Kill the manual ops work. Keep the humans for decisions.
For operations-heavy teams drowning in document processing, ticket routing, data entry, and approval loops. We replace brittle no-code automations with AI-native workflows that understand context, handle exceptions, and log everything.
We automate the repetitive work that sits between your systems — reading documents, moving data, triaging requests, generating routine replies. Usually two to six weeks per workflow. We start with the one costing you the most hours.
Updated August 2026
Not every workflow is worth automating.
How we triage before quoting anything.
Zapier is fine for simple pipes. Most real ops work isn't simple.
The workflows that actually cost your team hours every week are the ones with exceptions — the invoice that's missing a PO number, the ticket that could mean three different things, the form that a customer filled out wrong. No-code tools handle the happy path. We automate the full path.
AI-native automation means reading documents with true comprehension (not just regex), routing by semantic meaning (not just keyword rules), and handling exceptions with configurable human checkpoints (not email chains). We build on n8n, Temporal, and custom state machines — whichever gives you the most control and debuggability.
Every automation we build ships with an exception queue (so humans see what the AI couldn't handle), a cost dashboard (so you know what each workflow run costs), and a full audit log (so compliance teams can review every decision). These aren't nice-to-haves — they're how automation earns trust.
What we automate.
Document understanding & routing
Ingest invoices, contracts, forms, reports, and emails — extract structured data, validate against business rules, and route to the right system or person.
Email & ticket classification
Read inbound email and ticket content, classify intent, extract entities, assign to the right queue, draft initial responses, and escalate ambiguous cases.
Approval & exception workflows
Multi-step approval flows with AI-generated summaries, risk scoring, and one-click approve/reject — plus exception queues for cases that need human judgement.
Data entry & enrichment
Read unstructured sources and write structured data to your CRM, ERP, or data warehouse — with validation, deduplication, and audit logging.
Reporting & alert triage
Monitor feeds, dashboards, and alert streams — summarize, prioritize, suppress noise, and surface what actually needs attention.
Multi-system orchestration
Workflows that span Salesforce, HubSpot, Jira, Slack, Google Workspace, Notion, and your internal APIs — triggered by events, schedules, or webhooks.
Which workflows are worth automating.
Not all of them are. This is roughly how we triage before quoting anything.
| Workflow looks like | Automate? | Why |
|---|---|---|
| Same steps, high volume, clear rules | Yes — first | Fastest payback, lowest risk |
| High volume, messy inputs | Yes — this is where AI helps | Rules alone cannot read a scanned invoice |
| Low volume but very slow manually | Sometimes | Depends on what the delay costs |
| Needs judgement every time | Assist, do not automate | Draft it, let a person approve |
| Changes constantly | No — fix the process first | You would automate the churn |
| Happens twice a year | No | The build costs more than the task |
Where we start.
We measure what your team actually spends time on before proposing anything.
Draft and approve beats fully automatic for anything a customer sees.
Small, provable pieces rather than a transformation programme.
How we build it.
Workflow audit
We map the exact steps, volumes, error rates, and cost of the workflows you want to automate. We rank by ROI and start with the highest-leverage ones.
Exception design
We identify every exception case and design human checkpoints for each. The automation handles the clear cases; humans handle the edge cases — with full context surfaced.
Build & test
We build the workflow with AI comprehension at the reading and routing steps, rule-based logic at the deterministic steps, and configurable thresholds everywhere in between.
Shadow mode
We run in parallel with the existing manual process for 1–2 weeks — the automation acts, but humans review before committing. We measure accuracy and catch edge cases.
Handover & monitoring
We go live with a full observability stack — exception queue, cost dashboard, and SLA monitoring. We tune weekly and hand over to your ops team with full documentation.
Tools we use.
Frequently asked.
5 questions answered. Still have one? Reach out.
Zapier and Make connect systems with fixed rules. AI automation handles the cases where you need to understand content — reading a PDF and deciding what to do with it, classifying a ticket by meaning rather than keywords, or extracting data from an unstructured form. We use Zapier and Make for the simple pipes; we use AI for everything that requires comprehension.