Is the system you run as sound as you think?
An honest read of the system you already run, yours or a vendor's, ending in a costed review and a blueprint anyone can act on.
We review the architecture of software and AI systems that are already built: how they scale, where data flows, what is too tightly coupled, what they cost to run, how they fail, and how exposed they are. The result is a clear picture of what you have and a prioritised plan for what to do about it.
An engineering firm, not an audit shop. Act on the blueprint with anyone.
- Sound
- Watch
- Review first
You can feel the problem before you can name it
Most teams know something is wrong with a system long before they can name it. It is slower than it should be, the cloud bill keeps climbing, one change breaks three things that should be unrelated, and nobody is quite sure what happens the day the load doubles or a key service goes down. The knowledge lives in a few people's heads, and it leaves with them.
AI systems add their own questions. Where does the data go, what grounds the answers, who can see what, and what happens when the model is wrong. A second opinion from a team that builds these systems tells you which of these worries are real, which are not, and what each one would cost to fix, before you spend on a rebuild you may not need.
Five lenses, read one at a time
We read the same system through each of these lenses. The first four apply to any software. The last is for the systems with AI inside them, which carry questions of their own.
Scalability and cost
Where the system strains under load, what it costs to run today, and which parts will get expensive or slow as you grow.
Data flow and coupling
How data moves through the system and where parts are wired together so tightly that one change forces many others.
Failure modes and resilience
What happens when a dependency goes down, an input arrives broken, or a job fails halfway, and whether the system recovers or just stops.
Security posture
Access control, data handling, exposed surfaces, and the gaps that a review catches before someone else does.
AI-specific risk
For systems with AI in them: where answers come from, whether outputs are traceable to a source, how data is kept private, and where a wrong output reaches a real decision.
What you hold at the end
Every finding lands on one map of your system, marked sound, watch, or review first, and is written up so a technical lead and a decision-maker can both follow it.
- 01
A written architecture review
A clear account of how the system is built, what is sound, and what is at risk, in language a technical lead and a decision-maker can both follow.
- 02
A prioritised blueprint
The findings ranked by impact and effort, so you know what to fix first, what can wait, and what to leave alone.
- 03
Honest costing
What each change would take to make, with the quick wins separated from the work that needs real investment, so nothing is hidden behind a vague estimate.
- 04
A risk and failure-mode map
Where the system can fall over, how exposed it is, and what would happen if it did, written down rather than carried in someone's head.
- 05
A handover walkthrough
A working session with your team so the reasoning behind every finding stays with you, and the blueprint is yours to act on with any builder.
From what is wrong to yours to act on
- 01
Scope
We agree what the review covers and what matters most to you, the parts under strain, the decisions you are weighing, the risks that keep you up. We read the system, not a questionnaire.
- 02
Review
We work through the architecture against scalability, data flow, coupling, cost, failure modes, security, and AI risk. We talk to the people who run it and look at how it behaves in production, not only how it was meant to.
- 03
Blueprint
We write up what we found in plain language, rank it by impact and effort, and cost the changes honestly. You get a clear picture and an ordered plan, with the cheap wins separated from the larger rebuilds.
- 04
Hand over
We walk your team through the findings so the reasoning stays with you. You can act on the blueprint with anyone. We can help build the changes if you want us to, and we will say so plainly rather than steer you there.
We hold a review to the production standard we hold our own product to: it runs across 55 VAT jurisdictions, so the failure modes and cost trade-offs we look for in your system are ones we have lived with in ours. Pileform, our AI bookkeeping and VAT automation product, is the standing proof. If the blueprint calls for a build, that is our custom software work, and you are free to take it elsewhere.
What teams ask before a review
Will you review a system you did not build?
Yes, that is the point of this engagement. Most reviews we do are of systems built by an internal team or an outside vendor. We read what is there and tell you honestly how it stands, with no stake in who built it.
Is this just a sales pitch for a rebuild?
No. The deliverable is a costed review and a blueprint you can act on with anyone, including a team that is not us. Where a part of the system is sound, we say so. Where a simple fix beats a rebuild, we say that too. We would rather be honest than win the build.
What do we actually get at the end?
A written review of the architecture, a prioritised blueprint ranked by impact and effort, an honest costing of the changes, a map of the failure modes and security gaps, and a walkthrough so the reasoning stays with your team.
Do you review AI systems specifically?
Yes. On top of the usual scalability, coupling, and security checks, we look at where answers come from, whether outputs are traceable to a source, how private data is handled, and where a wrong output reaches a real decision. This is work we do on our own product as well as for clients.
How long does a review take?
It depends on the size of the system and how much of it is in scope. A focused review of one service is faster than a full platform. We agree the scope and the timeline up front, before you commit, so there are no surprises.
What if the review finds the system is mostly fine?
Then we tell you that, and you have spent a little to gain confidence and a short list of small improvements. A clean bill of health is a real and useful outcome. We will not invent problems to justify the engagement.
Tell us which system you are unsure about
Bring us the platform that is straining, the vendor build you cannot fully see into, or the AI system you are about to put in front of real data. We will tell you honestly how it stands and what to do next.