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Aplora

Automate work across your systems

Connect forms, CRM, documents and messages into a managed workflow, with rules, exception handling and a process owner.

See how it works

Next: a short form and a discussion of your task. No files need to be uploaded now.

  1. Source
  2. Automated step
  3. AI step
  4. Business ruleException queue
  5. Human checkpoint
  6. Target system
  7. MonitoringMany exceptions are a reason to revisit the rules, not to hire people
An interface schematic, not a screenshot of a running system: the product is assembled around each client's own stack, so there is no typical screen.

Growing volume demands a proportional increase in manual work.

Removes repetitive manual steps from end-to-end business workflows.

Capabilities and details

From a form or a call through to the CRM, the document, the e-mail and the management report — with explicit rules, exception handling and a named process owner.

Connects events, data, AI and business systems into one governed end-to-end workflow.

  • leads and CRM: form → qualification → record → task
  • customer requests: classification → response → escalation
  • documents and approvals: validation → routing → archive
  • scheduled operational reporting
  • monitoring of changes in external systems
  • SLA-driven notifications and escalations

What it looks like from the inside

  • The same data gets copied between systems by hand
  • A task sits waiting for an approval nobody is there to chase
  • Requests get lost between e-mail, messenger and the task tracker
  • Routing depends on who happens to be available
  • Reports are compiled by hand at period end and are stale by the time they're discussed
Hands typing data at a keyboard, with the paperwork it is being copied from lying alongside.

Which processes we take on

Six types teams usually start with. The order inside each comes from your process, not from a template.
An operations team works across several screens: some at laptops, others at monitors.

  • Leads and CRM

    form → qualification → record → task

  • Customer requests

    classification → response → escalation

  • Documents and approvals

    validation → routing → archive

  • Scheduled operational reporting

  • Monitoring of changes in external systems

  • SLA-driven notifications and escalations

How it works

The route from input to outcome

Nodes expand: input, processing, output, control.
  1. 01Source

    Signal

    Expand
    Input
    A form, an e-mail, a call, a system event, a schedule.
    Processing
    Intake, normalisation and de-duplication.
    Output
    An event in the processing queue.
    Quality control
    A repeat arrival does not create a second record — an idempotency key prevents it.
  2. 02Automated step

    Data enrichment

    Expand
    Input
    The event plus related records from CRM, ERP and reference data.
    Processing
    Deterministic lookups: client, contract, owner.
    Output
    The event with full context attached.
    Quality control
    If a match isn't found, nothing is guessed — the event goes to the exception queue.
    Integration
    CRM, ERP, databases
  3. 03AI step

    AI step

    Expand
    Input
    Unstructured text: an e-mail, a request, a transcript, a document.
    Processing
    Classification, entity extraction, priority scoring, draft response.
    Output
    A structured result with a confidence level.
    Quality control
    Below the confidence threshold nothing is decided automatically.
  4. 04Business rule

    Business rules

    Expand
    Input
    The structured result and your company policies.
    Processing
    Routing, scoring, limit and condition checks.
    Output
    A decision on the next action and who performs it.
    Quality control
    Rules live in configuration and are versioned — visible and revertible.
  5. 05Human checkpoint

    Manual checkpoint

    Expand
    Input
    The prepared action with its rationale.
    Processing
    A person approves, edits or rejects.
    Output
    An approved action.
    Quality control
    Placed wherever an error is expensive. The decision and its author are logged.
  6. 06Target system

    Action in the system

    Expand
    Input
    An approved or automatic decision.
    Processing
    Write to CRM/ERP, send a message, create a task, generate a document.
    Output
    The outcome, landed in the target system.
    Quality control
    A failure in the external system triggers a retry, then a parking queue and an alert — nothing is lost.
    Integration
    CRM, ERP, e-mail, messengers, n8n, your own APIs
  7. 07Monitoring

    Metrics and monitoring

    Expand
    Input
    Events from every step of the workflow.
    Processing
    Cycle time, exception rate, quality and cost are computed.
    Output
    An operational dashboard and alerts for the process owner.
    Quality control
    An exception-rate threshold is a signal to revisit the rules, not to add manual work.

Feedback: monitoring returns data to the rules — the loop closes rather than ending at the last step

What it needs

  • website forms and requests
  • inbound e-mail and support tickets
  • CRM and ERP events
  • documents and attachments
  • call recordings
  • exports and third-party APIs

What it produces

  • records and tasks in the target systems
  • messages and notifications to the right recipients
  • routing of a request to the right person
  • escalation when a rule is breached
  • an operational report on the workflow

What can be measured

Categories, not promised values. Numbers appear only in cases, with a baseline and a stated method.
  • cycle time from signal to outcome
  • share of requests handled without a manual step
  • number of rework loops
  • approval waiting time
  • exception rate and time to clear the exception queue

What it connects to

A specific service is named once the integration is verified.
  • CRM
  • ERP
  • telephony
  • e-mail and SMS
  • databases
  • messengers
  • n8n and APIs
  • the client's own systems
  • Google Workspace / Microsoft 365

Where the human stays

A person approves what is expensive: a client e-mail, money moving, contract terms. The rest runs automatically, with a log and a rollback.

What happens when something breaks

External systems go down, formats change, data arrives incomplete. A workflow with no answer to that works right up until the first failure.

