Bring ideas life with the world’s first graphics compiler for AI.
Describe what you want – AI generates the data, content and code, we compile and render it on‑the‑fly giving users low latency, buttery‑smooth next-generation experiences.
Create any UI, visual, scene, or immersive animations with stable, flawless logic at run-time. No servers, no templates, no compromises.
Ditch slow AI chat streams and iterate faster, unleash creativity and focus on delivering real impact.
SideSpin is a distributed compiler, code runner, and data renderer that turns declared human intent into platform-independent trusted visual output in real time — across iOS, Android, web, and displays. No servers, no templates, no ML hallucinations. Same input, same output, every time, down to the frame.
Three patents pending anchor the platform: a declarative animation engine that renders deterministically across GPU, WebGL, CSS, and CPU; a temporal interactive runtime that separates control from content so AI can safely prefetch future frames; and a distributed code-data fusion compiler that turns streaming LLM tokens into secure native executables on the client device.
📝 Declare intent, not code.
Describe the outcome in plain language — text or voice. A stateless, declarative transformation engine expands intent into a versioned, platform-agnostic specification. No imperative code, no templates, no ML guessing. Patent pending: stateless transformation of human intent.
🌍 One spec, every runtime.
Animation primitives are stored once in a versioned registry and referenced declaratively. At runtime, the same deterministic model is optimized against live device context and executed on the optimal runtime — GPU, WebGL, CSS, or CPU — with identical visual results across all of them. Patent pending: declarative animation engine.
⏱️ Content arrives in human time.
Control logic is strictly separated from presentation content. AI aggressively prefetches future frames into constrained, presentation-only buffers; progression advances only on declarative conditions. Wall-clock time becomes an implementation detail — frames are prepared early, revealed deliberately. Auto-heals when context shifts. Patent pending: temporal interactive runtime.
🔒 Auditable to the frame.
Every change — user action, device event, AI decision, UI update — is recorded on a single ordered timeline. Every rendered state is cryptographically hashable and retrievable. You can answer “what exactly was this person seeing?” down to the frame, not approximately. Supports HIPAA, SOC 2, GDPR, ISO 27001 workflows.
⚡ Code as compression.
Streaming LLM token outputs are fused into secure, hardware-optimized native executables in real time on the client — not a trusted server. Multiple untrusted LLM streams converge on-device with cryptographic verification at the edge. Send the code that generates the data plus tiny parameters instead of megabytes of tokens. Patent pending: live code-data fusion.
🤝 One verifiable timeline.
Humans, AI, and devices synchronize on a single, ordered, replayable timeline. Zero-latency branch switching between forward-looking states; every compilation step is hash-gated so any running state is restorable, auditable, and replayable frame-by-frame.
Intellectual Property
Patents pending
Patent pending (US & CA): Declarative Animation Engine — Deterministic, Context-Aware Rendering Across Platforms
A system that stores device-agnostic animation modules in a version-controlled registry, forms a deterministic model from a declarative specification, and optimizes rendering at runtime based on live contextual data from the target display device — selecting the appropriate runtime platform (GPU, WebGL, CSS, CPU) for each device.
- Module registry + declarative spec: Animation primitives stored once, referenced descriptively, parametrized at model-formation time — no imperative code, no templates, no ML.
- Context-driven optimization & runtime selection: The same deterministic model is optimized at runtime using live device context, then executed on the optimal runtime for that device.
- Cross-platform deterministic equivalence: Identical visual results across GPU, WebGL, CSS, and CPU targets — behavior pinned to the model version, not the platform.
- Verifiable, reconstructible execution: Natural audit trail from spec → model → context → optimization → runtime → execution enables reproducibility and compliance verification.
Patent pending (US): Temporal Interactive Runtime — Control-Content Separation with Async AI Preparation
A live, ongoing state handler that strictly separates control logic from presentation content — advancing experiences only on declarative conditions, while AI aggressively prefetches future frames and states into constrained, presentation-only buffers. Not a finite state machine: a continuous loop that runs across every device, auto-heals when context shifts, and produces a cryptographic audit trail of every frame and decision.
- Control-content split: Progression locked to explicit rules; prepared payloads cannot hijack logic.
- Declarative gating: Advances on render ticks via conditions — not async completion timing.
- Aggressive safe prefetch: AI pre-renders ahead without risking early display or correctness.
- Continuous auto-healing: When accessibility settings change, devices throttle, or networks hiccup, the runtime renegotiates instantly without divergence.
- Replay & audit: Append-only logs with presentation-sequence indexing enable exact reconstruction.
Patent pending (US): Live Code-Data Fusion — Distributed LLM Compilation stability on cross-platform client hardware.
A distributed compilation system that transforms streaming LLM token outputs into secure, hardware-optimized native executables in real time on the client device — fusing code and data generated from untrusted, diverse LLM sources into a single continuous compilation that runs stably while the code and data change underneath it.
- Distributed generation, client-side convergence: Multiple untrusted LLM streams fuse on the device, not a trusted server — cryptographic verification at the edge.
- Temporal-state compilation for possible futures: Emits branched, forward-looking artifacts for the next likely states; runtime switches paths with zero-latency branch switching.
- Predictive compilation via parallel runtime model: Dedicated ML model learns the compiler's runtime and environment, feeding predictions back to improve compilation over the device's life.
- Cryptographic per-view recall: Every compilation step hash-gated — any running state restorable, auditable, replayable frame-by-frame.
- Code as compression: Send the code that generates the data + tiny parameters instead of megabytes of tokens — reconstructs massive schemas locally at GPU speed.
How it works:
From intent to manifestation — describe the outcome in plain language and SideSpin’s deterministic engine makes it real, instantly and everywhere.
Unlike machine-learning systems that rely on probabilistic guesses or hidden templates, SideSpin produces the same result every time. This predictability ensures consistent experiences across devices and moments in time—supporting compliance in regulated industries like healthcare, finance, and privacy-sensitive environments.
📝 Speak
Express your intent in natural language—text or voice. The engine parses your request into goals, logic, and behaviors.🔧 Assemble
A registry pipeline connects UI components, actions, and services into a deterministic, platform-agnostic specification.📱 Render
Native clients—iOS, Android, web, desktop, or displays—bring the specification to life, adapting to context like locale, accessibility, or performance.🔗 Sync
Multiple users can share the same session in real time, with synchronized states and collaborative flows.📊 Observe
Structured interaction data is logged as users progress, supporting testing, auditing, and compliance requirements.♻️ Refine
Adjust instructions instantly, roll back at any time, and iterate without redeployment—ensuring continuous improvement and trust.The platform provides clear auditability, creating a verifiable record of how each experience is generated. This supports oversight, accountability, and compliance reporting required by frameworks such as HIPAA, SOC 2, GDPR, ISO 27001, and more.