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App Corp
Full-service software engineering
Engineering your experience…
App Corp
Full-service software engineering
Engineering your experience…
Browser-based AI inference — privacy-first, zero infrastructure
App Corp builds privacy-preserving AI experiences with Gemma 4 Edge and WebGPU. Run AI inference directly in the browser — no data leaves the device, no API costs, no vendor lock-in.
Gemma 4 Edge runs Google's Gemma models directly in the browser using WebGPU for GPU-accelerated inference. The architecture we use:
Capability detection checks for WebGPU support, estimates available memory, and determines which model variant to load. The fallback chain: WebGPU local inference → deterministic guided consultation → optional cloud inference.
Model loading uses the Transformers.js library to download and cache the model. First load requires network access; subsequent loads use the browser cache. Loading progress is displayed to the user.
Inference pipeline: user input → tokenization → model inference → structured output validation → deterministic rules engine → UI rendering. Free-form model output never directly controls application state.
Structured outputs enforce JSON schema validation on model responses. This prevents hallucination from breaking application logic and makes agent responses predictable.
All inference runs locally — no data transmission to cloud APIs. Trade-off: model capability is limited by device hardware (smaller models, shorter context windows).
Once the model is cached, inference is free. Trade-off: initial model download is 1-4GB depending on variant — significant for mobile users.
No network round-trip means faster responses for straightforward queries. Trade-off: complex reasoning is slower on device than cloud GPUs.
Strong marketing differentiator for privacy-conscious users. Trade-off: WebGPU support is not universal — fallback strategy is essential.
The primary cost is development time — integrating Gemma Edge with WebGPU requires specialized knowledge. Runtime costs are zero for local inference. For the fallback cloud inference path, costs follow standard LLM API pricing. The trade-off: higher development cost, zero operating cost. For high-volume applications, local inference can save thousands per month in API fees.
Gemma Edge and WebGPU are central to App Corp's AI Solution Architect — our flagship product. We built the browser-based AI experience that runs Gemma 4 directly in the browser, with a fallback chain to cloud inference when WebGPU is unavailable.
Our Gemma Edge experience includes WebGPU capability detection, model loading with progress indicators, structured output validation, and the deterministic rules engine that consumes model outputs. The key architectural decision: Gemma handles reasoning and classification, while TypeScript handles pricing, state management, and UI rendering. This separation ensures predictable behavior.
We have also built the fallback strategy: when WebGPU is unavailable or the device is too weak, the tool gracefully degrades to a deterministic guided consultation — still useful, still interactive, but without AI inference. This ensures the tool works for every visitor, not just those with modern GPUs.
Estimate architecture, cost, and ROI before you commit.
Deep-dives into architecture, cost, and implementation.
We have shipped production browser-based AI with Gemma Edge. Tell us about your use case and we will tell you whether local inference is the right approach — or whether cloud APIs make more sense.