Systems In Production
Every engagement here follows the same discipline: the operational context, the constraints we inherited, the architecture we deployed, and the outcomes as measured — not as marketed.
= The anatomy
The order matters. A system built before the constraints are known is a prototype that has to be rebuilt, and a number measured before the baseline is agreed is a number nobody trusts.
- 01
The operation as it runs today
Before anything is designed, the process is modelled end to end — the decisions currently made, who makes them, on what information, and how long each one takes. This is the baseline every later number is measured against.
- 02
The constraints that cannot move
Which data may not leave the perimeter. Which system cannot go down. Which decision a regulator must be able to read back. These are architectural inputs, not compliance paperwork collected afterwards.
- 03
The system, in your infrastructure
A single-tenant deployment into your private cloud or onto hardware you own. Documented, test-automated, and written so your engineers can read it — the architecture adapts to your stack rather than asking your stack to adapt.
- 04
Outcomes, against your own baseline
Measured on the decisions that matter to the operation, compared with how it ran before — not against a public benchmark that shares none of your constraints.
~ The patterns
Engagement patterns, not customer stories. Each describes the kind of problem, the constraint that shaped the architecture, and what gets measured — with the measurements left open until a customer approves publishing theirs.
High-frequency telemetry
- Context
- An engine produces temperature, pressure and timing data faster than any human process can read it, and the decisions that matter — how much power to ask for, and when — live inside microseconds.
- Constraint
- The hardware is not replaceable and the response has to happen on the vehicle, not in a datacentre it may not be able to reach.
- Architecture
- Telemetry read at the rate the hardware actually produces it, with optimisation running in the loop against what the engine reports while it is running.
- Measured
- [VERIFY: response-time and power-delivery deltas against the pre-deployment baseline]
Distributed asset optimisation
- Context
- Generation, maintenance and siting are usually optimised by three different teams against three different targets, and the interactions between them are where the losses are.
- Constraint
- Assets are geographically distributed, partially observable, and expensive to stop — a recommendation that requires taking one offline to verify is not a recommendation.
- Architecture
- One simulation covering all three, so a maintenance window is evaluated against its generation cost before it is scheduled rather than after.
- Measured
- [VERIFY: availability and yield change over a full seasonal cycle]
A decision layer beside the ERP
- Context
- The data needed for a decision already exists across several enterprise systems. What is missing is the decision itself, with its reasoning, delivered where the work happens.
- Constraint
- The ERP is the system of record and cannot be replaced, bypassed, or written to without an audit trail.
- Architecture
- A layer that reads from the existing systems and returns an action with its reasoning attached, inside the approval flows already in use.
- Measured
- [VERIFY: decision latency and escalation rate against the manual process]
Publishing an engagement means publishing an operation's constraints, and that is the customer's decision rather than ours. The technical briefing is where the specific ones get discussed — including which of the shapes above your operation resembles.
