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Enterprise Systems

Robotic Process Automation Services

Software bots that drive your existing applications like a person (clicking, typing and copying between systems that have no API), to take the high-volume, rules-based data entry off your team. We are honest about the catch: a bot depends on the screens it drives, so it breaks when they change. We build one where it genuinely earns its place, and tell you when a real integration is the right answer instead.

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What Robotic Process Automation means in practice

Who it’s for: Back-office teams drowning in high-volume, rules-based data entry between old systems that have no APIs and cannot be changed (where the choice is realistically a bot or more headcount), and who want an engineer who will tell them honestly when a real integration would serve them better instead.

Robotic Process Automation is a piece of software (a "bot"), that operates your existing applications the way a person does: it opens the screen, clicks the button, reads the field, types the value, and copies data from one system into another. It does not touch the underlying code or databases; it drives the user interface. That is the entire appeal and the entire weakness in one sentence. Because it works through the UI, an RPA bot can automate a task that spans a mainframe from 1998, a desktop application nobody has the source for, and a web portal a supplier controls: three systems that were never designed to talk to each other and that you cannot change, without integrating any of them. And because it works through the UI, the moment one of those screens changes. A field moves, a login page gets a new step, a portal is redesigned. The bot breaks, because it was following a script of clicks that no longer matches what is on the screen.

We build RPA with UiPath, Automation Anywhere and Microsoft Power Automate, in both flavours: attended bots that sit on a person’s machine and take over a repetitive stretch of their work when they trigger it, and unattended bots that run on their own on a schedule, orchestrated across a queue of work with no human at the keyboard. Done well, this genuinely removes drudgery: the daily grind of rekeying invoice data from a PDF into an accounting package, or reconciling two systems that hold the same records in different shapes, and it removes the transcription errors that come with humans doing that at volume. We are good at it. But being good at RPA, for us, mostly means being honest about when not to use it.

Here is the honesty, up front, because it is the thing that separates a bot that pays for itself from one that becomes a maintenance liability. RPA is very often a brittle band-aid. It sits on top of the problem rather than fixing it. Wherever a proper API or a real integration exists (or could be built), that path is almost always more robust than a bot pretending to be a human, because it talks to the system in a stable, supported way rather than reading pixels off a screen that can move. And automating a broken process does not fix the process; it just makes the mess happen faster and more reliably. So we treat RPA as a pragmatic bridge, not a destination: the right tool for automating across old systems that cannot be changed and have no API, and the wrong tool almost everywhere else. This service sits alongside our broader business-automation work, where the first question is always whether a bot is the right instrument at all.

What you get

  • A blunt assessment first, is a bot actually the right tool here, or is there an API or integration that would be more robust? We will tell you before you spend a penny on automation, and sometimes the honest answer is that RPA is the wrong fit
  • The process mapped and, where needed, cleaned up before it is automated, because automating a broken or needlessly convoluted process just entrenches it, so we fix or simplify the steps that do not make sense before a bot ever touches them
  • Attended or unattended bots built in UiPath, Automation Anywhere or Microsoft Power Automate (chosen on what you already run and licence, not on our preference), driving your existing applications to do the rules-based work
  • Orchestration for unattended bots: work queues, scheduling, retries and the logic that decides which bot picks up which item, so a fleet of bots runs reliably rather than as a fragile collection of one-off scripts
  • Robust exception handling and logging, because a UI-driving bot will hit the unexpected. A slow screen, a pop-up, a record that does not fit the rule, and it must fail safely and visibly rather than silently corrupting data
  • Secure handling of the credentials and sensitive data the bot touches, passwords in a proper vault, access scoped to exactly what the bot needs, and an audit trail of what it did, because a bot logging into your systems is a privileged account you have to govern
  • Honest documentation of the bot’s fragility and its maintenance burden, which screens it depends on, what will break it, and roughly what it will cost to keep running, so you go in knowing the ongoing commitment, not just the launch

What Robotic Process Automation does for you

  • Automating across systems you are not allowed to change

    The genuine, hard-to-replicate value of RPA is that it works from the outside. When a task spans a mainframe, a desktop application with no source, and a third party’s web portal: none of which you can modify, and none of which expose an API. A bot driving their screens is often the only practical way to connect them without a multi-year replacement programme. That is a real capability, and where it applies it can remove an enormous amount of manual toil quickly, without the risk and cost of changing the underlying systems.

