Automation that survives contact with the real process.
Most automation fails for the same reason: it was built on top of a process nobody had measured. We map the work first, put numbers on it, then automate the parts that earn it: deterministic where it must be, agentic where judgement is genuinely needed.
What you leave with
- The two or three processes where automation would pay back first, and the ones to leave alone
- A rough cost of the work as it runs today, from volume, cycle time and rework
- Which approach fits each step: a rule, an integration, an AI agent, or a decision to stop doing it
With a senior architect, not a salesperson. Sixty minutes, no cost, no obligation.
Automating a process you have not measured just makes the mess faster.
The failed automation projects we get called in to rescue have one thing in common: the tooling was chosen before anyone mapped how the work actually flows. So we start with the process, not the platform, and quite often the first recommendation saves more than the automation would have.
Map
Follow the work as it really happens, including the spreadsheet nobody mentions and the person who fixes things by hand every Friday.
Measure
Volume, cycle time, error rate, rework and cost per case. Now the business case is arithmetic instead of enthusiasm.
Decide
Some steps should be automated, some simplified, some deleted outright. We are explicit about which is which, and in what order.
Build
Deterministic where it must be, agentic where judgement is genuinely needed, with exception handling and a human path designed in from the start.
The work that automates well.
Document and inbox work
Invoices, claims, applications, supplier emails. Classify, extract, validate against the system of record, route the exceptions to a person with the context attached.
Cross-system handoffs
The steps where somebody copies data from one system into another because the two were never integrated. Usually the cheapest large win available.
Integration workBack-office operations
Onboarding, reconciliation, month-end packs, compliance evidence gathering. Repetitive, rule-heavy, and expensive precisely because it is invisible.
Judgement-assisted steps
Triage, summarisation, first-draft responses, prioritisation. An agent proposes, a person disposes, with the reasoning shown, not hidden.
Monitoring and response
Watching for a condition and doing the first three obvious things about it, then escalating with a proper handover rather than a raw alert.
Reporting that eats a week
If a person spends days assembling the same report every month, that is a pipeline wearing a disguise.
We will tell you when not to automate.
A process that changes every quarter, runs eleven times a year, or exists only because of a policy nobody has revisited since 2019 does not want an automation project. It wants a decision. We would rather hand you that decision in a free hour than bill you for building around it.
- Exception paths designed before the happy path is celebrated
- A human override on anything that touches money or a customer
- Every automated decision logged and explainable after the fact
- Handover documentation your own team can maintain
- A kill switch, and a tested way back to the manual process
What we automate, and what we build it with
Workflow, integration and AI agents, chosen per step rather than per vendor. Built in your own accounts and handed over with the runbook.
Workflow and orchestration
Power Automate, Azure Logic Apps, AWS Step Functions, Google Cloud Workflows, Zoho Flow, n8n and Make, or plain code where a platform would be more than the job needs.
Document and email processing
Invoices, claims, applications and supplier email: classified, extracted and checked against your system of record, with exceptions routed to a person.
AI agents with guardrails
Agents on Amazon Bedrock, Azure OpenAI or Vertex AI for the steps that need judgement, with the reasoning shown, limits enforced and a person on anything that touches money or a customer.
Integration instead of copy-paste
APIs, events and webhooks between the systems people currently re-type between, which is usually the cheapest large win on the list.
Reporting pipelines
The monthly pack that takes a week, rebuilt as a pipeline that runs itself and shows where every number came from.
Run and improve
Monitoring, an audit log of every automated decision, a tested way back to the manual process, and documentation your own team can maintain.
Sound familiar?
- People copy the same data between two or three systems every day
- A report or month-end pack takes days to assemble by hand
- An earlier RPA or automation project breaks every time a vendor updates a screen
- You want to use AI agents but need a person to stay in control of the decisions
- Nobody can say how much a process costs today, so nobody can say what automating it is worth
A small, senior team that stays accountable.
- Architect-led. Every engagement is led by a senior architect who stays accountable for the decisions, from the first hour to the handover.
- Over twenty years of building and integrating software. Enterprise integration, cloud platforms, product delivery and now AI agents, across sectors where systems cannot fail.
- Independent and vendor-neutral. AWS, Azure and Google Cloud, and whatever platforms you already run. We recommend what fits, including doing less.
- Research, applied. The patterns we use are published openly on The Software Lens and applied by our R&D Wing, so you can read the reasoning before you hire us.
- We run what we recommend. We build our own product, Nexcubator, on the same architecture we design for clients.
- You own everything. Your repository, your cloud account and your documentation from the first week. Based in Ireland, working across the EU and beyond.
Questions we get asked
Is this RPA?▼
Rarely, these days. Screen-scraping robots break every time a vendor ships a UI change. We prefer real integration at the API or event level, with agentic components only where the step genuinely needs judgement. If RPA is the only option left on a closed legacy system, we will use it, and we will tell you it is a stopgap.
How do you decide what to automate first?▼
Volume multiplied by cycle time multiplied by error cost, weighted by how stable the process is. That usually produces an unglamorous first candidate and a much better business case than the one people expected.
What happens when the automation gets something wrong?▼
It gets designed for on day one: exceptions route to a person with full context attached, every decision is logged, there is a documented manual fallback, and there is a kill switch that has actually been tested.
Do we have to replace our existing systems?▼
Almost never. Most of the value sits in the handoffs between systems you already own. Replacing platforms is a much bigger project with a much worse return, and we will say so.
One free hour. No pitch, no obligation.
Bring the problem you are stuck on: an integration that keeps breaking, a cloud bill nobody can explain, an AI project that is all demo and no product, or a platform you are about to invest in. You will leave with a straight answer and a written summary, whether or not you ever work with us.
- Architecture and integration review
- AI feasibility: what will actually work, and what will not
- AWS, Azure and Google Cloud cost and design
- Business process audit and ISO readiness
- Technical due diligence before you invest or acquire
Some of them do not need to exist.
A lot of automation work is moving data between HR, time-tracking, project and finance tools that were never meant to work together. Nexcubator runs those processes in one place, so the hand-off you were about to automate simply is not there.
See what one platform looks like