Automation & Orchestration Services
Remove the manual steps. Keep the control.
If a person clicks the same buttons, copies the same data, or sends the same emails every day, that is work a machine can do. We build a working pilot of the automation and give you numbers on what it saves before you commit to a full build.
What we pilot
Anywhere a person clicks the same buttons, copies the same data, or sends the same emails every day, there is work a machine can take over. These are the patterns we see most often.
- End-to-end workflows. Every step connects to the next, so nothing sits waiting for a human to move it across from one system to another.
- Chatbots for WhatsApp, Instagram, and Telegram. They resolve recurring conversations directly and escalate the rest to a real person with enough context that the handoff is useful.
- ETL pipelines. Extract, transform, and load between systems on the cadence your reports actually need, instead of on whatever schedule was easiest to set up.
- Web scraping and public data collection. Running with caching and rate limiting so sources don't block you, and with monitoring so you hear when a site layout changes.
- Notification and escalation flows. Alerts reach the right person and skip the ones who do not need to see them, so the signal stays useful.
Technologies
Automation breaks when the stack is too clever. We keep the orchestration simple, monitor every step, and reach for managed services wherever they are cheaper than operating our own.
- Messaging APIs. WhatsApp Business, Telegram Bot, and Instagram Messaging, with the right message templates approved ahead of launch so compliance is not a surprise.
- Orchestration. Apache Airflow for heavy data pipelines, and custom code when the logic does not fit comfortably.
- Browser automation. Selenium and Playwright with headless runs, structured error recovery, and retries for the pages that fail intermittently.
- Serverless. Cloud Functions, Lambda, and managed schedulers for jobs that run on a cadence and need to stay cheap as volume grows.
- Storage. S3, Cloud Storage, and file-based pipelines when the source system speaks in files rather than in APIs.
How we'd work on this
A common situation
Every Monday morning, someone exports sales data from Salesforce, opens the CSV in Excel to clean a few columns, runs a pivot, and uploads the result into the BI tool so leadership sees it before the standup. It takes two hours and breaks the moment a source column gets renamed.
How we'd approach it
Replace the weekly ritual with an ETL job. Extract from the Salesforce API on a schedule, transform the data in code with explicit column mappings and validation rules, and load the result into your BI warehouse. Add alerts so the pipeline yells when something shifts instead of silently producing a stale dashboard.
What you'd get
A working pilot of the automated report, a technical plan for what it would take to scale it, and an audit log showing the pipeline ran as expected.
Questions about business process automation
We start by mapping the current process with the person who runs it today and pinpoint where the time is lost (re-entry, copying between systems, waiting). From there we pick the level of automation that controls the cost: automate the repetitive core, leave rare edge cases for a person, and add monitoring so you hear about breakages instead of discovering them at month-end. The diagnostic phase locks the scope before we build the pilot, so you see the price and the payoff before any commitment.
Yes. We build a working pilot on top of the official WhatsApp Business API, with pre-approved message templates for compliance. It resolves recurring conversations (order status, invoice reissue, opening hours) and escalates to a human with full conversation context when the question goes outside its scope. You leave the engagement with a running pilot and a technical plan for rolling it out to production volume, so you can decide the next step before committing to a full build.
Airflow for heavy data pipelines with complex task dependencies. Custom code when the logic does not fit comfortably in a pipeline framework, which happens more often than you would expect.
It depends on the source and the data. robots.txt is a crawling convention, not a complete legal test. We assess each collection workflow against source terms, access controls, privacy, copyright, and applicable law before we implement it. When personal data is involved, that includes Brazil's LGPD. We also use caching, rate limiting, and monitoring so we do not overload the source.
Typically 1-2 weeks, from mapping to a working pilot. That includes API extraction, transformation in code with validation rules, and a test run to confirm the pipeline produces the expected output.
Eventually, yes. That is why we add per-step monitoring and alerts that fire when something moves. We also prefer APIs over scraping or browser automation whenever possible, because APIs change with notice and web pages change without.
Our differentiators
- Working pilot on real data before any long-term decision
- No lock-in: you keep all code and documentation
- Projects start in days, not weeks
Let's talk about your case
Talk to the Lab
Tell us the process or the idea in a few lines. What happens today, or what you want to try. We reply the same business day.
What happens next
- We reply the same business day
- Diagnostic in 1-2 weeks
- Working pilot in 2-4 weeks, technical plan in 1 week
Start here