AI Workflow Automation

We build AI-augmented workflows that connect your existing tools, draft outputs with LLMs, and route the important stuff to people for approval. Less ticket-shuffling, faster cycle times, fewer manual mistakes.

Get Started All AI Services

AI Workflow Automation

Automate the boring parts — keep humans where they matter.

Reliable workflow automations with LLM steps where they add value, deterministic logic everywhere else, and proper observability.

Common signs your team is overdue for ai workflow automation:

  • Teams copying data between CRM, email, spreadsheets, and Slack all day
  • High-volume, low-judgment tasks eating headcount (lead routing, document tagging, status updates)
  • Drafts that should take seconds (replies, summaries, briefs) take hours
  • Existing Zapier/Make flows breaking silently — no observability, no tests

What we build for ai workflow automation:

  • n8n / Make / Zapier orchestration — self-hosted when needed
  • LLM nodes for drafting, classification, extraction, summarization
  • Human-in-the-loop approvals via Slack, Teams, or email
  • Failure handling, retries, idempotency, and audit logs
  • Cost dashboards and alerts so spend can’t surprise you
Talk to an engineer

Capabilities

Where it pays off fastest

Reliable, observable automations — outcomes our clients keep coming back for.

Inbox triage

Classify, summarize, and draft replies for shared inboxes — humans approve before send.

Invoice & document handling

Extract structured data from PDFs, route exceptions, post into your accounting system.

Lead enrichment & routing

Enrich inbound leads, score them, assign to the right rep with a personalized follow-up draft.

Support deflection

Auto-draft replies grounded in your help center; escalate edge cases with full context attached.

How we deliver

Discovery → ship in 4 weeks

01

Map the work

Watch the actual process. Identify the 20% of steps causing 80% of the toil.

02

Design with guardrails

Decide what AI drafts vs. what AI decides. Define HITL checkpoints.

03

Build & dogfood

Ship one workflow end-to-end. Real users, real data, week two.

04

Roll out

Add the next workflow. Train the team. Hand over runbooks.

Tools & platforms we use:

n8n Make Zapier OpenAI Anthropic Slack Microsoft 365 HubSpot Salesforce Airtable Postgres Langfuse

FAQ

Questions teams ask us about AI Workflow Automation

Should we use n8n, Make, or Zapier?
Depends on volume, data sensitivity, and team. Zapier for the simplest cases; Make for richer flows; n8n (often self-hosted) when you need full control or have data residency rules. We’ll recommend in discovery.
How long does it take to get to production?
Most projects ship a real, usable system in 3–6 weeks. Discovery is 1–2 weeks; build sprints are weekly with demos.
Will my data be used to train models?
No. We default to enterprise tiers (OpenAI, Anthropic, Bedrock, Vertex) that don’t train on your data. For sensitive use cases, we deploy open-weight models on your infrastructure.
How do you control costs?
We design cost-aware from day one — model routing (cheap model first, escalate when needed), caching, batch processing, and per-user budgets with alerts.
Can you work with our existing engineering team?
Yes. We embed alongside your team, transfer ownership progressively, and document everything we build.