Computer Vision Layer

Turn vision into machine-readable data

Turn vision into
machine-readable data

Nabla is built on a fully proprietary vision model optimized for

maximum accuracy, minimal compute, and low-latency inference at the same time.

Focus

Dynamically target high-value visual regions to maximize accuracy and slash compute costs.

Adaptive processing.

Chain-of-Focus (CoF) targeting

Quadratic cost bypass

Low-resolution initialization

Interpret

Extract rich, generalizable context and precise predictions from complex visual inputs.

Global context mapping

Recurrent refinement

Rich visual representations

Overfit resistance

Deploy

Run highly capable transformer models in real-time across resource-constrained environments.

Edge-ready inference

Real-time execution

Efficient scalability

Maximized performance

Step 1: Focus

Step 2: Interpret

Step 3: Deploy

Focus

Dynamically target high-value visual regions to maximize accuracy and slash compute costs.

Adaptive processing.

Chain-of-Focus (CoF) targeting

Quadratic cost bypass

Low-resolution initialization

Interpret

Extract rich, generalizable context and precise predictions from complex visual inputs.

Global context mapping

Recurrent refinement

Rich visual representations

Overfit resistance

Deploy

Run highly capable transformer models in real-time across resource-constrained environments.

Edge-ready inference

Real-time execution

Efficient scalability

Maximized performance

Step 1: Focus

Step 2: Interpret

Step 3: Deploy

What is Nabla?

The next-generation vision foundation model

Built on our proprietary chain-of-focus architecture, Nabla delivers state-of-the-art visual understanding with minimal compute, designed specifically for real-time inference.

What is the problem?

Vision models are too heavy to scale.

Exponential Compute Costs

Treating every pixel equally leads to massive memory usage and unsustainable processing expenses.

Blocked by Hardware

Slow inference times make deploying state-of-the-art AI on edge devices nearly impossible.

<1% of video data is analyzed in real-time.

80% of GPU power is wasted on empty pixels.

16x compute cost just to double image resolution.

90% of edge devices can't run AI models.

How is it solved today?

Teams are forced to compromise.

Downsizing Inputs

Shrinking the image reduces memory load, but erases the critical visual context required for complex tasks.

Full-Resolution Processing

Running the entire image preserves details, but overloads the hardware and makes edge deployment impossible.

Current architectures force a strict trade-off: downsample inputs and destroy critical spatial context, or process full resolutions and bottleneck real-time inference.

How does Nabla solve this?

The compromise is over. Nabla reimagines vision from the ground up, seamlessly balancing infinite detail with real-time speed in a single, fluid architecture.

DYNAMIC ATTENTION

Automatically isolate high-entropy visual cues, dedicating processing power exclusively to areas with dense contextual data.

ADAPTIVE RESOLUTION

Perceive images with variable fidelity, eliminating the quadratic cost explosion by discarding up to 90% of irrelevant visual noise.

Who builds with Nabla?

Engineered for modern vision pipelines.

Deploy state-of-the-art visual understanding across the cloud and the edge, without scaling compute budgets.

ML Engineers

Edge Developers

Product Teams

ML ENGINEERS

Break the quadratic bottleneck.

Process complex visual data instantly. Nabla allows engineering teams to deploy foundation models without hitting memory walls or relying on massive GPU clusters.

Process complex scenes in real-time.

Eliminate exponential self-attention costs.

Save to collections for future prompts

Reuse copy across multiple projects

Case study

4K Transformer Scaling

Problem: Quadratic memory bloat in high-res ViTs.

Implementation: Dynamic high-entropy token isolation.

Result: 85% reduction in GPU VRAM overhead.

EDGE DEVELOPERS

Intelligence on any device.

Bring state-of-the-art vision to resource-constrained environments. Run complex pipelines directly on CCTV cameras, drones, and local servers.

Operate with an ultra-low memory footprint.

Eliminate cloud latency and bandwidth issues.

Deploy natively on constrained hardware.

Analyze live feeds with variable fidelity.

Case study

Drone-Based Powerline Inspection

Problem: 4K video analysis drains drone battery in 15 minutes due to continuous GPU load.

Implementation: Processing only the high-contrast metal cables at 4K; ignoring the sky and trees.

Result: 45-minute flight time (3x increase) on a single battery charge.

PRODUCT TEAMS

Infinite scale. Zero hardware bloat.

Turn massive, untouched video streams into actionable, machine-readable data without the unsustainable cloud computing costs.

Slash visual processing infrastructure costs.

Unlock insights from unanalyzed video data.

Scale vision pipelines predictably.

Automate complex visual inspections.

Case study

Smart Warehouse Ops

Problem: $15k/month cloud GPU bill to track high-speed sorting labels.

Implementation: 4K focus on package barcodes; low-res on static conveyors.

Result: 80% OpEx savings; 100% sorting accuracy maintained.

ML ENGINEERS

Break the quadratic bottleneck.

Process complex visual data instantly. Nabla allows engineering teams to deploy foundation models without hitting memory walls or relying on massive GPU clusters.

Process complex scenes in real-time.

Eliminate exponential self-attention costs.

Save to collections for future prompts

Reuse copy across multiple projects

Case study

4K Transformer Scaling

Problem: Quadratic memory bloat in high-res ViTs.

Implementation: Dynamic high-entropy token isolation.

Result: 85% reduction in GPU VRAM overhead.

EDGE DEVELOPERS

Intelligence on any device.

Bring state-of-the-art vision to resource-constrained environments. Run complex pipelines directly on CCTV cameras, drones, and local servers.

Operate with an ultra-low memory footprint.

Eliminate cloud latency and bandwidth issues.

Deploy natively on constrained hardware.

Analyze live feeds with variable fidelity.

Case study

Drone-Based Powerline Inspection

Problem: 4K video analysis drains drone battery in 15 minutes due to continuous GPU load.

Implementation: Processing only the high-contrast metal cables at 4K; ignoring the sky and trees.

Result: 45-minute flight time (3x increase) on a single battery charge.

PRODUCT TEAMS

Infinite scale. Zero hardware bloat.

Turn massive, untouched video streams into actionable, machine-readable data without the unsustainable cloud computing costs.

Slash visual processing infrastructure costs.

Unlock insights from unanalyzed video data.

Scale vision pipelines predictably.

Automate complex visual inspections.

Case study

Smart Warehouse Ops

Problem: $15k/month cloud GPU bill to track high-speed sorting labels.

Implementation: 4K focus on package barcodes; low-res on static conveyors.

Result: 80% OpEx savings; 100% sorting accuracy maintained.

Designers

Writers

Marketers