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Aplora

Fast to evidence. Rigorous after validation.

We don't start building until it's clear which number the work should move. And we don't call it finished until that number has been measured on the real process.

Two people at a whiteboard with a process diagram drawn on it

There are more AI tools every month. Manageable outcomes are rarer.

Tools used separately

  • Chatbot
  • Transcription
  • Copy generator
  • Dashboard
  • CRM plugin
  • Meeting notes
  • Inbox assistant

An embedded loop

  1. 1Process
  2. 2Decision
  3. 3Check
  4. 4Metric

The metric returns to the process — otherwise it is not a loop

  • People use AI individually, each in their own way.

    Individual tasks get faster, but business metrics don't move: the gain dissolves inside the process.

    Can you name one workflow where this changed a number?

  • The pilot happened. The rollout didn't.

    The budget is spent, the prototype works in a demo, and nobody uses it in daily work.

    Who was meant to own the solution after the pilot?

  • It isn't clear which workflow to take first.

    The decision keeps slipping while competitors accumulate operational advantage.

    What criteria do you use to compare the candidates?

  • Growth demands a disproportionate increase in manual work.

    Every new client adds operational load, and margin falls as you scale.

    How many people would you need to hire to double volume?

The whole model on one screen

  1. Diagnosis

    Key questionWhere is the problem
    ArtifactProcess map
    DecisionIs there a task here

  2. Business case

    Key questionWhat does it cost
    ArtifactImpact model
    DecisionIs it worth validating

  3. Prioritisation

    Key questionWhat comes first
    ArtifactBacklog and scoring
    DecisionWhich initiative

  4. Proof of Value

    Key questionDoes the hypothesis hold
    ArtifactPrototype and data
    DecisionContinue / change / stop

  5. Implementation

    Key questionDoes it work in the process
    ArtifactProduction workflow
    DecisionGo-live

  6. Adoption

    Key questionAre people using it
    ArtifactStandard and training
    DecisionMake it stick

  7. Measurement

    Key questionIs there an effect
    ArtifactDashboard
    DecisionScale or stop

AI Opportunity Assessment

Two different things that often get conflated. The difference is scope of work, not quality of attention.

Free fit meeting

30–45 minutes
  • Clarifying the business problem
  • Checking the fit
  • An initial view of the data and systems
  • A decision on whether a next step makes sense

  1. 8 min · Context

    What the business is and where it hurts

  2. 20 min · Walking the process

    Steps, volumes, where time is lost

  3. 10 min · Data and constraints

    What exists and what is missing

  4. 7 min · Verdict and next step

    Whether this is worth doing — said out loud

The outcome of this call is not a full opportunity map of your company. One call does not produce that, and promising otherwise would not be honest.

Paid assessment / AI Blueprint

  • Interviews with process owners
  • A study of the data and the systems
  • A fixed baseline
  • An opportunity map
  • Economic hypotheses
  • Prioritisation of initiatives
  • Architecture options
  • Risks
  • A Proof of Value and implementation plan

Proof of Value is not an open-ended pilot

A pilot without boundaries runs until patience runs out. A Proof of Value has every boundary defined before it starts — and one of them permits stopping.

An open-ended pilot: No boundaries; it ends when patience runs out

Proof of Value

  • A limited scope
  • A baseline fixed in advance
  • A target value
  • A deadline
  • A budget
  • A data set
  • A stop/go criterion
  • A named owner on the client side

What 'done' actually means

A working prototype is not done. Done is when the process has changed, not when the code runs.
  1. 1Embedded in the process
  2. 2People genuinely use it
  3. 3A solution owner is named
  4. 4Monitoring is in place
  5. 5The result is measured
  6. 6There is support and a development plan

All six are met — Done

If even one is missing, the work is not finished

AI Performance Office

Ongoing engagement for companies that would rather not build this function in-house from scratch. It is a separate format, billed separately.
  1. 01AI backlog
  2. 02Regular prioritisation
  3. 03Pilot management
  4. 04Budget and total cost of ownership control
  5. 05Quality monitoring
  6. 06Solution development
  7. 07Executive reporting

Who is responsible for what

A project without shared responsibility never reaches implementation. This is not legal boilerplate — it is the condition under which the work makes sense at all.

Aplora is responsible for

  • Methodology
  • Solution quality
  • Technical delivery
  • Transparency
  • Project control
  • Measurement

The client is responsible for

  • Data
  • Process owners
  • Approvals
  • Internal decisions
  • Rolling the change into how the team works

Stopping in time is also a result

A stretched pilot that is always about to show an effect costs more than an honest stop. Which is why the stop criterion is defined before validation begins, not at the moment it becomes awkward.

  • The hypothesis didn't hold on the data

    we record exactly what failed

  • There isn't enough data to conclude

    we say so rather than filling the gap with assumption

  • The process changed mid-validation

    we revisit the baseline rather than bend the result

  • There is an effect, but it doesn't cover the cost of ownership

    we count that as a negative result

Frequently asked questions

How long is the path to a first result?

It depends on the workflow, on data availability and on how quickly access is granted — three variables we don't control. Naming a timeline before the assessment means guessing, and we make a point of not using phrasing like 'first results in N weeks' without conditions. In the free meeting we give a range for your specific case and explain what it depends on.

What if the workflow has no owner?

Then that is the first thing to resolve, and to resolve internally — we cannot appoint an owner on your behalf. Without someone accountable for the process and empowered to change it, rollout hits approvals and stalls after the pilot. It is one of the reasons we say 'not a fit right now' — and we say it before you have paid for anything.

Can we stop after the assessment?

Yes, and it is a normal outcome. The assessment is a standalone product: you keep the baseline, the opportunity map, the economic hypotheses and a plan you can act on yourselves or with another vendor. We don't design the assessment as a funnel you can't leave.

We have our own engineers. Why you?

Often there is no reason, and we will say so. An in-house team wins on context and on support cost. We are useful where the need is experience in framing an economic hypothesis and measuring effect rather than writing code: choosing the workflow, fixing the baseline, defining the stop criterion, designing quality control. Sometimes the right outcome is that we do the assessment and your team builds.

Let's start with a free conversation about one specific workflow

Thirty to forty-five minutes. If the task isn't a fit for AI — or is, but not yet — we'll say so on the call.