  • Speed and consistency on rules-based volume

    For work that is genuinely rules-based and high-volume (the same fields, the same steps, hundreds or thousands of times), a bot does not get tired, distracted or bored, so it does not make the transcription slips a human inevitably makes at that volume, and it runs at a steadier pace around the clock. Where the rules really are unambiguous, this is a legitimate quality improvement as much as a speed one: fewer keying errors flowing downstream into the systems that depend on that data.

  • A tactical bridge that buys you time

    Sometimes the right long-term answer (a new system, a proper integration), is a year away, and the team cannot wait a year. A bot can be stood up in weeks to relieve the immediate pressure and keep the process running while the real fix is built properly on a sensible timeline. Used deliberately as a temporary bridge, with eyes open about its fragility, RPA lets you avoid both the pain of the status quo and the risk of rushing the permanent solution.

Why teams choose us for Robotic Process Automation

  • You want an engineer who will tell you honestly when RPA is the wrong tool, who will point you at an API or a real integration when one exists, even though building a bot would be easier and more billable for us, rather than a vendor whose only product is bots and who therefore sees a bot-shaped problem everywhere.
  • You want the process fixed before it is automated, not enshrined, because we treat a convoluted or broken workflow as something to simplify first, so the bot automates a sensible process rather than immortalising a bad one at machine speed.
  • You want bots built to survive reality, with the resilient selectors, exception handling and safe failure modes that separate an automation that keeps running from one that breaks on the first unexpected pop-up and quietly corrupts your data.
  • You want the credentials and sensitive data the bot handles governed properly (vaulted secrets, least-privilege access and an audit trail), by people who treat a bot’s login as the privileged account it is, not an afterthought bolted on after go-live.

What Robotic Process Automation includes

The concrete pieces of work this covers, scoped to what your problem actually needs.

  • Honest tool selection, bot, integration, or neither

    Before any building, we work out what should actually remove the manual work. If an API exists or could be added, we will usually recommend an integration over a bot, because talking to a system in a supported way is more robust than driving its screens, and we will say so even when it means a smaller RPA engagement for us. If the systems are genuinely closed and API-less, a bot is the right call and we build one. And sometimes the honest answer is that the process should be simplified or the volume does not justify automation at all. This assessment is the most useful thing we do, and it is where our incentives and yours are deliberately aligned.

  • Attended and unattended bots

    We build both shapes. An attended bot lives on a person’s machine and takes over a repetitive stretch of their work when they trigger it: useful for tasks that still need a human in the loop for judgement, with the bot handling the mechanical parts around them. An unattended bot runs on its own, on a schedule or off a queue, with no human present: the right fit for high-volume back-office batches that can run overnight. We choose the shape from the work, not the other way round, and we are clear about which parts of a process genuinely need a person and which can safely run unattended.

  • UiPath, Automation Anywhere and Power Automate

    We work across the main platforms and pick on your circumstances, not our habits. Microsoft Power Automate is often the pragmatic choice when you are already in the Microsoft ecosystem and licensing it costs little extra. UiPath and Automation Anywhere are the heavier, more capable enterprise platforms, with stronger orchestration for larger fleets of unattended bots. The right one depends on what you already run and licence, the scale of the automation, and who will maintain it, and we would rather build on the tool your team can live with than the one with the glossiest brochure.

