Multi-Sensor Defense Integrity
Commercial referenceDEF-005
Quote DEF-005 when requesting information, licensing, technical review, or acquisition discussion.
Who this is for
Sensor fusion leads and defensive awareness program managers who need calibration-state and contradiction evidence across a multi-sensor network.
The problem you already have
Sensors that look independent can share the same blind spot
Common-mode dependencies between sensors — a shared calibration source, a shared power bus, a shared processing step — can make several "confirming" sensors really just be one point of failure repeated. Treating sensor count as confidence misses exactly that.
Disagreement between sensors gets recorded, not resolved by majority vote
When sensors disagree, defaulting to whichever reading is more common isn't the same as understanding why they disagree — a contradiction between two sensor readings is evidence in its own right, and averaging it away throws that evidence out.
Two fusion algorithms disagreeing gets resolved by picking a winner, which throws away the disagreement itself
Different fusion methods — a Kalman-family estimator versus a Bayesian approach, say — can process the same sensor data and reach different conclusions. Automatically trusting whichever one "ranks higher" discards the fact that they disagreed at all, when that disagreement is itself information worth keeping.
This doesn't require raw sensor feeds or calibration constants
Never requested, and structurally rejected if submitted: raw sensor feeds outside the agreed contract, classified calibration constants, or sensor control access.
Real-World Operational Scenario: Three Healthy Sensors, One Wrong Answer
Scenario: Radar, EO, and RF sensors each independently classify a track the same way.
Three different sensor types each report healthy status and each classify a track as the same object type.
WITHOUT this check: Three-sensor agreement is treated as strong confirmation, aggregated into a high-confidence classification.
WITH this check: Two of the three sensors are found to share the same upstream calibration reference, recently updated with an error. The shared dependency is flagged, and confidence is downgraded to reflect only two independently-rooted confirmations, not three.
What BivectorAI Provides
Sensor Count Isn't Confidence Until Independence Is Checked
Common-mode dependencies — shared calibration, power, or processing — are checked before agreement counts as corroboration.
Disagreement Is Evidence, Not Noise to Average Away
Contradictory sensor readings are preserved as first-class evidence, not resolved by majority vote.
Degraded Sensors Are Downweighted, Not Silently Dropped
A degraded sensor's reduced authority is explicit in the output, not hidden.
Disagreement Between Fusion Methods Is Recorded, Not Voted Away
When two fusion approaches process the same evidence and reach different conclusions, that disagreement is stored as its own flagged state — never silently resolved by treating one method's ranking as automatically correct.
Evidence for Fusion Review, Not an Engagement Decision
Outputs are sensor-integrity evidence, never engagement or firing decisions — and never require raw sensor feeds outside the agreed contract or classified calibration constants.
Prove the Gate Before Broader Integration (Zero-Risk Evaluation)
Do not buy the claim. Test the gate.
Feed synthetic three-sensor data where two sensors share a hidden calibration dependency with a known error. It succeeds only if it flags the shared dependency and downgrades confidence accordingly.
This initial trial measures common-mode-dependency detection and false-corroboration prevention. Commercial scale-up occurs only after measured results prove value in your own lab environment.
What you provide
Redacted sensor-health, calibration-lineage, and cross-sensor classification records — which sensors share upstream calibration or processing dependencies, agreement/disagreement history — already logged by your fusion pipeline.
What BivectorAI actually hands you
- Common-mode dependency classification across your sensor set.
- Cross-sensor agreement and contradiction records, preserved rather than averaged away.
- Machine-readable output for API or dashboard integration.
- Traceable report for internal review, acceptance, audit or pilot comparison.
What we will never ask you for
Never requested, and structurally rejected if submitted: raw sensor feeds outside the agreed evidence contract, classified calibration constants, or sensor 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-005 · 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.
Controlled commercial surface
A controlled backend assessment surface currently exists for this capability.
Private / on-premise integration onlyNo automated PayPal checkout. Access is granted only under negotiated commercial entitlement.
Rights structure
Exclusive rights are defined by field of use, program scope, territory, term, sublicensing and technical-data rights. Underlying BivectorAI Background IP remains owned by BivectorAI unless expressly assigned.
Annual sustainment
May include updates, integration support, security maintenance, evidence infrastructure, validation support and agreed engineering services.
Revenue participation
Applied to attributable net program revenue under a definitive agreement with negotiated exclusions, reporting and audit terms.
Scope the DEF-005 diligence and integration boundary
Request DEF-005 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.