means failed pilots, lower adoption and no ROI, the exact outcomes the role was created to prevent. Customers do not need more FDEs. They need the right one, embedded with their business and IT teams.
- 01
Business analysis
Works with business users to turn ideas into AI systems, agents or skills.
- 02
Data engineering
Pulls in and transforms the data sources a model actually needs.
- 03
ML engineering
Hosts models and runs post-training to fit the customer's domain.
- 04
Cloud & platform
Deploys and integrates APIs as containers, production-grade.
- 05
Full-stack delivery
Integrates with applications and tests it end to end, like a developer.
From the first use case to a playbook your team owns.
Step 1
Find the use case
Work with the business to identify high-value use cases aligned with real goals, not the loudest demo.
Step 2
Design for the environment
Production AI designed around your workflows, systems and constraints, including regulated ones.
Step 3
Build the integration
APIs, retrieval, tool use, orchestration and MCP-style connectors, shipped as reusable components.
Step 4
Prove it safe
Evaluation, monitoring and guardrail mechanisms established before the first user, not after the first incident.
Step 5
Take it to production
Pilot-to-production rollout, adoption support and ongoing iteration with the people who own the process.
Step 6
Leave a playbook
Deployment patterns standardised so the next use case, team or region starts from a working reference.
Pilot to production, quickly
Robust, production-ready deployments rather than another proof of concept.
Measurable business value
Productivity, faster decisions or operational automation that the business can put a number on.
Stable, secure, maintainable
Solutions that operate reliably in the customer's environment after the engineer steps back.
Reusable patterns
Every deployment shortens time-to-value for the next one.
Enablement · Adoption · ROI