Remove the manual steps, not just the keystrokes.Build

The work your team does by hand is the work we delete

Most enterprise back offices run on a chain of manual handoffs: a document is read, retyped, checked, routed, checked again, and finally posted. Each handoff adds cost, latency and error surface. We rebuild that chain as a single automated pipeline with an audit trail and exception routing.

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Team reviewing process documentation around a meeting table

70–92%
of routine transactions automated end to end: 3–6×
faster average cycle time: 60–80%
reduction in processing cost per transaction: 85%+
fewer data-entry errors reaching downstream systems

What it looks like today

The pattern we find in almost every operation

Manual processes are rarely one big problem. They are dozens of small ones that individually look acceptable and collectively cost seven figures.

Documents are read by a human and retyped into a system that could have read them directly.
Approvals sit in someone's inbox for days because routing is a person, not a rule.
The same data is keyed into three systems because nothing talks to anything else.
Errors are found downstream — after the invoice is paid or the order has shipped.
Nobody can say what the process costs, because no one measures the queue.
Volume growth means headcount growth, so the process never gets cheaper with scale.

How we do it

The approach, step by step

Each of these is a decision point rather than a formality. Skipping any one of them is what turns an automation project into an expensive pilot.

Instrument before automating

We measure the real process — volumes, cycle times, exception rates, touch counts, fully loaded cost per transaction. Automation without a baseline cannot be proven, and unmeasured processes are usually worse than their owners believe.

Map the decision logic

Every manual process contains rules that were never written down. We extract them from the people doing the work, separate genuine judgement calls from habit, and document the logic in a form that can be tested.

Build the pipeline

Deterministic steps become integrations and business rules. Unstructured inputs — PDFs, emails, scanned forms, free text — go through extraction and classification models with confidence thresholds rather than blind trust.

Route exceptions, don't hide them

Anything below the confidence threshold is surfaced to a human with full context in a review queue. Automation handles the 80–95% that is routine; people handle the remainder faster because the busywork is gone.

Run it in production

Monitoring, alerting, replay for failed runs, throughput dashboards and a defined support path. We operate what we build so the automation keeps working after the launch deck is filed.

What you receive

Deliverables, stated up front

Everything below is in scope on a standard engagement. If something here is not relevant to your situation we will say so and price accordingly rather than padding the scope.

Process baseline with measured volumes, cycle times and cost per transaction
Documented decision logic and exception taxonomy for the process
Production automation pipeline with retry, replay and dead-letter handling
Document extraction / classification models with confidence thresholds
Human review queue for exceptions, with full source context inline
Integration layer against ERP, CRM, ITSM and document stores
Throughput, accuracy and cost-per-transaction dashboard
Runbook, alerting rules and a named support path

You are a fit if

More than four people spend most of their week on the same repeatable process
Volume is growing but headcount cannot keep growing with it
Cycle time is measured in days when the actual work takes minutes
Error and rework costs are visible but no one owns the root cause
The process spans multiple systems with manual re-keying between them

We will tell you it is a fit problem if

The process runs fewer than a few hundred times a year — the payback will not clear the build cost
The decision logic genuinely changes every time, with no stable rules to extract
The underlying data is so unreliable that automation would industrialise the wrong answer

Systems we work with

SAPOracle NetSuiteMicrosoft Dynamics 365WorkdaySalesforceServiceNowSharePointUiPath / Power AutomateAWS TextractAzure Document Intelligence

Not on the list? We integrate against anything with an API, a database, a file interface or a documented import format.

Questions we get on this

Workflow automation: the practical answers

Do we have to replace our existing systems?

Almost never. The automation sits alongside what you already run and interacts through supported APIs, database views, file drops or, where nothing else exists, the same interface a person uses today. Replacement is a much larger project and is only justified when the system itself is the bottleneck.

What happens when the automation gets something wrong?

Every pipeline we build ships with confidence thresholds and a review queue. Low-confidence items go to a human with the original document side by side with the extracted values. Failed runs are captured in a dead-letter queue and can be replayed after the cause is fixed, so nothing is silently dropped.

How long before we see production results?

A typical engagement puts the first process into production within six to ten weeks, starting with the highest-volume, lowest-complexity candidate so the savings start accruing while we work on the harder ones.

Is this RPA or is this AI?

Both, chosen per step. Deterministic rules and API calls are cheaper, faster and more reliable than a model, so we use them wherever the logic is stable. Models are reserved for genuinely unstructured input — reading a supplier PDF, classifying a free-text email, matching a description to a catalogue entry.

Also consider

All 11 capabilities

Custom internal software

Software shaped around your process, not the other way round.

Data foundation

Automation inherits every flaw in the data beneath it.

Customer operations

Answer faster, resolve more, and stop paying for the same conversation twice.

Bring us the process you already know is costing too much

Thirty minutes with an engineer is usually enough to tell whether it is worth automating, roughly what it would save, and whether the payback is inside a window your finance team will accept. If the answer is no, we will say so on the call.

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