  • Orchestration for fleets of bots

    A single bot is a script; a reliable automation at scale is an orchestrated system. We build the work queues that hold the items to be processed, the scheduling that decides when bots run, the assignment logic that shares work across bots, and the retry behaviour for the items that fail transiently. This is what turns a handful of fragile individual bots into something that runs a back-office process dependably, with visibility into what has been processed, what is pending, and what has been kicked out to a human because it did not fit the rules.

  • Exception handling and resilient design

    The whole game in durable RPA is what happens when the screen is not what the bot expected. A slow load, an unexpected pop-up, a session timeout, a record that breaks the rule. A naive bot ploughs on and corrupts data; a well-built one detects the anomaly, fails safely, logs exactly what it saw, and either retries sensibly or routes the item to a human. We design selectors to be as robust as the platform allows so cosmetic UI changes do not break them, and we build the exception paths deliberately, because in RPA the happy path is the easy ten percent and the edge cases are the ninety that decide whether the thing survives.

  • Handing the unstructured steps to AI

    The classic wall RPA hit was anything unstructured, reading a PDF invoice whose layout varied, interpreting a free-text email, deciding a case that did not fit a clean rule. Bots are rigid and those steps are not, so historically a human had to bridge them. Increasingly, AI handles exactly that: document understanding that reads a varied invoice, models that classify or extract from messy text, so the bot can hand off the fuzzy judgement and get a structured answer back to act on. Combined sensibly, this widens what is automatable beyond strictly rules-based work, and it is a live part of how we think about these engagements, drawing on our AI development work where it fits.

Where it fits

  • Invoice data entry from PDFs into a closed accounting system

    A finance team receives supplier invoices as PDFs and rekeys the line items by hand into an accounting package that has no import API and cannot be changed. The volume is high, the work is pure transcription, and the errors flow straight into the ledger. A bot, with AI reading the varied invoice layouts and the bot driving the accounting UI to enter the data, removes the rekeying and the keying errors, while routing anything it is not confident about to a person to check.

  • Reconciling two legacy systems that hold the same records differently

    Two old systems (say an order platform and a fulfilment system), hold overlapping records in incompatible shapes, and someone reconciles them daily by eye, flagging mismatches. Neither system exposes an API, and replacing either is years away. An unattended bot reads both, applies the matching rules, and produces the exceptions for a human to resolve, turning a full day of manual cross-checking into a short review of just the items that genuinely need judgement.

  • Bridging a process while a proper system is built

    A business is mid-way through commissioning a new platform that will eventually integrate everything properly, but the team is buckling under the manual process today and the new system is a year out. A deliberately temporary bot bridges the gap (automating the worst of the current toil now), with a clear understanding that it will be retired when the real integration lands. This is RPA used honestly for what it is best at: a pragmatic bridge, not a permanent fixture.

  • The case where we recommended not building a bot

    A common and instructive outcome: a client comes wanting to automate data flowing between two systems with a bot, and on inspection both systems have perfectly good APIs. Here the honest answer is that a real integration will be dramatically more robust than a bot driving screens that will eventually change, so we build, or recommend, the integration instead. It is less glamorous and sometimes more work up front, but it does not break every time a UI is redesigned, and steering clients here is exactly the judgement that makes the RPA we do build worth trusting.

How we approach Robotic Process Automation

We start every RPA engagement by trying to talk you out of it, because that is the most valuable thing we can do at the outset. The question is never "how do we automate this with a bot": it is "what is the most robust way to remove this manual work". If the systems involved have an API, or one could be added, an integration that talks to the system properly will almost always outlast a bot that drives its screens, and we will steer you there even though it is often a bigger, less flashy piece of work. RPA earns its place specifically when the systems are old, closed, and API-less, and changing them is off the table: a legacy ERP nobody will touch, a supplier portal you do not own, a desktop application whose vendor is long gone. That is the niche where a bot is genuinely the pragmatic right answer, and we are happy to build one there.

