Profit leakage diagnostic: turn suspected economic loss into an evidence-supported recovery case.
Aigenrix starts with signals, not conclusions. The Sprint tests the most material loss hypotheses against your financial and operating evidence, quantifies only what can be defended, and builds an intervention case where the economics justify action.
Improving operational efficiency isn't about cutting costs or automating faster. It starts with identifying which processes are eroding margin, consuming capacity or creating avoidable operating costs — and quantifying the impact before anything changes.
30 minutes, free and without obligation. The purpose is to assess whether a Profit Recovery Sprint is justified.
Four places economic value is commonly lost
Margin leakage
Margin erosion, unprofitable clients or projects, pricing that no longer matches delivery cost, discount leakage, and avoidable procurement or delivery cost.
Working-capital inefficiency
Extended receivables, invoicing delays, disputes, excess inventory and a credit policy that ties up cash — without automatically improving profit.
Revenue leakage
Slow lead response, missed sales opportunities, poor conversion, and inquiries that go cold before anyone follows up.
Operational inefficiency
Workflow bottlenecks, unnecessary handoffs, coordination overhead, and excessive manual work or management attention consumed by tasks that don't require judgement — a released hour has value only once it is actually redeployed or its cost is actually reduced.
Operational efficiency starts with the economics
Real operational efficiency doesn't start with automation. It starts with testing where margin, capacity and revenue may be lost and quantifying what the evidence supports. Once a causal explanation is supported by evidence, the process can be redesigned — and automation applied only where the economics justify it.
Illustrative composition, not measured data — the Sprint establishes your actual figures.
How the Sprint works
Establish the economic baseline
Current financial and operational performance, read from the systems you already run — the reference point every later number is measured against.
Generate and prioritize loss hypotheses
Automated screening of your data, with public and client-provided context, surfaces signals of possible loss. Public information only prioritizes what to investigate; it never establishes a monetary finding by itself.
Request the minimum evidence needed
For each material hypothesis, only the data needed to confirm or reject it — not a blanket data request.
Validate or reject suspected losses
Each hypothesis is tested: some survive, some are rejected, some stay unresolved for lack of evidence — and all three are reported. A benchmark gap can prompt a question; it does not prove a loss.
Quantify defensible exposure and recoverability
Calculate what is provable, estimate what can be defensibly estimated, and separate economic exposure from the expected recoverable range.
Test likely operating causes
Trace each financial symptom to what is causing it in operations, and check that explanation against the evidence — where the data cannot decide between explanations, you see both.
Build intervention economics
For interventions the evidence supports: cost, expected benefit range, payback assumptions, dependencies, risk and confidence. Human review decides where business judgment is required.
Define how realized value will be measured
The baseline, KPIs and measurement period used to report what actually changed after implementation.
What we need from you
Access to the numbers that already exist in your business: P&L or management accounts, time or activity data where you track it, CRM or pipeline exports, and a walkthrough of the workflows in question. No new instrumentation required to start.
Six outputs, in three documents
Evidence & exposure report
What survived the evidence test — and what did not.
- →Evidence-supported opportunity map
- →Economic exposure model, with source and calculation lineage
- →Rejected or unresolved hypotheses, stated as such
Mechanisms & intervention cases
Why it is happening, as far as the evidence supports — and what to do about it.
- →Cause / mechanism assessment, including material unresolved alternatives
- →Prioritized intervention cases: what to change, dependencies and risk
- →Technology recommended only where the economic case justifies it
Recoverable range & measurement plan
The figures your CFO asks for — labelled for what they are.
- →Expected recoverable-value range, with assumptions and confidence
- →Investment and payback assumptions, clearly labelled as estimates
- →Measurement plan for realized value after implementation
No material recommendation without an investment case
For significant interventions, Aigenrix evaluates the following before recommending a euro of spend:
- →Baseline
- →Evidence-supported causal mechanism
- →Annual economic exposure
- →Expected recoverable value
- →Implementation cost
- →Expected benefit range
- →Payback period
- →Implementation risk
- →Confidence level
- →Validation KPI
- →Measurement period
- →Go / stop criteria, where a pilot is required
A recommendation is not an opinion. It is a hypothesis supported by evidence and an economic case that can be tested.
We do not ask you to trust an unsupported recommendation. Where uncertainty is material, we test the intervention through a limited pilot with predefined success criteria before scaling — and measure actual results against the baseline afterward.
The objective is not to guarantee that every idea will work. It is to reduce the risk of committing significant capital before the economics have been evidenced and tested.
The Sprint decides what happens next
Not every finding justifies an AI implementation. Where the economics are material, the Sprint feeds directly into an Operational Digital Twin or straight into Deep AI Automation. Where they are not, we say so — and you keep the report either way.
See how the operational model works →Publishing soon
Profit Intelligence engagements outside clinics are in progress — first results are being verified before publication.
See the Deep AI work already in production →Read how we quantify the three numbers before a project starts →
Not sure where the money is actually going?
In the Review we establish whether there is a credible leakage hypothesis and enough data to test it — and tell you honestly whether a Sprint is worth running.
30 minutes, free and without obligation. The purpose is to assess whether a Profit Recovery Sprint is justified.
or write to team@aigenrix.com