Why they exist
For years I took my bikes to local shops and the pattern was always the same: improvised work, parts replaced without diagnosis, craft judgement substituted for method. It wasn't negligence — it was a complete absence of rigor. Nobody had decided that this deserved to be precise.
The lack of seriousness isn't a local accident. It's structural. In Peru, mountain biking organizes itself around competitions where the same people always win, communities where everyone knows each other and nobody questions anything, and shops where diagnosis is visual and the criterion is reputation. Nobody builds tools. Nobody publishes methodology. Nobody verifies anything because nobody has decided that verification matters.
The same problem exists in international pet export, in sports nutrition, in training analysis. There is a chronic deficit of technical seriousness in disciplines where imprecision has real consequences: a poorly calibrated suspension destroys a descent, a badly executed veterinary protocol destroys a trip.
I didn't ask permission to exist in that space. I used the same Python and Monte Carlo knowledge I developed building the DCM — a metamodel that treats coherence as a measurable variable — and applied it to the most concrete problem possible: that bikes work with demonstrable precision, not faith.
Industrial Noir
Industrial Noir is not an aesthetic. Not a filter. Not a color palette chosen because it looks good on screen. It is a technical posture. The decision not to soften what engineering does not soften.
Many people are afraid of shadows. I am not. Shadows are where real mechanics live: inside a hub, at the interface between pad and disc, in the chain nobody cleans until it's too late. Black grease on the gloves is not branding — it is the record that something was done correctly.
The BikeLab Studio workshop is a room inside a veterinary clinic. No ambient music, no specialty coffee. There is a workbench with a green mat, world-class tools, grease, torque and technical judgment. That is the résumé.
The digital tools on this page are the same technical criterion of the workshop, translated into an interface. The gap between the cyclist and the engineering of their bicycle is unnecessarily large. The calculators, the diagnostics, the physical models — they exist to close that gap. Available to any cyclist in the world.
I don't seek consensus.
Consensus is the average.
And the average doesn't tune a bearing.
The method
The tools on this page were not designed from cycling. They were designed from a broader framework: the Dynamic Coherence Model (MCD), a proprietary metamodel postulating that the functional persistence of complex systems — physical, biological or organizational — is sustained by coherence between three interdependent domains: energy, information and purpose.
The application to the MTB suspension problem was direct: a dual-chamber fork is a complex physical system with coupled variables and non-linear behavior. The correct method is not a rule-of-three formula — it is a polytropic model calibrated with real manufacturer data, validated through stochastic simulation with 10,000 Monte Carlo iterations in Python.
The same principle operates in electronic shifting diagnostics and training file analysis: model the maximum possible scenarios before issuing a criterion.
Validation methods are selected based on data type and model complexity. Monte Carlo simulation to quantify uncertainty across thousands of simultaneous parameter scenarios. Sensitivity analysis to identify which variables determine the result and which are noise. Statistical bootstrap to estimate confidence intervals when real data is limited. Latin Hypercube Sampling when the parameter space is multidimensional and requires efficient coverage. Goodness-of-fit tests — Kolmogorov-Smirnov or Shapiro-Wilk — to verify that model distributions match what the real data shows. The result is a tool with quantified uncertainty, not a number without backing.
The tools
SAG Calculator v5.0
bikelabstudio.com · ES / ENThe problem it solves: the pressure-SAG relationship in MTB suspensions is non-linear. It depends on positive and negative chamber volume, internal geometry, installed tokens, rider weight, operating temperature and frame leverage ratio. Manufacturer pressure tables are catalog approximations that don't model chamber interaction. No existing commercial tool calculates real wheel SAG in full-suspension bikes through leverage curve.
Built on a dual-chamber polytropic physical model: pressure in the positive chamber follows the polytropic law PVᵞ = constant, with a proprietary γ exponent empirically calibrated in the physical range between isothermal (γ=1.00) and adiabatic (γ=1.40) compression. The negative chamber is modeled as independent back-pressure. The SAG point is solved through inverse calibration — bisection algorithm combined with differential evolution (Storn & Price, 1997) — using official Fox, RockShox, Öhlins and SR Suntour tables as ground truth. For rear shocks, integrates leverage curves from 33 catalogued frames. Each index model validated with 10,000 Monte Carlo iterations before release. Measured confidence by segment: 72–76% entry-level, 83% mid, 83–89% mid-high and high-end (threshold ±2% SAG). Full white paper →
Use the tool →Di2 & AXS Diagnostic v1.0
bikelabstudio.com · ES / ENThe problem it solves: 60% of Shimano Di2 and SRAM AXS failures are not mechanical — they are communication protocol or power management failures. Most shops diagnose them as mechanical shifters, replace parts that work, and return the problem unsolved. No app. No registration. No prior technical knowledge required.
