Precision Gravity Sensor Technology
Commercial referenceDEF-030
Precision gravity measurement, simulation and evidence-assurance technology for qualified sensing, geodesy, infrastructure and sovereign programs.
Who this is for
Qualified sensing programs, geodesy and survey teams, critical-infrastructure operators, defense primes and sovereign integrators.
The problem you already have
Stable-looking measurements can be quietly wrong
A gravity-sensor reading can look numerically stable while calibration drift or environmental noise silently degrades the confidence that a downstream geodesy or infrastructure decision is actually relying on.
A reading that never drifts can still be wrong from the very first measurement
Drift monitoring watches for values changing over time — but a sensor miscalibrated from day one, reporting a consistent and stable offset, never trips a drift alarm because there's no "before" to compare against. Time-series consistency and physical correctness are two different questions.
This works from measurement and calibration logs, not sensor-design data
Never requested, and structurally rejected if submitted: sensor-design schematics, classified calibration constants, or raw platform control access.
Real-World Operational Scenario: The Reading That Never Drifted and Was Still Wrong
Scenario: A gravity sensor in a geodetic network reports stable values for months.
No time-series anomaly ever triggers — the readings are internally consistent with their own history the entire time.
WITHOUT this check: Calibration-drift monitoring only watches for values changing over time. A sensor that's been subtly miscalibrated since installation — consistently reporting the same wrong offset — never trips a drift alarm, because it was never right to begin with.
WITH this check: The reading is also checked for gradient consistency against neighboring sensors and the known background gravity model — not just consistency with its own past. A value that's perfectly stable over time but doesn't fit the physical gradient relationship with its neighbors gets flagged, even though it never "drifted" in the conventional sense.
What BivectorAI Provides
Spatial Consistency Checked, Not Just Time-Series Stability
Readings are checked against the physical gradient relationship expected with neighboring sensors and the known background model — catching a miscalibration that was wrong from the start and never drifted.
Environmental Noise Separated From Genuine Signal Change
Platform vibration, environmental interference, and genuine measurement change are modeled separately, so a noisy-but-correct reading isn't confused with a quietly-wrong one.
Confidence Classification, Not a Single Pass/Fail
Measurement quality is reported as a graded confidence classification tied to signal-to-noise and consistency evidence — not a binary that hides how close a reading is to the boundary.
Hash-Sealed Certificates for Post-Event Review
Every measurement-quality classification traces back to the exact calibration, gradient, and environmental evidence that justified it at the time.
What you provide
Redacted measurement logs, calibration history, and environmental-condition metadata already produced by your sensing platform. No raw sensor-design parameters are required.
What BivectorAI actually hands you
Evidence package
- measurement-quality evidence
- signal-to-noise and confidence classification
- calibration and provenance records
- reviewable assessment certificates
Integration package
Customer-safe input/output contract, local/private integration boundary, validation fixtures and reviewable evidence artifacts.
Deployment model
Qualified local, private or on-premise integration. No public Defense API and no uncontrolled public execution surface.
Technical disclosure
Customer-visible behavior and evidence are disclosed. Confidential implementation details, protected core methods and internal technical data are not published.
What we will never ask you for
Never requested, and structurally rejected if submitted: sensor-design schematics, classified calibration constants, or raw platform control access, or your system's core design data. Every submission is checked against this rejection list before any evidence logic runs — a malformed or over-broad submission is blocked and reported back to you, not silently processed.
How it actually runs
Engagement path & current acquisition terms
DEF-030 · Tier C — Strategic Sensing / Infrastructure
Secures a defined diligence and exclusive-negotiation period. Any option credit is agreed in writing as part of the signed commercial terms.
Starting integration engineering, customer-safe adaptation, local/private deployment work, validation and acceptance evidence for the agreed environment.
Scope-dependent consideration for negotiated exclusivity in a defined field, program, platform class, territory and rights scope.
Engineering support, update access, security maintenance, evidence infrastructure and agreed sustainment services.
Applied to attributable net program revenue under the definitive agreement, subject to negotiated exclusions and accounting rules.
Commercial boundary. These are indicative starting terms, not a binding offer. Final price depends on exclusivity scope, field of use, territory, sublicensing, technical-data rights, integration obligations, export-control review, support requirements and definitive agreement.
IP boundary. Exclusive FOU does not automatically transfer ownership of BivectorAI Background IP. Worldwide/all-defense rights, sublicensing, source-level technical-data rights or assignment require separate negotiated consideration.
Scope the DEF-030 diligence and integration boundary
Request DEF-030 Acquisition Discussion →Email fallback: contact@bivectorai.com
Defense technology is integrated through qualified local/private engagement. No public Defense API and no automated Defense checkout are provided.