Solutions
Applied at the hardest points.
Fantasma Synergy products are deployed where the stakes are high — climate, robotics, healthcare, and finance.
Reading a planet in real time.
Climate Intelligence
Distributed sensing at climate scale means 10,000 nodes, terabytes per day, and anomalies that happen once a decade. Edge Lattice makes the network intelligent enough to know the difference between a faulty sensor and a category-5 event.
The problem
Centralized anomaly detection was failing. 4.2 false positives per day. Detection latency of 4.2 hours. The data volume made cloud-first architectures economically impossible.
The solution
Deployed Edge Lattice across ocean buoy networks. Local inference per node, mesh consensus for anomaly validation. Only confirmed events escalate to the cloud.
Before → After
Decisions at the speed of physics.
Autonomous Systems
A robot that waits for the cloud to make a decision isn't autonomous — it's a remote-controlled puppet with latency. Edge inference enables real autonomy: decisions made locally, in under 12 milliseconds, on a 4W payload computer.
The problem
Autonomous inspection drones hitting network partitions in the field, causing mission aborts. 89% of decisions were round-tripping to a cloud endpoint. Each round-trip cost 180–400ms.
The solution
Neural Fabric and Edge Lattice deployed on-payload. Critical inference runs on-device. Cloud is consulted only for model updates and telemetry egress.
Before → After
The instrument that anticipates the surgeon.
Healthcare Imaging
Surgical robotics is a solved problem for the tool layer. The unsolved layer is intent — knowing what the surgeon needs before they reach for it. Human Signal's intent prediction runs in 6ms, on-device, with zero biometric data leaving the operating room.
The problem
Reaction-based surgical assistance added cognitive load rather than reducing it. Surgeons had to consciously control the robot rather than collaborating with it.
The solution
Human Signal multimodal intent recognition, trained on 800 hours of annotated procedures. Predicts next action at 91.3% accuracy 400ms ahead of onset.
Before → After
Alpha that doesn't arbitrage itself away.
Financial Intelligence
High-frequency financial ML has a unique problem: the model's predictions affect the market it's predicting. Naive training produces models that lose their edge within 90 days. Our feedback-aware training loop extends signal half-life by 165%.
The problem
ML-driven alpha signals were decaying in 73 days on average. Retraining was expensive and the models were systematically overfit to stale distributions.
The solution
Neural Fabric inference stack with a feedback-aware training loop that simulates market impact of predictions during training. Signal distributions stabilized.
Before → After
When reliability is not negotiable.
Dual-Use Intelligence
In dual-use applications, the cost of a wrong inference is not a refund request — it's a mission failure. Edge Lattice deployments in contested environments require Byzantine fault tolerance, cryptographic attestation, and offline-first operation for weeks at a time.
The problem
Existing inference infrastructure assumed reliable connectivity and cooperative nodes. Field deployments required a fundamentally different threat model.
The solution
Edge Lattice with mTLS mutual attestation, Byzantine fault tolerance (n/3 compromised nodes), and indefinite offline operation with cryptographically-signed delta sync on reconnection.
Before → After