AI Automation
We identify the manual, rule-bound tasks eating your engineering team's time and automate them, using the right mix of traditional automation, RPA, and AI where it actually adds value.
What we do
AI automation isn't about replacing your team, it's about removing the work that slows them down. Data entry that takes 2 hours per day. Reports that nobody reads but everyone compiles. Approval workflows that sit in inboxes. Customer emails that get the same answer 80% of the time. We map these processes, assess automation potential, and build systems that handle them, freeing your team for the work that actually requires judgment.
What we automate
Map and automate multi-step business processes that currently require manual handoffs, status updates, or routing decisions.
Extract structured data from unstructured documents, invoices, contracts, forms, emails, using OCR and classification models.
Customer-facing or internal chatbots connected to your knowledge base, CRM, or ticketing system. Built to handle real use cases, not demos.
Automated code quality checks, test generation, PR summaries, and regression detection integrated into your CI/CD pipeline.
Replace manual report compilation with automated pipelines that pull, transform, and deliver data to the right people on schedule.
Smart alerting systems that reduce alert fatigue by filtering noise, correlating incidents, and routing the right notifications to the right person.
Robotic Process Automation for software that doesn't have an API, screen scraping, form filling, data migration between old systems.
Automate repetitive ops work: environment provisioning, deployment scripts, access management, log parsing, and incident response runbooks.
Our tools
Real examples
Use case 01
A logistics company was processing 1,200 invoices per month manually. We built a document pipeline using OCR and a classification model that extracts vendor, amount, line items, and approval routing automatically. Manual processing time dropped from 3 minutes to 15 seconds per invoice.
Use case 02
A SaaS company's support queue was spending 60% of time on the same 15 question types. We built a classification model that routes inbound tickets and auto-generates first drafts for common issues. First-response time dropped from 4 hours to under 30 minutes.
Use case 03
Automated a 22-step manual onboarding process for new engineers: repo access, Jira setup, Slack channels, tool provisioning, and first-day task assignment. Time from offer accepted to day-one ready: from 2 days to 40 minutes.
Use case 04
Finance team spent 6 hours every Monday pulling data from 5 systems into a board report. We built an Airflow pipeline that assembles the report automatically and sends it at 8 AM every Monday. Zero human hours.
Our process
step 01
We spend the first week documenting your highest-cost manual processes. We quantify the time spent, error rate, and business impact for each.
step 02
Each process gets an automation score based on rule-clarity, data availability, and expected ROI. We prioritize the highest-value targets.
step 03
We design the automation solution and choose tools based on your existing infrastructure, we don't introduce unnecessary new dependencies.
step 04
We build in 2-week sprints with real data from the start. Each automation is validated against edge cases before it touches production.
step 05
Full documentation, monitoring dashboards, and a 30-day stabilization window. We measure actual time saved against the pre-automation baseline.
What clients see
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Avg. time reduction on automated tasks
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Avg. hours saved per team per month
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Automation reliability after stabilization
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Avg. time to first measurable ROI
FAQ
Find your first win
In a 30-minute call, we can usually identify 2 to 3 high-value automation targets and give you a rough scope.
