Arthoken
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New products. Better systems. Practical AI. Engineering around your business.
Let’s talk01 / Products & platforms
Turn your idea into a product people can use.
Start here when you need to test demand or replace a manual process with a product of its own.
02 / Existing systems
Make an ageing system safer and easier to change.
Start here when a change carries too much risk, or an old system is blocking a new business need.
03 / AI & automation
Put AI to work, with reliability and human oversight.
Start here when a task involves language or unstructured information, and you need to test whether AI can handle it reliably.
04 / Reliability & growth
Keep your system fast, reliable and affordable as demand grows.
Start here when growing demand is exposing slow responses, outages or rising infrastructure costs.
05 / Engineering teams
Remove the bottlenecks between your team and the next release.
Start here when work spends more time waiting for review, environments or release approval than being built.
One challenge can span several services. We scope them together.
Inside the practice
How the work gets done
We use models for research, architecture options, first-pass code, migrations, tests and documentation. Here is how that assistance fits into the engineering process.
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AI-generated work goes through review and validation before it ships. The engineer approving a change is responsible for its correctness, security and fit with your business rules.
What a demo is missing
For an AI-enabled system, the prototype tests the core interaction. Production adds the controls and operational support needed for everyday use.
We agree which controls your system needs as part of the production scope.
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Modernization
We move one capability at a time, compare behaviour against the old system, and plan each cutover around continuity and rollback.
A façade goes in front of the old system. Nothing behind it changes yet.
One capability at a time moves out into a service you can actually change.
Replay representative traffic safely and investigate differences before switching.
Traffic moves across when the evidence says it is safe. And it can move back.
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Reference architecture
A reference approach for AI-assisted decisions, showing how source information, business rules and human review connect.
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Reading, drafting, summarising, extracting. It is not the one making the final call.
Rules you can read, test and explain to an auditor months later.
Approvals are recorded. Reviewed examples can become evaluation cases for future changes.
Record the inputs, model output and applied rules so the team can investigate a result.
It starts with a conversation
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