Why not just buy an off-the-shelf AI tool?

A product encodes one opinion about how the work should be done. Where your process differs, the gap becomes permanent manual effort. We start from your process instead.

How long until something is in production?

Two to three weeks to a documented architecture, then four to six to a first increment running in shadow mode against real data.

Where does our data go?

Wherever you decide — your cloud account or ours, with region and retention as explicit decisions. We do not train models on your data, and every system ships with an audit log.

Yes. Source lands in your repository from the first commit, with the architecture docs, evaluation suite and runbooks. Taking it in-house later requires nothing from us.

Discovery and architecture are a fixed fee. Build work is quoted as fixed-scope phases, so you approve cost per phase rather than signing an open-ended commitment.

How is this different from RPA?

Robotic process automation drives the user interface — it clicks buttons as a person would, and it breaks whenever a screen layout changes. We integrate at the API and data layer, which is more durable and far faster. Where a system genuinely has no API we will still automate the interface, but as a deliberate last resort rather than the default technique.

What happens when one of our systems is down?

The run pauses at that step rather than failing the whole process. Transient errors are retried with backoff, and if the outage persists the run holds in a resumable state and alerts. When the system returns, work resumes from where it stopped — no partial writes, no duplicates.

Do we lose visibility once it is automated?

You gain it. Manual processes are invisible by nature — nobody knows how many invoices are mid-flight or how long a step really takes. Every run here is recorded with timings, so volumes, cycle time and exception rates become numbers on a dashboard for the first time.

What about the cases that genuinely need a human?

They are designed in from the start, not treated as failures. Discovery explicitly identifies which steps require judgment, and those become checkpoints in the exception worklist with the context pre-assembled — so a person spends seconds deciding rather than minutes gathering.

Will this replace people's jobs?

In practice it removes the copying and chasing, and the same team handles a larger volume while spending their time on exceptions and customers. We will tell you honestly during discovery which steps should stay manual — some should, either because judgment matters or because the volume does not justify the engineering.

How do you decide what to automate first?

Discovery measures volume, cycle time and exception rate on the real process, then ranks candidates by effort against hours recovered. You get a costed list, and we usually recommend starting with the second or third item rather than the largest — a fast, visible win builds more internal support than an ambitious one.

What we buildProcess Automation

Work that moves between systems without anyone carrying it

Most operational delay is not work — it is waiting. A record sits in one system until somebody notices and retypes it into another. Orchestration removes the waiting and surfaces only the cases that genuinely need a decision.

Map your slowest process How it works

Run history — dashboard

public/automation/runs.png

How it is engineered

Every step: Idempotent; A retry re-runs safely. Nothing is charged, sent or created twice.
State: Typed; Each step declares what it needs and returns. Malformed data stops the run.
On divergence: Alerted; When reality stops matching the model, someone is told — immediately.
Silent queues: 0; Exceptions surface in a worklist. Nothing decays quietly in a folder.

01Built for the messy middle

The happy path is the easy ten per cent

Anyone can automate a process where every input is clean. The engineering is in what happens when a field is blank, an API is down, or the same invoice arrives twice.

Orchestrator

Idempotent · Fully audited

What starts a run

Runs are driven by the events your business already emits — no one has to remember to kick them off.

Inbound documents: An invoice, order or form arrives by email, upload or API and starts its own processing.
System events: A status change in your ERP, CRM or warehouse system triggers the next stage automatically.
Schedules: Nightly reconciliation, weekly reporting and month-end routines, finished before anyone logs in.
Thresholds: Stock below a level, an SLA about to breach, a payment overdue — noticed without a human watching.

What it does with the awkward cases

Exceptions are the point of the system, not an afterthought bolted on once it is live.

Retries with backoff: Transient failures resolve themselves. Only persistent ones become anybody's problem.
Duplicate detection: The same document arriving twice is recognised and collapsed, not processed twice.
Exception worklist: Cases needing judgment queue with full context, so a person decides in seconds rather than investigates for minutes.
Compensating actions: A run that fails halfway unwinds what it already did, instead of leaving your systems disagreeing.

