RPA where it actually fits.
Honest advice where it doesn't.

RPA (robotic process automation) uses software bots to mimic human actions on a computer screen: clicking, typing, copying data, filling forms. It works at the interface level, operating software the way a person would, rather than through a programmatic integration. This makes it different from API integration (which connects systems directly and is more reliable but requires the systems to have APIs) and from AI automation (which handles judgment and unstructured data). RPA’s specific strength is automating manual work on systems that don’t have APIs, particularly legacy systems. That’s where it genuinely earns its cost.

The key question is whether the systems involved have APIs. If a system you need to automate has a usable API, proper integration is almost always better than RPA: more reliable, cheaper to maintain, and it won’t break when a screen layout changes. If the system is a legacy application with no API, where a human currently has to operate it manually, RPA is often the right answer. Most real processes are a mix, with some steps needing bots and others better served by integration. We assess this honestly before recommending anything, which is the opposite of how a platform vendor approaches it.

We do build RPA, where it fits. But we’re a product engineering team, not a bot reseller, so we have no incentive to push bots where they don’t belong. The most expensive and common RPA mistake is using bots to screen-scrape between systems that could have been integrated properly. Those bots are fragile, expensive to maintain, and break constantly. Being honest about where RPA fits and where it doesn’t is exactly what makes us a better partner for it. You end up with automation that works and costs less to run.

Yes, this is a common situation and one we specifically address. Fragile, high-maintenance RPA deployments usually share a few causes: bots doing work that integration should handle, poor exception handling, and brittle screen-scraping. We assess your deployment, identify what’s salvageable, fix what can be fixed, and re-architect what can’t. Often this means replacing unnecessary bots with proper integration, which reduces your ongoing maintenance burden significantly. The goal is automation that’s reliable and affordable to run, not just patching the bots you have.

More than vendors usually admit. Because bots operate at the screen level, they break when the software they interact with changes: an updated interface, a moved button, a changed field. The maintenance burden depends on how often the target systems change and how the bots were built. We design bots to be as resilient as possible and are upfront about the realistic maintenance cost before you commit. We also look for opportunities to replace fragile bots with more durable integration where it makes sense, reducing the long-term burden.

Where a commercial RPA platform fits, we work with the major ones, including UiPath, Automation Anywhere, and Microsoft Power Automate. But the first question is whether you need a platform at all. For some situations, particularly smaller or more targeted automation needs, a full enterprise RPA platform is more than necessary and a more lightweight approach fits better. We recommend based on your actual scale and needs, not based on a vendor relationship. We’ll tell you honestly when a platform is worth it and when it’s overkill.

Pure rule-based RPA handles structured, predictable tasks. For documents and unstructured data, RPA alone struggles, because it follows fixed rules and unstructured inputs vary. The modern approach combines RPA with AI: document intelligence and LLMs read and interpret the unstructured content, and the automation acts on the extracted data. We build this combination, which handles document-heavy and variable processes that pure RPA can’t. If your process involves a lot of unstructured documents, this combined approach is usually what you actually need.

RPA is often faster to deploy than deep integration projects, which is part of its appeal, because it sits on top of existing systems rather than requiring changes to them. A focused RPA automation for a well-defined process typically takes a few weeks. More complex automations, or ones combining RPA with integration and AI, take longer. The assessment and design phase matters, because building the wrong automation fast is worse than building the right one properly. We scope clearly before starting.

Carefully, because exceptions are where RPA most commonly fails. Bots follow rules, and exceptions by definition don’t fit the rules. We map the exceptions during the design phase, then build explicit handling: some exceptions get automated handling, some route to a human, some use AI to interpret and decide. A bot that only handles the standard case and breaks on every exception isn’t production-ready. Designing for exceptions is a large part of building RPA that actually works in production.

You do. The bot configurations, the integration code, the automation logic, and the documentation all belong to you. Where we build on a commercial RPA platform, you hold the platform relationship and licenses directly. There’s no dependency on us to keep your automation running. We design for your team to be able to maintain and extend it, and document accordingly. We can stay involved for ongoing support and optimization, but you’re never locked in.

Attended bots run alongside a person, triggered by human activity and assisting with a task in real time. They’re useful when a human stays in the loop, for example a bot that pulls up customer information while a support agent is on a call. Unattended bots run independently, on a schedule or a trigger, with no human involvement, handling high-volume back-office processing like overnight data transfers or batch report generation. Most organizations use a mix. We assess your specific processes and recommend the right type for each, rather than defaulting to one. The choice affects both the cost and the design, so we get it right during scoping.

This matters more for RPA than for most software, because bots break when the systems they operate change. We deploy with monitoring that tracks each bot’s performance, success and failure rates, and exceptions. When a bot fails, the monitoring flags it before it causes downstream problems, and the automation is designed to fail safely, for example by routing the work to a human queue rather than silently dropping it. We also track patterns over time, so if a bot is starting to fail more often because a target system is changing, we can address it before it breaks completely. Realistic monitoring and incident handling is part of what separates RPA that works in production from RPA that becomes a liability.

Bots often need credentials and access to systems, which makes security a real consideration. We design with proper credential management (bots use secured, managed credentials rather than hardcoded passwords), role-based access so each bot can only reach the systems it genuinely needs, and full audit logging so every action a bot takes is recorded. For regulated industries, that audit trail is often a compliance requirement, and it’s also one of RPA’s genuine benefits: bots execute the same steps the same way every time, which produces consistent, auditable records. We build these controls in from the start rather than adding them after a security review flags them.