Automation & Orchestration Services
A job you can see fail
The work runs on a schedule, and you can see that it ran. A failure sends an alert. You find out from the alert, not from a wrong number days later.
Examples
The engineering problem, in a few shapes. A schedule, a dependency, and a failure that has to be visible.
- 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.
- 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.
- 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 technical situation
The payload shape changes. The pipeline keeps running, and the number goes out wrong.
How we'd approach it
We schedule the job, validate the payload before the load, and alert when a run fails. Airflow or plain code, picked for the dependency. An API when the system has one.
What you'd get
You leave with the job running, the code in a repo you own.
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 locks the scope before we build the fix, so you see the price and the payoff before any commitment.
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 the GDPR. We also use caching, rate limiting, and monitoring so we do not overload the source.
Typically 1 to 2 weeks, from the map to the report running, when the file and the owner are already available. That is the small end of a 2 to 5 week project. 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.
What you get
- A working fix on your data
- You keep the code
- 2 to 5 weeks when the owner and the files are available
Let's talk about your case
Talk to the Lab
Tell us the problem in a few lines. What the team does today, and where it breaks. We reply the same business day.
What happens next
- We reply the same business day
- Diagnostic in 3-5 days
- Working fix in 1-3 weeks. Technical plan in the last days, with the work done.
Start here