Robotic process automation, intelligent document processing, workflow orchestration and AI agents — applied to the processes where automation genuinely pays back, and only those.
Automation has a credibility problem, and it is self-inflicted. Too many programmes automate whatever process was volunteered first, discover that the exceptions outnumber the happy path, and quietly stall at four bots nobody maintains.
We assess before we automate. Volume, variability, exception rate, stability of the underlying system and the real cost of the manual process — those five things tell you whether a process should be automated, redesigned, or left alone. We are comfortable telling you the answer is the third one.
These are the measures we baseline at the start and report on throughout — not results claimed in advance.
A structured evaluation of your candidate processes that produces a ranked pipeline with expected payback — and an explicit list of what not to automate.
Software robots for rule-based, high-volume work across systems that have no usable API — built with exception handling as a first-class concern.
Extracting structured data from invoices, contracts, forms and correspondence, with confidence thresholds that route uncertain cases to a person instead of guessing.
Automating the process itself rather than the clicks — replacing email-and-spreadsheet coordination with an orchestrated workflow that has state, rules and an audit trail.
Language-model applications applied where they hold up under scrutiny — retrieval over your own knowledge, drafting, classification and triage — with evaluation and guardrails built in.
Models built to answer a decision someone actually makes, deployed into the workflow where that decision happens — not left in a notebook.
Five phases, each with deliverables you can point at. No phase ends on a status report alone.
Map the candidate processes, capture real volumes and handling times, and score each one for automation suitability against evidence rather than enthusiasm.
Deliverables
Redesign the process before automating it. Automating an inefficient process just produces a faster inefficient process with a maintenance contract.
Deliverables
Develop the automation and run it in parallel with the manual process on live data, so accuracy is proven before anyone relies on it.
Deliverables
Controlled production rollout with monitoring in place from the first transaction, then extension to adjacent processes once the first is genuinely stable.
Deliverables
Automations break when the systems underneath them change. Ongoing operation means monitoring, repairing and improving straight-through rates over time.
Deliverables
Different problems want different commercial shapes. We will tell you which one fits, including when it is the cheapest one.
A standalone discovery producing a ranked, costed automation pipeline you own regardless of who builds it.
One end-to-end process automated and run in production, so the business case rests on your own data rather than a vendor benchmark.
A standing team delivering automations continuously against a managed pipeline, with a governance rhythm around it.
Monitoring, maintenance and improvement of automations already in production — whoever originally built them.
We pick tools to fit your existing estate and your team's ability to maintain them, not to fit our preferences.
We score it on five factors: transaction volume, how much the inputs vary, the exception rate, how stable the underlying systems are, and the genuine cost of doing it manually today. A high-volume process with a 40% exception rate and a system due for replacement is a bad candidate, however painful it feels.
The engagements that succeed redirect capacity rather than cut headcount — the automation absorbs the repetitive volume and the team moves to exception handling, quality and work that needs judgement. We would rather set that expectation honestly at the start than have it discovered later.
The automation breaks — this is the single biggest cause of RPA estates decaying. We build monitoring that detects failure immediately, design bots to be resilient to minor interface changes, and include upstream change impact assessment in ongoing support.
For some tasks, yes; for others, not yet, and the difference matters. Classification, retrieval over your own documents, drafting and triage hold up well with proper evaluation. Anything where a confident wrong answer is expensive needs a human approval gate, and we design those in rather than around.
By deploying within your cloud tenancy or a contracted region, using enterprise endpoints with no training on your data, redacting PII before inference where the use case allows, and documenting the data flow so your security and compliance teams can review it properly.
A short conversation is usually enough to tell whether we're the right fit. If we're not, we'll say so and point you somewhere better.
Or email us directly at reachus@segakiv.com
