SynesisLabs.ai Synesis Labs is a research-led intelligence company dedicated to turning complexity into strategic understanding and foresight.

Synesis draws from the Greek σύνεσις, meaning understanding, discernment, intelligence, practical judgment, and the mental act of bringing things together. Its root sense is important: syn- means “together,” so the word implies more than isolated knowledge — the capacity to connect signals, causes, patterns, and consequences into coherent understanding.
P_success Model P_success is a decision-grade, probability-based algorithm that sits atop LLMs and rolls many drivers of success into one number. P_success replaces gut feel with a quantified, auditable probability-of-success and a strategic pathway to mitigate risk. BETA

Project context

Internal factors (project strengths)

Elasticities (project risks)

Calibration constants

Default: k=1.0, α=1.0, β=0.20, sigmoid centre=0.10, slope=14. Tune to match your domain.

P_success P_success — Probability the project reaches escape-velocity (durable commercial traction or strategic-objective achievement). The headline output of the model: your 14 parameter values run through the equation and mapped to a 0–100% probability via a sigmoid calibrated against typical deeptech benchmarks. 50% = as likely to succeed as fail. Higher = better.
P_failure P_failure — Mirror of P_success: the probability the project does NOT reach escape-velocity. Always 100% − P_success. Displayed explicitly because in due-diligence, insurance, and risk-adjusted-return contexts the failure probability is often the more decision-relevant number. Lower = better.
Raw score Raw score — The pre-probability output of the equation: project strengths divided by project risks. Calibrated so a raw score of 0.10 maps to 50% P_success; below that the project is more likely to fail than succeed, above is the reverse. Useful for diagnostic comparisons between scenarios — small changes in raw can produce large shifts in P_success near the 50% inflection point.
Δ vs baseline
Adjust parameters to see the equation evaluate.

Scenarios

BenchmarksA benchmark can be a direct competitor, an opponent, an industry standard / legacy incumbent, an aspirational comparator, or an internal sister project — anything you score on the same 14-parameter framework to compare your project against.

Reference documents

Drop files or click to upload
PDF, DOCX, XLSX, CSV, TXT, MD, JSON

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Tips

Try: "What's my P_success?", "Run scenario X", "Compare to [benchmark]", "How sensitive is the result to [parameter]?"