Engineer the work between your systems
Custom workflows connecting triggers, data, rules, AI judgement and approvals into an operating layer your team can actually inspect.
- Built around consequence, not blanket autonomy
- 98% extraction accuracy on structured documents
- First working automation live in 2–4 weeks
- UK-built
- London-based
- Human-reviewed automation
- Scoped before build
- Staged implementation
- GDPR-conscious by design
Automation fails where ownership disappears
Most operational waste doesn't live inside one tool — it lives in the handoffs between them. A lead copied into a spreadsheet by hand. A document waiting in an inbox. A report stitched together from exports. Somewhere, an employee has quietly become the integration layer.
- The same manual handoff, repeated every single day
- No one can say why a workflow failed last Tuesday
- Every new automation feels like a bespoke, unrepeatable project
What an engineered operating layer changes
Triggers, data and deterministic rules do the repeatable work. Bounded AI judgement handles what rules can't. Exceptions route to a person — and everything is logged, so your team can see exactly what happened and why.
Less repeated handoff work
Move information and actions between systems without manual copying.
More consistent process execution
Apply approved rules and validations at the point of work.
A safer route to scale
Expand from a monitored workflow rather than a hidden collection of automations.
What you receive
Triggers, data pipelines, deterministic logic, bounded AI judgement and human approval gates — engineered as one inspectable system.
Rules where rules are stronger
Keep known logic deterministic, testable and easy to audit.
AI where interpretation adds value
Bound model tasks with approved context, structure and fallback.
Exceptions with owners
Route uncertainty, missing data and failed actions into visible queues.
Operations you can inspect
Monitor runs, approvals, errors and business outcomes after launch.
AI where judgement helps
We don't default to AI for everything. Deterministic logic runs wherever the rule is already known — AI earns its place only where judgement genuinely adds value, and every exception has a defined human owner.
Proof, not promises
Representative figures observed across Silverstone AI automation deployments.
Results vary by scope, data quality, implementation and operating environment.
How delivery works
From one manual handoff to a monitored automation system.
Discover the handoff
Identify value, owner, baseline, systems and risk.
Design the controls
Map logic, AI tasks, approvals, exceptions and evidence.
Build and test
Implement normal and failed paths in the appropriate stack.
Operate and expand
Review runs and outcomes before automating the next process.
Trace the handoff before you automate it
Questions before the first workflow runs
Whichever fits the requirement — n8n for complex self-hosted orchestration, Make for visual flexibility, Zapier for straightforward cloud integrations, or custom code where logic or scale demands it.
Wherever it offers a suitable API, webhook or approved interface. We verify access and rate limits during discovery, before anything is promised.
Some bounded actions will. Others pause for approval or route to a person — the model follows consequence, not a blanket autonomy target.
Validation, deterministic rules, test cases, retries, logs and human review all play a part. No responsible provider promises zero errors.
Choose the first workflow with enough care to scale
Every week you delay is another week of the same manual handoff. The first conversation is exploratory and commits you to nothing.