Commerce · Starn22
Invoice intake and e-invoicing
OCR invoice ingestion with LLM extraction, plus SII electronic invoicing with XML signatures and SOAP, for a commerce platform covering wholesale, in-store POS and consumer ordering.
What to automate
Work AI can take off your team
If someone does it many times a week, the same way, and it involves reading or writing, it's a good candidate.
Invoices and documents
OCR plus LLM extraction turns supplier invoices, PDFs and forms into clean, validated records in your system.
Data entry and enrichment
Parse resumes, emails and spreadsheets, fill in the missing fields, and keep records consistent and searchable.
Customer conversations
First-line replies on chat and voice that look up the real answer, and pass the hard cases to your team with context.
Ordering and operations
Turn sales and stock signals into draft purchase orders and tasks that someone approves with one click.
Compliance and paperwork
Generate and send regulated documents automatically, such as electronic invoices with XML signatures for tax systems.
Scheduled and event-driven jobs
Background jobs that run on a schedule or react to events, with queues and retries so nothing gets lost.
Proof
Automation I've built and run
Real systems for real businesses, in production.
Operations · Starn22
Automated purchase orders
An AI agent turns POS sales velocity and supplier invoices into draft purchase orders, with human approval before anything is sent.
Recruiting · Baysten
Resume parsing and enrichment
LangChain and OpenAI enrichment services with schema-validated output, Algolia search and scheduled jobs that keep profiles current.
AI data · DataLeon
Human-in-the-loop review
A React and TypeScript editor where people correct model output, including a keyboard-accessible bounding-box tool for OCR pipelines. This is the review step that makes automation trustworthy.
Fintech · P2P lending
Event-driven processing
Python and AWS Lambda event processing and partner REST APIs behind high-traffic lending workflows.
How it works
From a manual process to a reliable pipeline
Map the process with real examples
We walk through how the work is done today and collect real inputs: the invoices, emails or records your team handles.
Split it into AI steps and code steps
AI reads and decides where the input is messy; ordinary code moves, checks and stores data where it isn't.
Validate everything the model produces
Structured outputs checked against a schema, so a bad extraction is caught instead of saved.
Route the unsure cases to a person
A simple review screen for anything low-confidence or high-stakes, and corrections that feed back into the system.
Run it like production software
Queues and retries with SQS and EventBridge, Lambda or containers, and monitoring so you know it's working.
Stack
Tools I automate with
PythonFastAPIDjangoFlaskNode.jsOpenAIAnthropic ClaudeLangChainStructured outputsOCRAWS LambdaEventBridgeSQSS3PostgreSQLMongoDBRedisAlgoliaBackground jobsREST APIsSOAP · XMLDockerGitHub Actions
Questions
AI automation: FAQ
What is AI automation?
AI automation uses language models to handle steps that used to need a person because the input was messy: reading an invoice, sorting an email, enriching a record, answering a routine question. Classic automation moves data between systems; AI automation also understands it. The two work best together.
Which business processes are worth automating with AI?
Ones that happen often, follow a pattern, and involve reading or writing text: invoice and document intake, data entry from emails or PDFs, enriching leads or candidates, first-line customer replies, and turning raw data into a report. If a person does it many times a week the same way, it is a good candidate.
Is AI automation reliable enough for real business work?
When it is built to be checked. Every AI step has its output validated against a schema, low-confidence results go to a person in a review screen, and corrections are kept so the system improves. Retries, queues and monitoring handle the failures any production system has.
Do you use no-code tools or write custom code?
Custom code, in Python or Node.js, running on your cloud or mine. That costs a little more up front than a no-code workflow, but it handles volume, edge cases and sensitive data properly, and you own it.
How do we start?
Send me the process you want to automate and a few real examples of its inputs. I will tell you which parts AI can handle, which still need a person, and how I would build it, before any build starts.
What should stop being manual?
Send me the process and a few real examples. I'll show you what AI can take over and what stays with your team.