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 workflows that connect steps without manual intervention and chatbots that handle recurring conversations.
What we build
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, n8n for low-code flows, and custom code when the logic does not fit either 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
The Monday report lands on its own, with an audit log for every run. You get your time back, a schema that survives Salesforce updates, and baseline numbers on how often the source data actually changes shape.
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 prototype, so you see the price and the payoff before any commitment.
Yes. We build a working prototype 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 prototype and a technical plan for rolling it out to production volume, so you can decide the next step before committing to a full build.
n8n for low-complexity flows where a visual interface is worth the trade-off. Airflow for heavy data pipelines with complex task dependencies. Custom code when the logic does not fit comfortably in either, which happens more often than you would expect.
It depends on the content and the site. Public data from sites that do not prohibit it via robots.txt or terms of use is legitimate. We scrape with caching and rate limiting so we do not overload the source, and with monitoring that fires when the layout changes.
Typically 1-2 weeks, from mapping to the report running on its own. That includes API extraction, transformation in code with validation rules, loading into the BI tool, and alerts for when the source data shifts format.
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 prototype 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
Describe the challenge in a few lines. We'll get back to you to discuss next steps.
What happens next
- 30-min call, no commitment
- Diagnostic in 1-2 weeks
- Working prototype in 2-4 weeks, technical plan in 1 week
Start here