SAID
Deterministic inference engine. GPU-first model inference with constraint gates. Calculus-based model selection picks the optimal model per query. Generate-validate-retry gate engine mathematically guarantees valid output.
Overview
SAID treats inference as a deterministic operation. Rather than generating output and hoping it is correct, SAID generates, validates against formal constraints, and retries with targeted corrections until the output is mathematically guaranteed to be valid. The API is OpenAI-compatible — switch to deterministic inference without rewriting your stack.
GPU-First Inference
Purpose-built for GPU execution. SAID maximizes throughput and minimizes latency by treating GPU-first inference as the default path, not an optimization.
Constraint Gates
Every inference output passes through deterministic constraint gates before delivery. Invalid outputs are caught and corrected — not shipped and hoped for.
OpenAI-Compatible API
Drop-in replacement for existing OpenAI API integrations. Switch to deterministic inference without rewriting your application layer.
Key Capabilities
| Capability | Description |
|---|---|
| Calculus-Based Model Selection | Mathematical optimization picks the optimal model for each query based on cost, latency, capability, and constraint requirements |
| Generate-Validate-Retry | Gate engine that generates output, validates against constraints, and retries with targeted corrections until output is mathematically valid |
| GPU-First Architecture | Inference pipeline designed from the ground up for GPU execution — not a CPU pipeline with GPU bolted on |
| Constraint Gate Engine | Deterministic validation of every output against formal specifications before delivery to the caller |
| Multi-Model Orchestration | Route queries across multiple models with mathematical guarantees on selection optimality |
| Deterministic Output | Same query, same constraints, same model selection, same validated output — every time |
| OpenAI-Compatible API | Standard API interface compatible with existing OpenAI client libraries and integrations |
| Audit Trail | Complete record of model selection decisions, constraint evaluations, and retry cycles for every inference |
Use Cases
Regulated Industries
Deterministic inference guarantees for financial services, healthcare, and government where model output must be auditable. Every inference decision traces back to a formal constraint, satisfying regulatory requirements for explainability and reproducibility.
Multi-Model Orchestration
Calculus-based model selection across heterogeneous model portfolios — LLMs, specialized models, and custom fine-tunes. SAID picks the optimal model per query based on mathematical cost-quality tradeoffs, not static routing rules.
High-Throughput Pipelines
GPU-first execution with constraint gates for production inference at scale. The generate-validate-retry cycle guarantees valid output without sacrificing throughput, running at thousands of inferences per second.
Edge Deployment
Deterministic inference on resource-constrained environments where model selection matters most. SAID's calculus-based selection ensures the right model runs on the right hardware with mathematically bounded latency.
Deterministic Inference Research
SAID is built on active research into the mathematical foundations of constrained inference — how to guarantee output validity without sacrificing generation quality or throughput.
Current research explores optimal retry strategies that minimize generation cycles while maximizing constraint satisfaction, calculus-based model selection under multi-objective optimization with latency and cost constraints, and formal verification of gate engine termination guarantees under arbitrary constraint sets.
Explore ResearchResearch Areas
- ▸ Optimal constraint-guided retry strategies
- ▸ Multi-objective model selection optimization
- ▸ Gate engine termination guarantees
- ▸ GPU-native constraint evaluation pipelines
- ▸ Compositional constraint specification languages
Inference With Guarantees
Stop hoping your model outputs are valid. Start proving they are.