Event in the queue

No events on the schematic — yours land here

  • A delayed retry — most third-party failures are transient
  • A parking queue: an event that couldn't be processed waits instead of vanishing
  • Manual review for anything below the confidence threshold
  • An alert to the process owner rather than silence in a log
  • Rollback of an action already taken
An interface schematic, not a screenshot of a running system. The empty slots are deliberate: they are filled from your own sources.

Four levels of implementation

A more complex level isn't a better one. A deterministic link is cheaper, more predictable and easier to maintain — and if it solves the problem, AI has no place here.
  1. 01

    Deterministic integration

    Links and triggers with predictable behaviour. The cheapest and most reliable level.

  2. 02

    An AI step inside the workflow

    Classification, extraction, a drafted response — where the input is unstructured text.

  3. 03

    Agentic workflow

    Actions with context and tools. Justified when there are many steps and each depends on the last.

  4. 04

    Knowledge / RAG layer

    Corporate knowledge and documents used as a source of context.

Left to right, what grows is complexity and cost of ownership — not value. The level follows the task, the risk and the economics: if a deterministic link solves it, there is no reason to add AI.

How a rollout goes

The order is always the same: first the boundary of the test, then the test itself, and only then the rollout.
  1. 1

    Process review

    You get: a map of steps and the bottleneck

  2. 2

    PoV boundary

    You get: criteria to continue or stop

  3. 3

    Build on one flow

    You get: a working route

  4. 4

    Test on your data

    You get: a measurement against the baseline

  5. 5

    Rollout

    You get: a process owner and monitoring

A person works through a wall of sticky notes laid out as process lanes.

Cases

Where this has been applied

  • eCommerce and retail · AI Workflow Automation

    Client under NDA

    An online store's support front line stopped growing with its order volume

    Enquiries that reach an operator
    −25–40%
    Client figures: before and after on the same support channels, adjusted for seasonality
    Ticket handling time
    −30–50%
    Client figures: before and after on the same support channels, adjusted for seasonality
    Case details

    Operators were absorbed by repeat questions, complex enquiries got lost in the queue, and the knowledge base went stale faster than anyone could fix it.

    Support satisfaction (CSAT)
    +10–18%
    Client figures: their own post-resolution survey
    Read the case

  • EdTech · AI Workflow Automation

    Client under NDA

    One account manager runs twenty-five cohorts instead of five

    Cohorts per account manager
    5 → 25
    Client figures: actual workload before and after at unchanged headcount
    Cost of running one course
    −40%
    The client's own cost model; the composition of the costs was not disclosed to us
    Case details

    Account managers kept cohort statuses in their heads. Every new course meant either overload or loss of control: missed classes and blown deadlines surfaced after the fact, when the student had already fallen behind.

    Read the case

  • Consulting and professional services · AI Workflow Automation

    Client under NDA

    Answers stopped living in people's heads: search across company documents, with the source attached

    Time spent finding information
    −40–60%
    Client figures: before-and-after process comparison plus team feedback
    Errors caused by out-of-date instructions
    −20–35%
    Client figures: their own incident records
    Case details

    Finding an answer took time, and what turned up could be out of date. Different people answered the client differently, and the surest route was to ask a colleague — that is, to interrupt one more person.

    Read the case

  • eCommerce and retail · AI Workflow Automation

    Client under NDA

    Nurturing stopped being one campaign to the whole list

    Stage-to-stage funnel conversion
    +30–40%
    Client figures: conversion trends after segmented nurturing went live
    Conversion to sale
    +20–50%
    Client figures: observed across different segments, hence the wide range — this is not a single measured value
    Case details

    The customer profile was described from gut feel, nurturing was launched by hand and identically for everyone, and hypotheses took weeks to test. Sales received leads with no readable warmth and spent time on people who were not ready.

    Read the case

  • EdTech · AI Workflow Automation

    Client under NDA

    AI across four departments of a school: marketing, sales, content and operations in one loop

    Revenue
    ×3 in 4 months
    Client figures: revenue over the four months after launch. The contribution of the automation itself is not isolated from it — the market, the product line and the team all changed over the same period
    Operating costs
    −60%
    Client figures: cost trend over the same period; the cost composition was not disclosed to us
    Case details

    Growth meant one of two things: losing quality, or hiring into every department in proportion to volume. No single department was the bottleneck — the seams were, and nobody owned them.

    Read the case

Frequently asked questions

Can we start with a single workflow?

That is the right way to start. We pick one workflow with clear economics, fix its baseline, and validate the hypothesis on a limited volume. Trying to automate everything at once almost always ends as a set of half-finished integrations with no measurable effect.

What happens when something breaks?

First a delayed retry — most third-party failures are transient. If that doesn't help, the event moves to an exception queue, the process owner is notified, and it is cleared by hand. Nothing disappears silently: every event carries a log of its input, output and the rules applied, and the action can be rolled back.

What if our system has no API?

Then we look at what exchange routes exist: exports, direct database access, mail notifications, file drops. Sometimes a workable route exists, sometimes it doesn't — and then the workflow can't be automated without changes on the system's side. That gets established during the assessment, before you have paid for anything.

Why are there different implementation levels?

Because a more complex level is not automatically a better one. A deterministic integration is cheaper, more predictable and easier to maintain than an agentic workflow — and if it solves the problem, there is no reason to add AI. An AI step earns its place where the input is unstructured text or where judgement is required. The choice follows the task, the risk and the economics, not the novelty of the technology.

Let's look at this workflow on your own material

Discuss the process, systems and bottleneck. We'll check whether the economics work and name the next step.