Use cases by role.
Explore example problems across different roles, or bring us a problem of your own.
Founders & Owners
High-stakes decisions, thin data, and nobody whose job it is to check the reasoning.
Typical questions
- Should we raise now or extend runway first?
- Which of our three markets deserves the next hire?
- Is this price increase going to cost us more customers than it earns?
Why ordinary AI struggles
- Answers immediately, before the objective and constraints are known.
- Produces confident estimates where a calculation or a measurement is needed.
- Cannot tell you which unknown matters most.
How RAZOOM approaches them
- Clarifies runway, objective and risk tolerance first.
- Models cash and unit economics explicitly instead of estimating them.
- Identifies the one or two facts worth measuring before committing.
Relevant methods
- Unit-economics model
- Monte Carlo simulation
- Value of Information
- Scenario analysis
Expected future workflow
- 1Open a Case with the problem
- 2Answer the few questions that matter
- 3Review the attack plan
- 4Receive a recommendation — or the measurement to run first
Executives
Strategic questions with many stakeholders, where the framing decides the outcome.
Typical questions
- Why is margin falling in our best-performing region?
- Should we build, buy or partner for this capability?
- Which initiatives should we stop?
Why ordinary AI struggles
- Takes the question at face value, even when it is the wrong question.
- Blends evidence and opinion into one fluent paragraph.
- Leaves no record of why a conclusion was reached.
How RAZOOM approaches them
- Tests whether the question resolves the real decision; reframes it if not.
- Separates evidence from interpretation so it can be challenged.
- Records assumptions and dissent alongside the recommendation.
Relevant methods
- Causal analysis
- MCDA
- Scenario analysis
- Decision threshold analysis
Expected future workflow
- 1Bring the strategic question
- 2Agree the decision criteria
- 3Review evidence and competing explanations
- 4Keep a Decision Record that survives the meeting
Consultants
Ambiguous client problems that need a defensible structure quickly.
Typical questions
- What is actually driving this client's churn?
- Which of these growth options survives a downturn?
- Where is the value in this operating model redesign?
Why ordinary AI struggles
- Generates frameworks without testing which one fits the problem.
- Cannot show the client why the structure is right.
- Treats every hypothesis as equally likely.
How RAZOOM approaches them
- Builds a problem graph with explicit dependencies you can show a client.
- Keeps competing hypotheses alive and weighs them by evidence.
- Identifies the analysis that would most change the recommendation.
Relevant methods
- Hypothesis Ecology
- Causal inference
- Sensitivity analysis
- Counterfactual World Farm
Expected future workflow
- 1Frame the client problem as a Case
- 2Agree the problem graph
- 3Assign analyses by expected decision value
- 4Deliver a recommendation with its evidence trail
Analysts & Researchers
Questions where data exists but the right method — and the right skepticism — matter.
Typical questions
- Is this demand shift seasonal, structural or a measurement artifact?
- Which mechanism best explains the effect?
- What experiment would discriminate between our hypotheses?
Why ordinary AI struggles
- Reasons about numbers instead of computing them.
- Rarely questions the measurement itself.
- Converges on one explanation too early.
How RAZOOM approaches them
- Routes statistical questions to statistical tools.
- Checks data provenance and measurement validity before interpretation.
- Designs the smallest experiment that separates competing hypotheses.
Relevant methods
- Bayesian analysis
- Time-series analysis
- Causal inference
- Experiment design
Expected future workflow
- 1Attach the data and the question
- 2Review the proposed methods
- 3Inspect analyses with their inputs
- 4Receive conclusions with calibrated confidence
Engineers & Technical Leaders
Capacity, reliability and architecture decisions that deserve calculation.
Typical questions
- Will this queue design hold at three times peak load?
- Which failure mode should we fix first?
- Is the migration worth its risk this quarter?
Why ordinary AI struggles
- Gives plausible capacity numbers without a model behind them.
- Misses interactions between components.
- Does not quantify the risk it describes.
How RAZOOM approaches them
- Builds queueing and capacity models with stated assumptions.
- Ranks failure modes by severity, likelihood and detectability.
- Stress-tests designs against the scenarios that break them.
Relevant methods
- Queueing theory
- Capacity planning
- FMEA
- Stress testing
Expected future workflow
- 1Describe the system and the decision
- 2Validate the model inputs
- 3Review load and failure scenarios
- 4Get thresholds you can monitor
Investors
Theses and valuations that depend on a small number of fragile assumptions.
Typical questions
- Which assumption, if wrong, breaks this investment case?
- What has to be true for this valuation to make sense?
- How does the thesis hold up across downside scenarios?
Why ordinary AI struggles
- Summarizes the pitch instead of attacking it.
- Does not separate what is known from what is assumed.
- Cannot say which diligence question matters most.
How RAZOOM approaches them
- Identifies the load-bearing assumptions and attacks them first.
- Weights valuation across scenarios instead of one forecast.
- Ranks diligence questions by how much they could change the decision.
Relevant methods
- Assumption Attack
- Scenario-weighted valuation
- Regret analysis
- Value of Information
Expected future workflow
- 1Bring the thesis and the materials
- 2Review the assumption map
- 3Prioritize diligence by decision value
- 4Decide with conditions that would reopen the case
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