Practice
Forward Deployed AI Engineering
Senior engineering embedded close to the operation, taking one defined AI opportunity into dependable production use.
The position
Forward Deployed AI Engineering places senior engineering judgement inside the organisation, alongside the people who own the process, to take a defined operational use case from intent to a capability that runs in production and can be measured, operated and owned internally.
The target is a measurable operating capability running inside the systems and workflow the organisation already depends on, not a demonstration and not a strategy document.
The failure this fixes
The obstacle is rarely the model. It is the distance between an intention and a system the organisation can actually run.
- A defined use case with no production route
- The operational problem is understood and the intent is real, but there is no engineering route from that intent to a capability the business can run day to day.
- Work that stops at demonstration
- A prototype behaves well in controlled conditions and then stalls, because production requires integration, data access, permissions, evaluation, failure handling and someone accountable for operating it.
- No agreed measure of good enough
- Without evaluation criteria agreed before build, quality stays an impression, and there is nothing to expand or withdraw the capability against.
- Nothing the organisation can operate afterwards
- Delivery produces something nobody inside the organisation can evaluate, adjust, extend or safely retire.
Frame, build, deploy, assure, transfer.
Most of the market stops after build and deploy. The last two stages are what make the capability something the organisation can trust and keep.
Frame
The problem, the workflow, the systems and data involved, the intended operating outcome, the constraints that apply and the autonomy boundaries the organisation is prepared to accept.
Build
The appropriate AI or software capability for that outcome, at the smallest scope that proves it in real use, using the architecture justified by the problem and operating context.
Deploy
Into the real operating environment, with the controls that environment requires rather than the ones a demonstration can get away with.
Assure
Where Lornets differs
The existing Lornets Production Assurance approach applied to the capability, with its behaviour, limits and residual risks established through evidence and verification.
Transfer
Where Lornets differs
Handover of the capability, the evaluation method and the operating knowledge to the internal team, or continuation under Managed Production Engineering if the organisation prefers.
Already operating an AI system and need to establish its governance and evidence position? See AI Governance & Assurance Readiness.
What production imposes
Until these hold, a working prototype is not yet something a process can depend on. They are engineered in from the outset rather than added afterwards.
- Integration with the systems the workflow already runs on
- Data access, quality and residency
- Identity, permissions and security
- Evaluation criteria and measured behaviour
- Human intervention where the decision carries consequence
- Failure handling, recovery and reliability under real usage
- Observability of system and model behaviour
- Latency in the actual workflow and the cost of running it
- Deployment, change control and operating ownership
Architecture is a choice
Lornets is not an AI agents shop. Autonomy is granted where it is justified by the consequence of the decision and bounded everywhere else. The architecture follows the operational problem.
- Conventional software with no model involved
- Deterministic workflow automation
- Traditional machine learning
- Retrieval over the organisation's own material
- An LLM application inside an existing product or process
- Agentic components where the task genuinely warrants them
- A combination of these, selected against the outcome
None of these is the default. The selection is made against the outcome and recorded with its reasoning.
What you receive
- A working capability operating in the real environment and workflow
- Measured behaviour against the agreed criteria, including the failure modes observed
- The integration, data access and permissions position as built
- Operating controls as implemented, with the cost position at real usage
- Technical Decision Records for material choices, including autonomy boundaries
- Residual Risk Register, including accepted limitations
- Handover material, operating guidance and knowledge transfer
Typically a short framing period, then a bounded delivery period by agreement
Scoped per engagement following framing
Outside the engagement
- Generic MVP or first-product development.
- Speculative AI strategy or research without production intent.
- Commodity staff augmentation or temporary coding capacity.
- Model, platform or licence resale. Lornets has no vendor incentive.
- Guaranteed accuracy, adoption or commercial outcomes. Behaviour is measured and reported.
Legal or regulatory opinions and formal certification sit outside the engagement.
Who this is for
- A consequential operational workflow with enough value to justify engineering it properly, where the result affects customers, cost, risk or delivery
- Genuine intent to run the capability in production
- An outcome that can be measured once the capability is in use
- Systems and data that are relevant and reachable
- A named business owner for the process the capability supports
Questions we are asked
Start with the decision, not the technology.
Bring the process you want AI to affect and the standard it would have to meet before the organisation could depend on it. Framing happens inside the engagement, not as a separate product.