Is this staff augmentation?

No. The engagement is accountable for a defined operational outcome, not for supplying developer hours. Lornets works embedded with your team, but the scope is a specific use case taken to a production capability, with the evaluation and operating position that goes with it.

Do you only build autonomous AI agents?

No. Agentic architecture is an implementation choice, not the objective. Depending on the problem, the right answer may be conventional software, a deterministic workflow, traditional machine learning, retrieval, an LLM application inside an existing product, agentic components, or a combination. Autonomy is granted only where it is justified.

Do we need an existing prototype?

No. A defined operational problem, the systems and data around it and a named business owner are what matter. Where a prototype already exists it is assessed on its merits and kept if it remains appropriate.

Can you work with our existing engineering team?

Yes, and that is the normal arrangement. Your team keeps ownership of the system. Lornets contributes senior engineering judgement, the evaluation method and the production conditions, then transfers the operating knowledge rather than retaining it.

Can you work with our existing cloud or model provider?

Yes. Lornets resells nothing and holds no vendor relationships, so existing cloud, platform and model choices are kept where they remain appropriate. Where a change is proposed, the reason is stated and the decision remains yours.

How do you decide how much autonomy to give an AI system?

By the consequence of the decision being made. Autonomy boundaries are agreed during framing, human intervention is placed where a wrong answer would matter, and any widening happens on measured behaviour rather than confidence. The principle is least autonomy necessary.

Forward Deployed AI Engineering

Turn a consequential AI use case into a production capability.

Lornets embeds senior engineering inside the organisation to frame the use case, build the capability the outcome justifies, deploy it with the controls production requires and transfer it to the internal team.

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The question this service answers

How do we turn a consequential AI use case into a production capability inside the organisation?

The target is a measurable operating capability running inside the systems and workflow the organisation already depends on.

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.

Where assurance is required, the work is evaluated through the existing Production Assurance Framework.

Assess first. Keep what works. Change only what the evidence justifies.

The commercial position

The situation: The organisation intends to use AI in a real operational process, and needs to move from intent or a stalled pilot to a capability it can depend on.
The decision: Is this AI use case viable, and what is the smallest capability worth building and operating?
What Lornets does: Lornets embeds senior engineering inside the operating environment, tests candidate use cases against value, data, error tolerance and cost, defines evaluation before build, then delivers a bounded capability under production conditions.
What you leave with: Use-case viability decision, including use cases assessed and any not recommended
Evaluation criteria and measured baseline behaviour
A working AI capability operating in the real environment
Production conditions for oversight, data handling, observability, failure behaviour and cost
Handover material, operating guidance and retained internal capability
What may happen next: Where the capability becomes important to the business, Production Readiness Assessment or Managed Production Engineering may follow. Concluding that a use case is not currently justified is an equally valid outcome.
When this is not appropriate: You want a demonstration proof of concept with no intention of operating it
You need a model provider, platform reseller or licence procurement route
The build decision is already fixed and only delivery capacity is wanted
The process the AI is meant to support is not yet defined or owned

Why AI work stalls before production

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.

How the engagement works

Frame, build, deploy, assure and transfer. This engagement model sits inside the wider Lornets methodology.

01
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.
02
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.
03
Deploy
Into the real operating environment with the controls it needs: integration, data access, identity and permissions, human intervention points, failure handling, observability, latency and cost.
04
Assure
The existing Lornets Production Assurance approach applied to the capability, with its behaviour, limits and residual risks established through evidence and verification.
05
Transfer
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.

What production requires

These production conditions are engineered into the capability from the outset. Until they hold, a working prototype is not yet something a process can depend on.

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 an engineering choice

The outcome decides the design. Agentic architecture is one option among several and is used only where the task genuinely warrants it.

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

Engineering principles

These principles hold whether the capability is a retrieval workflow, an assisted decision, an automation path or a model-backed feature inside an existing product.

Outcome before architecture. The operating result decides the design, not the reverse.
Least autonomy necessary. Autonomy is granted where it is justified and bounded everywhere else.
Human intervention by design, placed where a decision carries consequence.
Evidence before expansion. A capability is widened once its measured behaviour supports it.
Agentic architecture is an implementation choice, not the objective.
Keep what remains appropriate. Existing systems, models and workflows stay where they still serve the outcome.

What you receive

The engagement produces an operating capability and the evidence that supports it, together with a clear statement of what the organisation now owns.

Typically a short framing period, then a bounded delivery period by agreement

Scoped per engagement following framing

A working capability operating in the real environment and workflow
Evaluation criteria and measured behaviour, including observed failure modes
Integration, data access and permissions position
Operating controls: intervention points, failure handling, observability and cost position
Technical Decision Records for material choices, including autonomy boundaries
Residual Risk Register, including accepted limitations
Handover material, operating guidance and knowledge transfer

Engagements normally begin with a short embedded framing period, followed by a bounded build and deployment where the evidence justifies it.

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

Boundaries

Legal or regulatory opinions and formal certification sit 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.

Questions and objections

Related engagements

Production Readiness Assessment
Where software already exists and the question is whether the organisation can depend on it.
Production Hardening & Scale
Where a system is already in production and specific constraints have been established.
Managed Production Engineering
Where a capability in production needs continuing stewardship as systems and usage change.

Start with the decision, not the technology.

Describe the process you want AI to affect and what would have to be true for the organisation to depend on it. We will establish whether Forward Deployed AI Engineering is the appropriate engagement.

Book a readiness call How Lornets works