Accuracy Centre

Methodology — runway model v1.0.0

The business cash-runway model answers one resolvable question: will cash stay above zero for the stated horizon?

• Simulation: 5,000 monthly cash paths. Revenue follows multiplicative growth with normally-distributed monthly shocks at the user's stated volatility; costs grow at the stated rate; committed one-off cash events land in their stated months.
• Determinism: the random seed is derived from the inputs (or supplied explicitly), so identical inputs always produce identical results. This is enforced by automated tests.
• Uncertainty display: probabilities are shown as bands rounded to 5-point steps, widened by Monte-Carlo standard error and by input quality. Raw values are stored only for calibration scoring.
• Evidence gates: the model refuses to run when core inputs are marked unknown, or when every input is an assumption. It does not fabricate values.
• Confidence: derived structurally from how many core inputs are facts versus assumptions — never from model enthusiasm.
• Adversarial review: a separate challenge module (different method: straight-line burn heuristic plus a rule library) attacks every result; material disagreement between the two models is recorded and shown.
• Resolution: the outcome criterion and due date are frozen at prediction time and cannot be edited afterwards. Outcomes are scored with the Brier rule.

Methodology — career model v1.1.0

The career-change model answers only the resolvable question: if you make the change, does your financial buffer survive the income transition? Income ramps linearly to your expected level over your stated months, with monthly volatility shocks; essential costs drain the buffer; 5,000 seeded paths. Same guarantees as runway: deterministic seeds, evidence gates, banded display, frozen resolution criteria. Version 1.1 delivers the independent-heuristic cross-check promised at 1.0: a deterministic straight-line drain model (separate code path, no Monte-Carlo) attacks every result, and material disagreement between the two is recorded and shown.

Stated honestly: no defensible base rate exists for career-change success (definitions vary too much to pool), so this model does not offer one. Context figure only: ~2.9m UK workers changed jobs in 2025 (Indeed Hiring Lab, citing ONS). Known weaknesses: partner income, benefits during transition, emergency costs and the return-to-employment option are not modelled.

Methodology — opportunity model v1.0.0

The major-purchase model answers only the resolvable question: if you take on this upfront cost and recurring commitment now, does your financial buffer survive the horizon? The upfront cost leaves the buffer at month zero; thereafter income varies with monthly volatility shocks against fixed essential costs plus the commitment; 5,000 seeded paths. It also reports the largest monthly commitment at which your median path still survives, and a waiting analysis that honestly notes it does not model price rises while you wait. Same guarantees as the other models, including a paired independent surplus-arithmetic cross-check.

Stated honestly: no defensible base rate exists for whether a major purchase "works out" (outcomes vary too much to pool), so this model does not offer one. Context only: 13.1 million UK adults (24%) had low financial resilience in May 2024, and 42% could not cover 3+ months of living costs if their main income stopped (FCA Financial Lives 2024). Known weaknesses: price and interest-rate changes while deciding, one-off repair shocks, resale/exit value, and essential-cost inflation are not modelled.

How accuracy will be reported

• Calibration by domain (runway, career, opportunity) and by horizon bucket (0–3m, 3–12m, 12m+) — never one universal percentage.
• Reliability tables (mean forecast vs actual frequency per probability bin), sample sizes, and confidence intervals.
• Per model version, so improvements and regressions are visible across versions.
• Retractions and corrections, listed on this page permanently.

Reproducibility — a worked receipt

The example on the homepage is a real engine run. Anyone entering the same inputs reproduces it exactly:

Run it yourself →

Reference data & source registry

No source is ever described as "live" unless an actual connection exists and has recently refreshed. Today, none are live; all reference data is bundled snapshots with retrieval dates.

Known weaknesses (runway v1.0.0)

• Revenue shocks are modelled as smooth monthly volatility; single lumpy events (losing an anchor client, a tax bill) are only captured if entered as committed cash events.
• Receivables timing (booked vs banked revenue) is not modelled.
• The bundled reference class pools all UK industries and sizes; sector-specific base rates are not yet included, and the 1-year survival rate was not captured in our snapshot, so it is not shown.
• Outcome data is self-reported until verified-evidence attachment ships; self-reporting can bias calibration optimistically.
• No calibration history exists yet — the model's real-world track record is unproven, which is exactly what the outcome loop exists to fix.

Model versions

Boundaries

LEXUN models business and career decisions. It refuses medical, legal and immigration questions, routes personal-crisis language to human help, and provides decision intelligence — not financial, legal or medical advice.