AI automation for the work between your systems
Every trigger and approval you can inspect
- Scoped by consequence
- 98% extraction accuracy
- Live in 2–4 weeks
AI automation is a custom workflow connecting triggers, data, rules, AI judgment and approvals into an operating layer your team can actually inspect. Built on the tools US and UK businesses already run.
- Built around consequence, not blanket autonomy
- 98% extraction accuracy on structured documents
- First working automation live in 2–4 weeks
- US & UK clients
- London studio, US-based team
- Human-reviewed automation
- Scoped before build
- Staged implementation
- 24-hour team coverage
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. That person is usually the documentation too: the rule about which orders need a second check, the exception nobody wrote down, the account a connector authenticates as. When they're away the process slows; when they leave, it breaks. And the usual first response — a connector task built in an afternoon — moves the data without ever recording who owns the decision or what should happen when the data is wrong.
- The same manual handoff, repeated every single day
- No one can say why a workflow failed last Tuesday
- Every new automation feels like a custom, unrepeatable project
- One person holds the process — and all of the documentation
- A renamed field breaks a connector task silently, for weeks
What an engineered operating layer changes
Triggers, data and deterministic rules do the repeatable work. Bounded AI judgment handles what rules can't. Exceptions route to a named person — and every run is logged, so your team can see exactly what happened and why. The change is narrow and measurable: the handoff stops depending on who is at their desk, the same rule is applied at three in the morning as at three in the afternoon, and the next workflow extends a monitored system instead of becoming another one-off build.
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 judgment and human approval gates — engineered as one inspectable system. You also receive what makes it survivable: credentials held in your own accounts, a documented run-through of every branch including the failure paths, a named owner on each exception queue, and monitoring that alerts a person when a run stalls rather than letting it fail quietly.
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 judgment helps
We don't default to AI for everything. Deterministic logic runs wherever the rule is already known — AI earns its place only where judgment genuinely adds value, and every exception has a defined human owner. The same discipline decides autonomy: we start from consequence — what a wrong action would cost, who finds out, and how it gets reversed. Cheap-to-undo steps run unattended. Anything that moves money, messages a client or writes to a system of record waits for approval until it has earned autonomy. Each rule is written down where the people who run the process can read it, so the automation stays explainable long after launch.
Connector-first automation
Starts with the apps to hand and a happy-path trigger. Data moves, but nobody owns the decision, exceptions have nowhere to go, and one renamed field breaks it quietly.
Silverstone AI operating layer
Starts with ownership, source of truth, exception and consequence, then chooses the platform. Rules stay deterministic, AI stays bounded, and every run is logged.
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. Discovery traces a real run of the process end to end, exceptions included, and confirms API access and rate limits before anything is promised. Build runs against a copy of your data, and the failure paths are tested as deliberately as the normal ones. A first working automation is typically live in 2–4 weeks, then reviewed against the baseline before the next process is touched.
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: HubSpot, Salesforce, Pipedrive, QuickBooks, Xero, ServiceTitan, Jobber, Shopify, Stripe and the rest. 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.
AI automation is quoted from the published implementation bands: £2,000–£10,000$2,500–$12,500 for one focused workflow automated end to end, and £10,000–£50,000$12,500–$65,000 for a coordinated multi-system build, with most SME implementations landing between £10,000$12,500 and £25,000$32,500. Ongoing support starts from £350$450 per month. Software, API, hosting and usage costs are identified separately before work begins, and every band is published in GBP and USD on the pricing page.
A first working automation is typically live within 2–4 weeks of discovery: one process, its exceptions and its monitoring, rather than a platform rollout. Wider multi-system programs average around twelve weeks from discovery to deployment. The variables that move that date are access to the systems involved, the state of the source data, and how quickly exception rules can be confirmed with the person who currently makes those calls.
Connector tools are excellent at moving data and poor at owning decisions. In-house automations usually hold until the first edge case: a missing field, a duplicate record, an API returning an error nobody sees. Silverstone AI builds the parts teams rarely build for themselves — validation, retries with limits, exception queues with named owners, run logs and alerting — and will keep your existing Zapier or Make tasks in place wherever they are already doing the job well.
You do. Automations are built inside your own accounts and workspaces wherever the platform allows it, with credentials held by you and a handover pack documenting every trigger, branch, exception route and integration. Silverstone AI works across n8n, Make, Zapier and custom code precisely so nothing depends on a proprietary layer of ours: you can keep us on support, move to another provider, or bring the system in-house.
Data handling is designed for each implementation rather than claimed as a blanket guarantee. In practice that means data minimization — the workflow reads only the fields it needs — least-privilege credentials, a defined retention period, and a written record of which systems and which model providers see what. UK work is designed with UK GDPR and Information Commissioner's Office guidance in view; for US clients the same questions are asked against state privacy law and sector expectations, including HIPAA-conscious, non-clinical workflows in healthcare settings.
Connected systems change: a CRM renames a field, a vendor deprecates an endpoint, a token expires. Automations are therefore built to fail loudly rather than quietly — validation on the way in, retries with limits, and an alert to a named owner when a run stalls. Under a support retainer, monitoring, fixes and version updates are handled as part of the plan; without one, changes are quoted as they arise. Either way the run history shows precisely which step broke.
Sometimes, and that answer is settled in discovery rather than promised in a proposal. Where a system offers no API, the routes worth testing are scheduled file or email exports, a direct database or reporting connection, a supported import format, or a vendor integration that already exists. Where none of those exist, we say so: a fragile screen-scraping workaround that breaks at the next interface update is usually worse than the manual step it replaces.
Repetition matters more than size. A single handoff performed several times a day by one person is a better first candidate than a complex process touched twice a month, and narrow standalone automations start at £2,000$2,500. Silverstone AI works with owner-run UK businesses and US small and mid-sized companies as well as larger operations, and part of the purpose of discovery is to say plainly when a process is not yet worth automating.
Robotic process automation drives the user interface: it clicks, types and reads screens as a person would, and it breaks when the screen changes. The automation Silverstone AI builds works at the data layer instead — APIs, webhooks and database connections, with deterministic rules for logic that is already known and bounded AI only where interpretation is genuinely required. That makes it more durable to run, easier to test before launch, and far easier to audit afterward.
Silverstone AI is a London studio with US-based team members, and each system is built for the market it operates in: business-hours support across UK and US time zones, workflows that respect local time zones and date formats, currency and tax fields matched to the region, and the stack your team already runs — HubSpot, Salesforce, Pipedrive, Xero, QuickBooks, ServiceTitan or Jobber.
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.