AI Automation
Stop paying people to do what software should do.
Document processing, workflow automation, intelligent routing, and reporting pipelines. Built to replace real manual work.
Free, no commitment. Takes about 30 seconds.
What we automate
AI automation use cases
Document processing
Extract, classify, and route data from invoices, contracts, applications, and forms. Replace manual data entry.
Workflow automation
Trigger actions based on ML classifications. Route tickets, approve requests, flag exceptions: without human review.
Automated reporting
Generate scheduled reports, summaries, and alerts from raw data. Replace analyst time on recurring work.
Intelligent alerting
Replace threshold-based alerts with ML models that distinguish real incidents from noise. Fewer false positives.
Data enrichment pipelines
Automatically classify, tag, and enrich incoming data records before they hit your database or CRM.
Process monitoring
Detect anomalies in operational processes: production lines, financial transactions, service delivery.
Stack
What we build automation with
Client projects
What our teams have automated
Use case 01
Invoice processing automation
A logistics company processed 2,000 invoices per month manually. The engineers we placed built a CV + NLP pipeline that extracted line items, matched them to POs, and flagged discrepancies. Manual review time dropped by 78%.
Use case 02
Support ticket routing
An enterprise SaaS company had tier-1 support manually categorizing and routing 10,000 tickets per month. The team we placed built a classifier that routed 84% of tickets automatically with 93% accuracy.
Process
How we identify and automate workflows
step 01
Process mapping
We document the manual workflow: inputs, outputs, decision points, and exception handling. This is the spec for the automation.
step 02
Data and ML design
We identify which steps require ML (classification, extraction) vs. rules, and design the data flow between components.
step 03
Build and test against real cases
We build the automation and test it against historical examples: including edge cases and exception types.
step 04
Deploy with exception handling
Production deployment with human-in-the-loop fallback for low-confidence cases. Monitoring tracks automation rate and accuracy.
Before you decide
The doubts everyone has before hiring us.
And what we do about each one.
AI projects never make it past the prototype.
Your data is assessed in the first week, and you get an honest answer on whether there is enough signal to build something useful.
Data assessment in week oneThis will just be a chatbot on top of an API.
Language models are used when they fit, like text and language tasks.
The right tool for the problemOur data is too sensitive to leave our infrastructure.
Model training can run entirely in your cloud, so your data never leaves your infrastructure.
We'll end up locked in to you.
Trained weights, training code, data pipelines and all documentation transfer to you on delivery.
Models and code are yours
FAQ
Common questions
Find your first automation
What's the most expensive thing your team does manually?
What happens next
- 1
You tell us what you need. It takes about 2 minutes.
- 2
A real person replies within 4 business hours.
- 3
A 30-minute call to understand the project and agree on next steps.