TryTrust AI
TryTrust AI provides a unified inference platform that separates prefill and decode phases across different models to cut costs and latency. Its KV-cache transfer technology lets a small model read long contexts while a larger model generates tokens, and its DFlash parallel drafting predicts 16-token blocks in a single pass for over 2× speedups. The platform serves text, audio, vision, and action-policy models through a single API with token-based pricing.
- Artificial Intelligence
- Developer Tools
- Software Only
- AI Agents
Funding
Founders
Founder details are not available yet.
Product
Problem
Large language model inference is dominated by the cost of prefill—the phase where a model reads the input. Because prefill costs scale with the size of the model doing the reading, users pay large-model rates before the model produces a single word, making long-context requests disproportionately expensive. Additionally, decode speed is limited by serial next-token prediction, which constrains perceived latency and throughput.
Solution
trytrust.ai lowers inference cost and latency by separating the prefill and decode phases across different models. Its core technology transfers KV caches from a small "reader" model to a larger "decoder" model, allowing the small model to build the context while the large model generates tokens at its native speed. The transfer uses a learned ridge mapper and a rank-16 residual MLP to project the cache into the target model's attention space, gating the handoff on held-out quality metrics to ensure correctness. trytrust.ai also offers DFlash, a parallel drafting technique that predicts 16-token blocks in one forward pass, delivering exact token-matching speedups of over 2× without altering the target model's output. These technologies are served through a unified inference API platform.
Target Audience
Primary customers are AI engineering teams and enterprises seeking low-cost, high-throughput inference for production workloads, including RAG pipelines, real-time voice applications, and safety-classification tasks.
Features
- KV-cache transfer that maps a small model's prefill cache into a larger model's attention space, enabling fast and low-cost long-context handling with gated quality checks
- DFlash parallel drafting that predicts 16-token blocks in a single forward pass, achieving 2.154× geometric-mean speedup with exact token agreement against native decoding
- Handoff overhead of only 68-100 ms per request after warmup, with decode speeds of 133-150 tokens per second for transfer paths
- Model pairing system that falls back to ordinary prefill if a quality or latency gate is not cleared, ensuring reliability
- Audio-specific KV transfer for voice models, improving accuracy from 17.2% to 31.2% on a sealed spoken MMSU test at nearly the same decode speed
- Unified API across text, video, and action-policy inference, including safety classifiers and vision models
- Token-based pricing with no subscriptions, including discounted flex tier at 40% lower cost for delayed delivery