AI task automation agents that run multi-step workflows end to end — invoice processing, data entry, email triage, and report generation — with audit logs and human approval gates when confidence is low.
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Independently audited, certified and built to standards you can check
AI task automation agents are autonomous systems that execute multi-step business workflows across your ERP, CRM, and internal tools. Built for operations and finance teams drowning in manual back-office work, they read intent, call systems, verify output, and escalate when uncertain — replacing RPA that breaks on every UI change.
Agents that read PDFs and scans, extract line items, match against purchase orders, flag discrepancies over your tolerance threshold, and post clean entries to your ERP — with the source document linked to every field for audit.
Inbound mail classified by intent and urgency, enriched with account context pulled from your CRM, routed to the right queue, and answered directly when the resolution is unambiguous. Ambiguous cases go to a human with a drafted reply attached.
The work that exists only because two systems disagree. Agents pull both records, diff them field by field, apply your business rules, correct the side that is wrong, and log why — replacing the weekly spreadsheet nobody wants to own.
Agents query the warehouse, assemble the numbers, write the narrative commentary a human would have written, and deliver to Slack or email on schedule — with the underlying query attached so finance can verify the figure.
New records enriched from public and licensed sources, deduplicated against what you already hold, normalised to your schema, and written back with a confidence score so low-confidence rows queue for review instead of silently polluting the database.
Agents that walk a defined procedure every time — gathering evidence, checking each control, and producing the completed artefact with timestamps. The checklist runs identically at 3am on a holiday as it does on a Tuesday.
Our Work
200+ products shipped across fintech, healthcare, e-commerce, and SaaS — built to scale, designed to convert.
Web Design
A marketing site for an interior design studio, rebuilt on Next.js to load fast on mobile and convert visitors into enquiries.
Healthcare
A patient management platform handling scheduling, records and clinician-patient messaging for a healthcare provider.
E-Commerce
A fitness e-commerce storefront built on Next.js with Shopify as the commerce backend and Stripe handling payments.
Logistics
A delivery management platform with live vehicle tracking, route planning and customer-facing shipment status.
Logistics
A freight management platform for an established trucking operator, covering load tracking and job records.
SaaS
A multi-tenant SaaS platform that aggregates business reviews across sources and surfaces them in one dashboard.
We sit with the people doing the work and record the actual process — including the exceptions they handle by instinct. Those exceptions are what kill naive automations, so we capture them first.
Every system the agent touches gets a typed, permission-scoped tool with its own rate limit and rollback path. The agent never gets raw database or admin access — it gets a narrow set of verbs it is allowed to use.
The agent runs against live traffic but commits nothing. We compare its proposed actions to what your team actually did, and tune until agreement is high enough to trust. Nothing goes live on a demo.
We release autonomy by risk band. Low-value, reversible actions go unattended first; high-value or irreversible ones keep a human approval gate permanently. You choose where each line sits.
Dashboards for completion rate, escalation rate, latency and cost per task. When the process changes, we update the agent — automations that are not maintained quietly stop matching reality.
Common questions about AI task automation agents — reliability, escalation, integrations, audit evidence, timelines and running cost.
Ask our teamRPA replays a fixed script of clicks and selectors. It works until a field moves, a label changes, or an input arrives in an unexpected format — then it fails, usually silently. A task automation agent works from the goal rather than the script: it reads the current state, decides the next step, and adapts when the interface or the data shifts. In practice this means it survives the ordinary drift that breaks RPA bots, and it can handle the exception cases that previously routed to a human.
It escalates rather than guesses. Every agent we ship has an explicit uncertainty path: when confidence falls below the threshold you set, or when a case matches a rule you have marked as human-only, the agent stops, packages the context it gathered, drafts what it would have done, and hands it to a person. The human either approves it in one click or corrects it — and the correction becomes a test case.
Anything with an API, which in practice covers ERPs (SAP, NetSuite, Dynamics), CRMs (Salesforce, HubSpot), accounting (QuickBooks, Xero), ticketing (Zendesk, Jira, ServiceNow), databases, email, Slack and Teams, storage, and internal services. For legacy systems with no API we build a narrow integration layer rather than screen-scraping, because scraped integrations are the ones that break.
Every run produces a complete trace: the trigger, the inputs it read, each tool call with its arguments and response, the model reasoning at each decision point, the final actions, and the identity of any human who approved a gated step. Traces are retained per your policy and exportable. For regulated processes we scope this in the design phase so the evidence you need is a byproduct of normal operation, not a retrofit.
Four to six weeks for a single well-scoped workflow, including the shadow-mode period. That timeline assumes we can get access to the target systems in week one — API credentials are usually the critical path, not the engineering. Broader programmes covering several related workflows run two to four months.
Running cost is model inference plus hosting, and it scales with volume rather than headcount. We instrument cost per completed task from day one and show it on the dashboard, because an agent that quietly costs more per task than the manual process is a failure even if it works. Where volume justifies it we route simple steps to smaller, cheaper models and reserve frontier models for the genuinely hard decisions.
Bring us the workflow your team complains about most. We will map it, tell you honestly whether an agent is the right answer, and scope it at a fixed price.