Once we have agreed a bot is the right tool, we fix the process before we automate it. Automating a convoluted, error-prone workflow just makes it convoluted and error-prone at machine speed, so we map what actually happens, strip out the steps that exist only because someone once did it that way, and settle the rules the bot will follow, including what it should do with the awkward cases, because those are what break automations. Then we build with the fragility designed in mind from the first line: resilient selectors, generous exception handling, clear logging, and a safe failure mode, because the difference between an RPA bot that survives contact with reality and one that becomes a support headache is almost entirely in how it handles the moments when a screen is not quite what it expected.

How the engagement runs

We open with the assessment that decides whether we should be building a bot at all. We sit with the process, watch what people actually do, and map the systems involved: crucially, whether any of them have an API or could realistically get one. If a robust integration is available, that is what we recommend, even when it means a smaller job for us, because a bot driving screens is a worse long-term bet than a proper connection wherever a proper connection is possible. Only when the systems are genuinely old, closed and unchangeable do we confirm RPA as the right tool. This front-loaded honesty is deliberate: it is far cheaper to decide correctly here than to discover six months in that the bot you built is a maintenance millstone that an integration would have avoided.

With a bot confirmed as the right instrument, we fix the process before automating it, simplifying the convoluted steps, settling the rules, and deciding explicitly what happens to the cases that do not fit, because automating a mess just industrialises the mess. Then we build for durability from the start: resilient selectors, thorough exception handling, clear logging and a safe failure mode, developed against the real screens the bot will drive. We test hard against the awkward cases, not just the happy path, because those are what break RPA in production. Finally we deploy, wire up the orchestration and monitoring, and hand over with clear documentation of what the bot depends on, what will break it, and what keeping it running will cost, so you own the automation and its maintenance reality with your eyes open, and know exactly which screen changes to warn us about before they land.

Bot design and orchestration

The design of a single bot is a careful description of a human’s interaction with a set of screens, and its robustness lives almost entirely in how it identifies the things it clicks and reads. A brittle bot locates a field by its position on the screen or a fragile attribute, and breaks the instant the layout shifts; a well-designed one uses the most stable selector the application and platform allow, so cosmetic changes do not floor it. Around that core interaction we build the exception handling that decides what the bot does when reality diverges from the script (a timeout, a pop-up, an unexpected value), because a bot with no answer to the unexpected does not just stop, it can carry on and corrupt data, which is far worse than failing cleanly. Every bot we build fails visibly and safely, and logs enough that you can see exactly where and why.

At scale, the architecture is less about any one bot and more about orchestration: the layer that manages a fleet of unattended bots against the work to be done. Items to process land in queues; a scheduler and assignment logic decide which bot takes which item and when; transient failures are retried and persistent ones are escalated to a human; and the whole thing is observable, so you can see throughput, backlogs and exceptions rather than trusting that the bots are quietly coping. This is the difference between an automation you can run a back-office function on and a collection of scripts that work until they do not. It is also where the honest cost of RPA shows up. The orchestration and the ongoing tending of it are real, and we would rather you saw that clearly than discovered it after go-live.

Credentials, sensitive data and governing a bot’s access

A bot that logs into your systems is a privileged user, and it must be governed like one. That starts with credentials: the passwords and keys a bot uses to sign into the applications it drives never live in the bot’s script or a config file, they live in a proper secret vault. The credential store built into UiPath, Automation Anywhere or Power Automate, or an external vault, and are fetched at run time, scoped tightly to the specific systems that bot needs and no more. A bot should have the least access that lets it do its job, so that if it misbehaves or its account is compromised, the blast radius is contained. And because bots often run under service accounts that can act at scale and at speed, that scoping matters more, not less, than it would for a human user who is inherently rate-limited by being one person.

