CGI and Vantor team on AI spatial intelligence for GNSS-denied operations
CGI and Vantor have formed a new alliance to deliver AI-enabled spatial intelligence and situational awareness for missions where GNSS is unavailable or degraded. The plan: combine CGI's artificial intelligence, edge computing, and visual analytics with Vantor's Tensorglobe spatial intelligence platform and its Raptor navigation and geolocation products.
The goal is straightforward-faster, more confident decisions and higher operational resilience when satellite signals are jammed, spoofed, blocked, or simply not there. If GNSS is core to your CONOPS, this matters. For context on GNSS and why it can fail, see guidance from the European Space Agency and interference notes from GPS.gov.
What's in the stack
- AI-driven analytics: CGI Machine Vision for imagery and CGI SignalSense for signal intelligence.
- Edge processing to run models on-platform with low latency and limited bandwidth.
- Secure, scalable access to satellite data and space-based sensing feeds.
- Vantor Tensorglobe for spatial intelligence orchestration and services.
- Vantor Raptor for assured navigation and coordinate generation in GNSS-denied or degraded conditions.
By fusing CGI's Machine Vision and SignalSense with Tensorglobe, the partners aim to sharpen imagery analytics and high-precision geopositioning across multiple use cases. Integration with Raptor is expected to improve coordinate accuracy and confidence for users who must operate without reliable satellite navigation.
Operational outcomes you can expect
- Shorter decision loops with on-edge analytics and prioritized intel feeds.
- Higher PNT assurance when GNSS is jammed or spoofed-less drift and fewer dead-reckoning errors.
- Cleaner common operating picture through fused imagery, signals, and space-based data.
- Interoperable and sovereign options to meet national requirements and coalition needs.
- Reduced bandwidth dependence by pushing processing closer to the sensor.
The companies see immediate fit for defence, national security, and environmental missions where agility and precision are non-negotiable. Target markets include the UK, Europe, and allied customers that require AI at the edge with space-based situational awareness.
"As governments and industry organisations look to improve resilience and responsiveness, integrating near real time space based intelligence into digital command and control networks will be key to achieving decision advantage," said John Hanley, Senior Vice President Consulting Services, Secure Mission Critical Solutions, at CGI in the UK.
"Collaborating with CGI allows us to extend the reach of Vantor's technology and apply it to new use cases that demand both agility and precision. Our combined capabilities will help defence and civil government customers derive actionable intelligence faster and more securely, supporting safer operations and smarter use of global data assets," said Anders Linder, General Manager, Vantor International.
If you lead Operations, here's how to act on this
- Map your GNSS-denied scenarios (urban canyons, contested airspace, subterranean, high-latitudes) and define acceptable PNT error budgets by mission type.
- Inventory edge compute at the point of collection (UAS, UGV, manned platforms, ground nodes). Note GPU/NPUs, storage, and power constraints.
- List your primary sensors and comms paths. Identify where on-edge fusion would cut latency or bandwidth.
- Set validation metrics now: time-to-fix, geolocation CEP, image exploitation throughput, false-positive rates, and mission decision times.
- Plan red-team trials with controlled jamming/spoofing to test resilience and failover behavior.
- Align on data governance and sovereign hosting requirements early to avoid rework in accreditation.
- Clarify integration to current C2/C3I systems and data models (STANAGs, OGC standards, message buses).
- Stage procurement in phases: pilot on a representative platform, expand to a mission set, then scale fleet-wide with TTP updates and training.
Expect the partnership to pitch integrated offerings that plug into existing command networks while adding assured positioning and AI-based insight at the sensor edge. If done right, that means fewer blind spots, quicker tasking, and higher mission confidence when GNSS can't be trusted.
Want practical frameworks and checklists for deploying AI at the edge in operational settings? Explore AI for Operations.
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