Interactive Beat Note Visualizer
Experience how picosecond timing creates measurable phase shifts
Observe the Phase Shift (φ) dial. Wireless timing that starts to look like fiber (LoS simulation first; hardware validation underway).
Slide to add path delay. Watch the jitter band swell or shrink as environments change.
Understanding Femtosecond-Scale Timing
Three ways to think about what we measure
Distance intuition
In 1 fs, light travels ~0.3 μm (illustrative). We're operating at scales where path-length differences become measurable.
RF intuition
At GHz carriers, tiny phase shifts correspond to tiny time shifts. We exploit this relationship to extract timing from interference patterns.
Compute intuition
This lives below a single CPU clock tick — so we don't measure "time," we measure structured phase.
Hardware Blueprint
Professional-grade dev kit targeting sub-picosecond line-of-sight precision
Physical Stack (Top to Bottom)
Performance Comparison
How Driftlock Choir compares to existing timing technologies
How Chronometric Interferometry Works
Extracting femtosecond timing from radio frequency interference
Signal Generation
Two RF signals with a precise frequency offset (Δf is implementation-specific; selectable across a wide range depending on band, SNR, and architecture) are transmitted simultaneously
Beat Note Formation
Signals interfere upon reception, creating a low-frequency beat note whose phase encodes time-of-flight
Phase Extraction
Advanced signal processing extracts the beat note phase with extraordinary precision (σφ approaches fundamental limits)
Timing Calculation
Timing precision follows: στ = σφ / (2π · fcarrier), achieving femtosecond resolution
Applications
Enabling next-generation synchronized systems and deep-tech businesses
6G JCAS Networks
Joint communication and sensing requires picosecond synchronization for coherent distributed radar
PNT-Degraded Environments
GPS-denied or GPS-unreliable scenarios demand alternative timing sources — indoor, underground, contested.
Distributed Sensing
VLBI radio telescopes, SAR imaging, and MIMO radar systems benefit from wireless femtosecond synchronization
Industrial TSN
Time-sensitive networking for robotics and automation without expensive fiber infrastructure
Financial Networks
High-frequency trading and market data feeds benefit from verifiable sub-nanosecond correlation across sites
Space Systems
Deep space communication, navigation, and inter-satellite links with extreme precision
What E1–E27 Tell Us
From validated physics to deployment-ready architectures and revenue lines
Physics & Scaling Laws Validated
E1–E3 & E17–E18 confirm the physics: sub-picosecond timing is theoretically bounded and achievable. Scaling laws (1/SNR, 1/f) are validated across 100+ test points, matching CRLB predictions.
Network-Scale Synchronization
E4–E7 & E16 identify stability limits. Simple consensus scales to ~30 nodes; hierarchical approaches enable 100+ nodes while maintaining picosecond precision.
Simulated Mobility & Multipath Stress
We model Doppler, clock noise, and multipath regimes — tracking when the estimator holds, degrades, or refuses. We don't pretend multipath is solved. We map where it breaks.
Toward Products & Revenue
E12–E13 & E21 focus on drift tracking, cost tiers, and MAC efficiency, directly informing dev kits, OEM modules, and SaaS nodes modelled in our 5-year financial projections.
Together, E1–E27 say: this isn’t a single clever demo, it’s an architecture that survives noise, scale, faults, motion, and clutter. The next chapters—E14/E15 hardware, PTP bridges, and FPGA acceleration—turn that architecture into deployable infrastructure.
Business Potential
Modeled outcomes are scenario-dependent
Modeled Outcomes
A range of outcomes exists — the constraint is hardware validation + channel robustness, not addressable demand.
Markets: 6G infrastructure, industrial TSN, coherent sensing, PNT-degraded scenarios.
Unit Economics (Illustrative)
SaaS timing nodes show favorable CAC payback and strong LTV/CAC ratios in early models — contingent on deployment density and churn assumptions.
What Determines Upside
Near-term: Hardware loopback results, multipath robustness
Mid-term: OEM partnerships, standards participation
Long-term: Density of deployed timing infrastructure
View modeling assumptions →
Adoption curves, pricing tiers, and milestone gates available on request. Detailed model shared under NDA.