The data a bot handles is frequently sensitive (invoices, personal records, financial figures moving between systems), so it is subject to the same protection as any other processing of that data: encrypted in transit and at rest, not written out to stray log files or screenshots, and handled in line with UK GDPR where personal data is involved. Alongside that, every bot we build keeps an audit trail: a record of what it did, to which records, and when, so that a bot’s activity is accountable and reviewable rather than an opaque process running in the background. A bot acting invisibly on sensitive systems is a governance risk; a bot whose every action is logged, whose credentials are vaulted, and whose access is least-privilege is a controllable one, and that is the only kind we are willing to put into production.

Signs it’s time

  • Your team spends hours a day rekeying data between systems that cannot talk to each other, copying invoice lines from a PDF into an accounting package, or transcribing orders from a portal into an ERP, and there is no API on either side to do it properly
  • You have high-volume, rules-based back-office work (reconciliations, data migrations between old systems, form-filling, report assembly), that follows clear rules, never really varies, and eats a person’s day for no good reason
  • The systems involved are old and closed. A legacy platform, a vendor application with no integration, a portal you do not control, and cannot be changed or replaced any time soon, so the usual answer of "just integrate them" is genuinely not available
  • You need a fast, tactical win now (to relieve a team that is buckling, or to bridge a gap), while a proper system or integration is built over a longer horizon, and you understand the bot is a temporary bridge, not the permanent fix

How we think about RPA

Our governing belief is that RPA is a band-aid, and the skill is in knowing when a band-aid is the right thing to reach for. It sits on top of a problem (it drives the symptoms of a system rather than changing the system), and that means it is inherently more fragile than a solution that goes deeper. A bot depends on screens it does not control, so it inherits the instability of every interface it touches; every UI change is a potential outage. That is not a reason never to use RPA, it is a reason to use it precisely where its trade-off is worth it: automating across old, closed, API-less systems that cannot be changed, where the alternative is more people doing soul-destroying rekeying. Outside that niche, the more robust answer (a real integration, a fixed process, or leaving well alone), is usually the better spend, and our job is to tell you which situation you are in.

The second thing we hold to is that automating a broken process is worse than useless, because it makes the brokenness faster, more reliable and harder to see. So we refuse to bolt a bot onto a convoluted workflow without first asking why the workflow is convoluted; often the biggest win is not the automation at all but the simplification that precedes it. And we are candid about the total cost, not just the build: an RPA bot carries an ongoing maintenance burden that a proper integration does not, and pretending otherwise would be selling you a liability dressed as a solution. We would rather quote you the honest lifetime cost of a bot (including the day it breaks because a supplier redesigned their portal), and let you weigh that against an integration, than win the work by hiding the fragility that is RPA’s defining characteristic. This is the same philosophy that runs through our business-automation service: the goal is to remove the manual work in the most durable way available, and a bot is only sometimes that way.

Technologies we build it with

Chosen per problem, not per fashion. This is the stack we most often reach for on this work.

How we deliver

  1. 01

    Discover

    We map the system, the constraints and the business it serves, including the parts nobody documented.

    Architecture brief

  2. 02

    Architect

    Decisions get made, written down and defended before a line of production code exists.

    Decision records

  3. 03

    Build

    Short cycles against working software. You see progress in the product, not in a status deck.

    Shipping increments

  4. 04

    Operate

    Monitoring, incident response and iteration. The system is alive, so the engagement is too.

    Runbooks & SLOs

Want a straight answer on Robotic Process Automation?

A short call with a senior engineer, before you write a brief. If Robotic Process Automation is the wrong answer for your situation, we will say so and tell you what we think is right.

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What changes

  • The drudgery comes off the team

    The repetitive, rules-based rekeying that ate hours of skilled people’s days gets done by a bot instead, faster, at any hour, without the transcription errors that come from a human copying the same fields hundreds of times, freeing the team for the work that actually needs a person.

  • Old systems bridged without touching them

    Systems that have no API and cannot be changed get connected in practice (the data flows between them), without a risky, expensive change to the legacy platforms themselves, buying you a working process now while a proper long-term answer is figured out separately.