02Anatomy of a run

Every run is a record you can inspect afterwards

01
Triggered
An event, document or schedule starts the run with a unique key that makes it safe to repeat.
02
Validated
Inputs checked against a schema before anything is written. Bad data fails here, not three systems later.
03
Enriched
Related records pulled from your systems so the decision is made with complete context.
04
Executed
Steps run in order, each idempotent, each recording what it changed and where.
05
Excepted, if needed
Anything ambiguous or above threshold routes to the worklist with the evidence attached.
06
Closed
Systems updated, notifications sent, and the full run history retained for audit.

03The hard parts

The failures that quietly cost more than the manual process did

Bad automation is worse than none, because it fails at scale and without anyone watching. These six are what keep it honest.

01
Double processing
The same invoice paid twice is the classic automation disaster. Every step is keyed and idempotent, so a retry or a duplicate trigger produces exactly one outcome.
02
Half-finished runs
A failure between two writes leaves your systems disagreeing. Runs either complete or compensate — there is no state where the ERP and the CRM tell different stories.
03
Silent decay
The dangerous failure is the one nobody notices for a month. Divergence between expected and actual volumes raises an alert on its own.
04
Exception backlogs
If exceptions are painful to clear, they accumulate until the automation is abandoned. The worklist is designed for seconds-per-case, with context pre-assembled.
05
Change breaking it
Your systems will change. Contract tests on every external boundary fail loudly in staging rather than silently in production.
06
Nobody can explain it
Every run stores its inputs, steps, decisions and outputs. When finance asks why something posted, the answer is a record, not an archaeology exercise.

04Where it earns its place

High volume, clear rules, and a handoff that always stalls

Pick the process people complain about. It is usually high-volume, rules-based, and blocked on somebody retyping something.

Invoice to payment
Extracted, matched against PO and receipt, posted — with only true discrepancies held for review.
Order exceptions
Short shipments, address failures and stock issues investigated and resolved before the customer calls.
Employee onboarding
Accounts, access, equipment and paperwork provisioned across every system on day one.
Reconciliation
Ledgers, statements and settlements compared nightly, with only genuine breaks escalated.
Supplier onboarding
Documents collected and validated, records created, compliance checks tracked to completion.
Reporting packs
Assembled from source systems on schedule, consistent every time, without a late-night export.

Screenshot — in context

public/process-automation/screen-1.png

Screenshot — result

public/process-automation/screen-2.png

05FAQ

What teams ask before they commit

Robotic process automation drives the user interface — it clicks buttons as a person would, and it breaks whenever a screen layout changes. We integrate at the API and data layer, which is more durable and far faster. Where a system genuinely has no API we will still automate the interface, but as a deliberate last resort rather than the default technique.

06Also built here

The rest of what we do

Next Step

Let's build your next system.

Bring us the process costing your team the most hours. We'll tell you honestly which parts are worth automating.

Book a Strategy Call contact@araxys.dev

Prefer to talk now?+91 89391 53390+91 63806 69805

Fixed-scope phases
You own the source
NDA on request
No obligation

Work that moves between systems without anyone carrying it

How it is engineered

The happy path is the easy ten per cent

What starts a run

What it does with the awkward cases

Every run is a record you can inspect afterwards

Triggered

Validated

Enriched

Executed

Excepted, if needed

Closed

The failures that quietly cost more than the manual process did

Double processing

Half-finished runs

Silent decay

Exception backlogs

Change breaking it

Nobody can explain it

High volume, clear rules, and a handoff that always stalls

Invoice to payment

Order exceptions

Employee onboarding

Reconciliation

Supplier onboarding

Reporting packs

What teams ask before they commit

The rest of what we do

AI Agents

Voice Agents

Knowledge & Retrieval

Integrations

Custom Software

Let's build your next system.