Services · AI Workflow Automation
AI workflows that
keep running.
Multi-step business workflows where LLMs do the reasoning and your team handles the exceptions. Built with eval suites, observability, and human-in-the-loop gates where they matter.
What I automate
Lead generation + outreach
Scrape sources, enrich with LLM-driven research, qualify, draft personalized outbound, send via your sequencer. Built this pattern for VC Automation.
Candidate screening
Parse CVs, score against role criteria, surface top candidates with reasoning, escalate to recruiters with override capability. Built for Xandidate.
Document processing
Extract structured data from PDFs/contracts/forms. Classify, normalize, route. Hand off uncertain items to a reviewer queue.
Customer support triage
Read incoming tickets, classify by intent and urgency, draft responses, route hard ones to humans, auto-resolve easy ones.
Sales enablement
Auto-research prospects before calls, draft proposals from playbooks, post-call summaries into CRM with action items.
Back-office operations
Invoice categorization, expense routing, vendor matching, recurring task automation - quietly powerful once it runs.
The pieces that come standard
Eval suite
A labelled set of real inputs + expected outputs. Run before every prompt or model change. Prompts stop being a guessing game.
Observability
Per-run logs, latency, token cost, model attribution. Dashboard so you actually know what the AI is doing.
Human-in-the-loop
Approval queues, confidence routing, override capture. Pattern catalog in the HITL guide.
Retry + fallback
Idempotent steps, dead-letter queues, model failover via the Vercel AI Gateway. Workflows survive flaky providers.
Pricing
| Scope | Timeline | Price |
|---|---|---|
| Single-step LLM-augmented flow (classification, drafting, summarization) | 1-2 weeks | $3.5K-$12K |
| Multi-step workflow with HITL and observability | 3-5 weeks | $15K-$30K |
| End-to-end automation platform (CRM-integrated, multi-pipeline) | 5-8 weeks | $25K-$60K |
| Hourly retainer for ongoing iteration | Ongoing | On request |
What this looks like in practice
Worked examples of builds like this: the problem, the workflow, what the AI does and where people stay in the loop.
A Monday KPI brief that explains last week's numbers instead of just charting them
Computes last week's KPIs in SQL, checks the data is complete, finds what moved and why, and posts a short written brief to Slack at 07:00 every Monday.
Shopify / BigQuery or Google Sheets / GA4 and the ad platform APIs / HubSpot / Accounting and payments
An agent for the shared inbox: every email sorted, answered or filed into the right system
Reads every email in your shared mailboxes, sends orders and invoices into the right system, drafts replies for approval, and flags fake bank-detail changes.
Microsoft 365 shared mailboxes / Gmail and Google Workspace / The ERP (Business Central in this example) / Teams or Slack / The AP flow and accounting system
Commercial insurance submissions assembled from proposal forms, loss runs and property schedules
Reads a client's proposal forms, loss runs and property schedules, reconciles them, and drafts one submission per insurer for the account handler to check and send.
Broker management system (Acturis, Applied Epic, Vertafore) / Outlook / Excel schedules of values / Insurer portals and e-trading / BiPRO interfaces (Germany)
Customer orders from email, PDF and WhatsApp, entered into the ERP without retyping
Turns emailed PDFs, Excel forms and WhatsApp photos into sales orders in SAP Business One or NetSuite, and sends only the doubtful lines to a rep.
Outlook and Microsoft 365 / WhatsApp Business Platform / SAP Business One / NetSuite / Excel order forms
Every inbound lead researched, scored and routed before a rep opens it
Researches and scores every inbound lead against your rubric, catches existing customers, routes by territory and capacity, and replies with a booking link.
HubSpot / Salesforce / Enrichment provider (Clay or Apollo) / Cal.com or Calendly / Slack
Fixing n8n, Zapier and Make automations that report success and did nothing
Audits inherited n8n, Zapier and Make automations, then adds a run ledger, idempotency keys and a daily reconciliation so a green run means the work was done.
n8n / Zapier / Make / Postgres / Slack
Frequently asked questions
What is AI workflow automation?
Automating a multi-step business process - like lead research, candidate screening, or document review - using LLMs to handle the parts that previously required human judgment. Different from simple Zapier flows because the steps involve reasoning, not just data movement.
Which workflows are good candidates?
Anything high-volume, low-stakes-per-item, and pattern-based: lead enrichment, outbound email drafting, candidate screening, invoice categorization, customer ticket triage, content moderation. Bad candidates: regulated decisions, anything irreversible without human review.
Do you build on n8n, Make, Zapier, or custom code?
Depends on the workflow. Simple cron-style flows live well on n8n. Anything with serious LLM logic, state, or eval needs lives in custom code - usually Next.js + Vercel Workflow or a worker queue. I will recommend honestly.
How do you handle the AI failing or hallucinating?
Every workflow has fallbacks, retries, and human-in-the-loop gates where wrong outputs cost real money. See the human-in-the-loop AI guide.
How much does AI workflow automation cost?
Simple LLM-augmented flows start at $3.5K-$12K. Full multi-step pipelines with eval, observability, and HITL run $15K-$45K. Ongoing optimization on an hourly retainer.
How long does it take to ship?
Two to six weeks depending on integration count and human-loop complexity. First working version typically in week 2 so you can see the system run on real data.