  • A clear-eyed decision, not a sold one

    You end up knowing exactly what you have: whether a bot was genuinely the right tool, what it will cost to keep running, and what would break it, or, just as often, an honest recommendation that an integration is the better spend, made before you committed to the wrong thing.

Industries we serve

Domain knowledge changes what gets built. A few of the sectors we know before the first meeting.

How pricing works

  • A fixed-scope build for a defined automation. A specific process, a known set of systems, a clear set of rules: quoted once we have mapped the process and confirmed a bot is genuinely the right tool, so you are paying to remove a specific, understood piece of manual work rather than a vague promise of "automation".
  • An assessment-first engagement, priced on the assessment, where we evaluate a process or a set of them and come back with an honest recommendation (bot, integration, process fix, or leave alone), before any build. This is the sensible way to start when you are not yet sure RPA is the right answer, and it frequently saves clients from building the wrong thing.
  • A monthly engagement covering both the build-out of a fleet of bots and the maintenance they require, which is the shape that reflects RPA’s reality: bots need tending as the screens they drive change, so ongoing support is not an optional extra but part of the honest cost of running them, and pricing it as a standing commitment keeps that visible.
  • A bridge engagement, scoped as explicitly temporary. A bot built to relieve immediate pressure while a proper system or integration is delivered separately, priced with its retirement in mind, so you are not paying to maintain a stopgap for longer than it is meant to exist.

Typical timeline

  1. 01

    Assessment and honest recommendation

    Around a week mapping the process and the systems, checking whether an API or integration exists, and deciding whether a bot is genuinely the right tool, ending in a straight recommendation, which is sometimes that you should not build a bot at all and we will say why.

  2. 02

    Process simplification and rule design

    Fixing the process before automating it: stripping out the convoluted steps, settling the rules the bot will follow, and deciding explicitly what happens to the awkward cases that do not fit, because a bot automating a mess just makes the mess faster.

  3. 03

    Build, exception handling and testing

    A few weeks building the bot with resilient selectors, thorough exception handling, secure credential handling and clear logging, then testing hard against the edge cases and the unexpected screens, not just the happy path, because that is what decides whether it survives production.

  4. 04

    Deployment, orchestration and honest handover

    Deploying with the orchestration, monitoring and audit trail in place, then handing over with candid documentation of what the bot depends on, what will break it, and what keeping it running will cost, so you own the automation and its maintenance reality with your eyes open.

What working with us actually means

  • We will tell you when RPA is the wrong tool

    Our first loyalty is to the most robust answer, not to selling a bot. If an API or a real integration exists, we will steer you there even though it is less billable for us, because a bot driving screens is a worse long-term bet than a proper connection. That honesty is precisely what makes the bots we do build worth trusting. We only build them where they genuinely earn their place.

  • We fix the process, then automate it

    We refuse to enshrine a broken or convoluted workflow in a bot, because automating a mess just industrialises it. We map what actually happens, simplify the steps that make no sense, and settle the rules (including the awkward cases), before a bot touches anything, so the automation runs a sensible process rather than immortalising a bad one at machine speed.

  • We build bots that survive reality

    The difference between an RPA bot that keeps running and one that becomes a support headache is almost entirely in the exception handling, the selector robustness and the safe failure modes. The unglamorous engineering that the happy-path demo never shows. We build for the ninety percent that is edge cases, because that is what decides whether a bot lasts in production.

  • We are honest about the maintenance burden

    RPA carries an ongoing cost a proper integration does not, and we quote you that honestly, including the fact that a supplier redesigning their portal can break your bot overnight. You go in knowing the lifetime cost and the fragility, not just the launch, because selling you a maintenance liability dressed as a finished solution is not something we are willing to do.

How to engage us

Three ways to work with us on this, chosen to fit the problem, not our margin.

Related services

Part of Digital Transformation. Other work we do alongside this.

Common questions

Isn’t RPA just a fragile band-aid that breaks all the time?

