AI Integration · Workflow Automation
Automation that finally handles the messy steps.
Approval flows, classification, routing, document handoffs — the workflows your Zapier, Make, or n8n setup keeps tripping over because they need judgement. We add an AI layer where it earns its place, and leave the rest alone.
Automation versus intelligent automation
If/this/then/that automation has covered the easy half of your workflows for years. Trigger fires, fields move, Slack pings, row gets added. The other half — the steps that need a person to read something, classify it, decide which of three branches it belongs on — has stayed manual because no rule engine could handle it.
That’s where AI earns its keep. Not by replacing your automation stack, but by being the one node in the flow that handles judgement so the rest of the flow can run unattended.
What we ship
Classification and routing
Incoming requests, form submissions, inbound emails, support tickets, expense reports — anything that arrives in free-form text or a messy file. The model classifies it (category, urgency, owning team, customer tier) and the downstream automation runs as if the data had been clean all along.
Approval flows with judgement
Expense approvals that auto-approve under your policy and route the edge cases. Discount requests that auto-handle within negotiated bounds and escalate the outliers with a summary of why. Contract approvals that pre-check against playbook before reaching a partner. Every decision logged with the input, the policy applied, and the outcome.
Document handoffs between systems
A PDF arrives in a shared inbox, gets extracted, posted to NetSuite, attached to the deal in your CRM, mentioned in a Slack channel, archived in SharePoint, all with the right metadata. The kinds of handoffs that today require a human to copy-paste between five tabs.
Long-running, multi-step processes
Onboarding flows, procurement intake, employee leave handling, customer renewals. Workflows that span days, multiple humans, and conditional branches. The AI layer holds context across steps and remembers what’s outstanding, so nothing falls through.
3 – 8 hours / week
Typical operator time recovered per workflow once intelligent routing and approval flows are live, based on the engagements we run. The bigger gain is usually cycle time — workflows that used to wait a day in someone’s inbox close in minutes.
The automation platforms we work with
We plug into Zapier, Make (Integromat), n8n, Workato, Tray.io, Microsoft Power Automate, Pipedream, Airtable Automations, and custom workflow engines built in your own stack. We don’t force you to migrate. The AI step is a node in your existing flow — webhook in, structured response out.
For workflows that have outgrown a no-code platform, we build the orchestration directly — usually in TypeScript or Python with Temporal, BullMQ, or AWS Step Functions for durability.
Where the model runs
Three deployment patterns, chosen by what the workflow is moving:
- Vendor API (default): Claude, GPT, or Gemini through no-retention endpoints. Fastest for routine internal workflows.
- Your cloud: The workflow orchestrator and any intermediate state stay in your AWS, Azure, or GCP tenant; vendor models reached via your own keys.
- On-prem / open-source: Llama or Mistral hosted inside your network for regulated or air-gapped operations.
How we control AI cost
Workflows fire constantly. A bad cost design means a 3am bot loop will wake you up to a four-figure bill. Every workflow we ship has:
- Per-execution token caps and per-day per-workflow ceilings
- Small model for classification, premium model reserved for low-confidence escalations only
- Aggressive caching of repeated classifications — same input, same answer, no second call
- Idempotency guards so retries don’t multiply spend
- Automatic shutoff with Slack alerting if a workflow exceeds its budget envelope
- A cost dashboard your operations team can watch directly
Audit logs your operations team will trust
Every AI decision in a workflow is logged: the input, the prompt version, the model, the response, the policy or threshold applied, the downstream action taken, the human reviewer (if any), the timestamp. When something goes sideways three weeks later — and something always does — you have the trail to figure out which step misfired and fix it without guessing.
The 30-day proof of value
Pick the workflow that hurts most — the one your team keeps complaining about, the one that bottlenecks a quarterly close, the inbox that’s always behind. We’ll ship the AI layer for that workflow in 30 days, measured against your current cycle time and error rate. If those don’t move, you don’t scale. Cost envelope in writing on day one.
Frequently asked
Do we have to rip out our existing Zapier / Make setup?
No. We’d rather not. Your existing flows handle the deterministic 80% perfectly well; replacing that would waste your money. We add the AI step as a node in the flow you already have. Migration off no-code only happens when the workflow has genuinely outgrown the platform.
What happens when the model makes a wrong call?
Three layers: confidence thresholds route low-confidence decisions to a human; policy guards block actions outside defined bounds (no auto-approving an expense over your policy cap); every action is logged and reversible. The system is designed around the assumption that the model will be wrong sometimes.
How do you handle workflows that span multiple days?
Durable execution. For long-running flows we typically use Temporal, AWS Step Functions, or a similar workflow engine so state survives restarts, retries are safe, and pending human steps don’t time out silently. The AI is one node inside that durable workflow, not the orchestrator itself.
What does this cost ongoing?
Token spend is usually small per workflow ($100 – $1,500 / month at typical mid-market volume) because classification calls are cheap and well-cached. Optional retainer for monitoring, prompt updates, and adding new workflows. Sized honestly before you sign.