Directed decision tree implemented as a structured JavaScript object, built in vanilla HTML with no external dependencies. Diagnosis operates in three sequential, independent layers: power (battery, charge, E-Tube wiring), communication (wireless pairing, firmware, crash mode, pogo pin, junction box) and mechanical (indexing, limits, physical adjustment). Each layer is exhausted before moving to the next. Logic navigates 8 question nodes and 26 decision options with full backtrack support. Covers Shimano Di2 11-speed and 12-speed, SRAM AXS Road (Red, Force, Rival) and SRAM AXS MTB (XX, X01, GX Eagle). Based on official Shimano E-Tube and SRAM AXS technical documentation.
Use the tool →FIT Analyzer
bikelabstudio.com · ES / ENThe problem it solves: raw .FIT data carries documented instrumental noise — ±1–2 rpm in cadence (magnet/accelerometer), ±0.5–1.5 km/h in GPS speed, ±2–5m in barometric altitude, ±1–3% in power. Diagnosing performance from that data without an uncertainty model is producing conclusions without statistical backing. No free platform quantifies that uncertainty before issuing a number. The file never leaves the browser — nothing is uploaded to any server.
Native FIT v2.1 protocol parsing (Garmin FIT SDK) in pure JavaScript, fully local processing. The tool segments the session by temporal continuity, applies an instrumental uncertainty model per data source, and calculates 5 primary metrics with Monte Carlo validation and dynamic thresholds calibrated by discipline, suspension type, drivetrain and wheel diameter: (1) local cadence variance, (2) effective development proxy speed/cadence, (3) Climbing Stability Index (CSI), (4) Variability Index NP/AP, (5) cardiac decoupling Pw:HR. The white paper cites 8 verifiable DOIs in scientific literature. Full white paper →
Use the tool →The numbers
3 tools in production. 10 brands of suspension catalogued. 132 models of MTB suspension indexed. 33 frames with leverage curves. 10,000 Monte Carlo iterations per model before release. 5 primary metrics of performance analysis with instrumental uncertainty model. 8 decision nodes covering the full failure tree for electronic drivetrains. 10 DOIs verifiable in the white paper corpus. 2 ScholarlyArticles published with explicit methodology. Coverage: XC, Trail, Enduro, DH, E-MTB, power analysis, electronic diagnostics. Available in Spanish and English. Free access for any cyclist in the world.
The same method. Different problems. Tools that work because they are built from verifiable physics and statistics, not from shop intuition.
Built with workshop-grade rigor.
Available anywhere.
Zoovet Travel · International pet export
Pet Travel Planner ICD-Travel
zoovettravel.com/pet-travel-planner/The problem it solves: taking a pet abroad involves dozens of variables that change by country, airline, species and calendar — FAVN serology with specific time windows, zoosanitary certificates with issuance deadlines, breed restrictions, quarantine protocols, ISO microchip. A family that discovers a protocol error at the airport already failed months earlier. The error wasn't a knowledge gap — it was a timing gap.
Deterministic inverse-timeline rule engine. The user enters the animal's actual dates — microchip, rabies vaccine, deworming, FAVN sample, travel date — and the tool compares them against the regulatory thresholds for the destination country loaded from a curated dataset (rules.json): minimum age, rabies vaccine validity and chronological order, FAVN waiting period, country-specific blocks. Detects biological blocks (insufficient age) and temporal blocks (insufficient days) and produces a green/amber/red traffic light with technical justification per variable. No randomness — accuracy comes from the quality of the regulatory dataset. Available in Spanish and French.
© 2026 Carlos Eduardo Ravello Joo (ORCID 0009-0007-5631-7436). All rights reserved. Code and database property of the author; reproduction or commercial use without authorization is prohibited.
Use the tool →IATA Kennel Size Calculator
zoovettravel.com/kennels-en.htmlThe problem it solves: the wrong kennel size is one of the most common reasons for pet boarding rejection at check-in. Generic breed-based tables give average sizes that ignore the actual animal's measurements. IATA requires calculation from individual measurements — and most families don't know the exact formulas or how to apply the brachycephalic correction factor.
Deterministic geometric calculator per IATA LAR 2026. The user enters the animal's actual measurements: A (nose to tail base), B (floor to elbow), C (shoulder width), D (floor to head or ears). The tool applies IATA formulas: internal length = A + B/2, internal width = 2·C, internal height = D + 3 cm. For brachycephalic breeds it applies a ×1.10 factor across all three dimensions and automatically moves up one size. Selects the smallest size in the fixed L50–L120 series satisfying all three dimensions simultaneously. Includes a breed table with average measurements for auto-fill. No randomness — the result is geometric and reproducible. Available in Spanish and French.
© 2026 Carlos Eduardo Ravello Joo (ORCID 0009-0007-5631-7436). All rights reserved. Code and database property of the author; reproduction or commercial use without authorization is prohibited.
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