Why We Built a Sub-35ms Decision Engine
Generative LLMs are brilliant at writing essays and synthesizing knowledge. But using a 70B parameter model to decide if a support ticket is urgent or whether an input contains a prompt exploit is architectural madness.
1. The Problem with “LLM Everything”
Between 2023 and 2026, standard developer workflows began routing simple, structured decisions through cloud LLMs like GPT-4 or Claude. An email comes into customer support, and the backend sends it to an LLM just to get back a single word: "billing".
The costs of this pattern are severe:
- 2,000ms – 3,500ms latency: Autoregressive token generation buffers sequential outputs, leaving end-users staring at spinners.
- Financial Waste: Paying $0.02 – $0.05 per decision for binary categorization at scale accumulates tens of thousands in monthly cloud bills.
- Hallucinations & Injection Vulnerabilities: Generative models can be tricked by user text (“Ignore previous instructions...”), causing authorization bypasses or erratic outputs.
2. System 1 vs. System 2: Kahneman Applied to Microservices
In his landmark cognitive psychology work Thinking, Fast and Slow, Daniel Kahneman divides human cognition into two distinct modes:
Instantly recognizing a face, dodging an obstacle, or sensing urgency in a customer voice. Executes in a single mathematical pass in under 35 milliseconds.
Solving 17 × 24, writing a legal brief, or debating policy. Powerful for complex reasoning, but far too slow and expensive for high-frequency routing.
Laya AI is engineered strictly as a System 1 engine. It does not generate creative text or invent words. It reads incoming text across 100+ languages in a single tensor pass in RAM and returns deterministic, calibrated category probabilities, urgency scores, and boolean flags.
3. Technical Architecture: The mmBERT 322M Core
Laya is powered by the open-source convaiinnovations/laya foundation model — a 322-million parameter multilingual bidirectional encoder (mmBERT).
Because it is non-generative, there is zero temperature drift. The same input produces the exact same classification scores every single time, making it safe for compliance-critical pipelines.
4. Zero-Retention Privacy Guarantee
Your customer emails, internal documents, and security prompts should never become another company's AI training corpus.
Laya enforces a strict Zero-Retention Policy. Payloads are loaded into volatile RAM, evaluated by the tensor pipeline, transformed into JSON responses, and immediately dereferenced for garbage collection. No customer payload is ever written to disk or retained.
5. About the Creator & Open Attribution
Harshad Jadav
Cloud Architect, AI Systems Engineer & Open Source Contributor
I build deterministic infrastructure for production AI systems. Laya was born out of frustration with paying exorbitant cloud GPU bills for simple ticket routing and prompt defense checks.
Model lineage: convaiinnovations/laya on Hugging Face.
Visual mark (bfc-mark.png) property of Brain Function Collapse.
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