Often, yes, and we will not pretend otherwise. Because a bot works by driving the user interface of an application, it depends on those screens staying the same, so any change to a UI it touches can break it. That is RPA’s defining weakness, and it means a bot carries an ongoing maintenance burden that a proper integration does not. So we use RPA deliberately: where the systems are old, closed and have no API, and cannot be changed, a bot is genuinely the pragmatic right answer despite its fragility, because the alternative is more people doing manual rekeying. Everywhere an API or real integration is available, that is almost always more robust and we will steer you there. The fragility is real; the skill is in only reaching for a bot where its trade-off is worth it, and being upfront about the maintenance cost when we do.

When should we build a proper integration instead of a bot?

Almost whenever you can. If the systems you want to connect expose an API (or one could reasonably be added), an integration that talks to them in a supported, stable way will outlast a bot that drives their screens, because it does not break every time a UI is redesigned. A bot is the right tool specifically when a real integration is not available: a legacy system with no API that cannot be changed, a desktop application whose vendor is gone, a third party’s portal you do not control. So the honest rule is: integration where you can, bot where you genuinely cannot. A large part of our value is telling you which situation you are actually in, and we will recommend the integration even though building the bot would be easier work for us, because it is the better long-term answer for you.

Won’t automating our process just make our existing problems faster?

If we automated it blindly, yes, and that is exactly why we do not. Putting a bot on top of a convoluted or broken workflow does not fix the workflow; it makes the same mistakes happen faster, more reliably and less visibly, which is worse than the manual version because at least a human sometimes notices something is wrong. So before we automate anything, we map what actually happens and simplify it: we strip out the steps that exist only because someone once did it that way, settle the rules clearly, and decide explicitly what should happen to the cases that do not fit. Often the biggest win in an RPA engagement is not the bot at all: it is the process simplification that has to happen first. We automate a sensible process, never a mess.

How do you keep the credentials and sensitive data a bot handles secure?

A bot that logs into your systems is a privileged account and we govern it like one. Its credentials never live in the script or a config file: they sit in a proper secret vault, whether the credential store built into the RPA platform or an external one, and are fetched at run time, scoped tightly to only the systems that bot actually needs. We give a bot the least access that lets it do its job, so that if anything goes wrong the damage is contained. The sensitive data it handles (invoices, personal records, financial figures), is encrypted in transit and at rest, kept out of stray logs and screenshots, and handled in line with UK GDPR where personal data is involved. And every bot keeps an audit trail of what it did and when, so its activity is accountable rather than an opaque background process. A bot acting invisibly on sensitive systems is a governance risk; a vaulted, least-privilege, fully-logged one is controllable.

How does AI change what RPA can do?

It widens it meaningfully, by handling exactly the parts traditional RPA was bad at. Classic bots are rigid and rules-based, so anything unstructured: a PDF invoice whose layout varies, a free-text email, a case that does not fit a clean rule, used to need a human in the middle. Increasingly, AI handles those steps: document understanding reads the varied invoice, models classify or extract from messy text, and the bot takes the structured result and acts on it. So the combination automates work that was previously off-limits to a purely rules-based bot. It does not change RPA’s fundamental trade-off (a bot driving a UI is still fragile against screen changes), but it does mean the honest answer to "can this be automated" is broader than it used to be, and it is a live part of how we scope these engagements, drawing on our AI development work where the unstructured steps warrant it.

Thinking about Robotic Process Automation?

Tell us the problem in your own words, not in requirements. A senior engineer reads it and comes back with a straight view on whether Robotic Process Automation is the right answer here, or what would be.

  1. 01A senior engineer reads it. Not a form queue, and not an account manager.
  2. 02We reply either with questions or with a straight answer that we are not the right fit.
  3. 03If it looks like a fit, a technical call with the person who would actually run the delivery.
  4. 04Then scope, effort and risk in writing, before anyone signs anything.
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