Aurora Mobile Limited, a leading global provider of customer engagement and marketing technology services, today announced that its enterprise-grade AI agent platform, GPTBots.ai, has integrated Jev — the "System One" decision model from TypeSafe AI — to build what the team calls a "two-layer AI architecture": one layer that thinks, and one layer that judges.
The integration introduces a two-layer AI architecture inside GPTBots.ai: one layer that handles reasoning, and one layer that handles decisions — each optimized for what it does best. The move comes just one week after TypeSafe AI released Jev on September 15, a purpose-built decision model that has since been integrated by Vercel, Cloudflare, LangChain, and other major developer platforms.
Every AI agent workflow is full of decisions that don't require language generation. Is this a billing question or a technical one? Does this retrieved document actually answer the user's question? Which model should handle this task? These are binary or categorical judgments — yes/no, this/that, relevant/irrelevant — yet most platforms run them through full-scale LLMs that generate paragraphs of text just to arrive at a single classification.
The result: enterprises pay for words they don't need, wait for tokens that could have been a millisecond decision, and get no reliable measure of how confident the model actually is.
Jev was built for this exact gap. It does not generate text. It takes unstructured state as input and returns structured, probabilistic decisions — Choice, Score, or Yes/No — in a single parallel pass, with calibrated confidence scores attached to every answer. According to TypeSafe's published benchmarks: Speed 70–500ms end-to-end, Cost $0.042 per million input tokens, Reliability 0% structured output error rate, Adoption by Vercel, Cloudflare, LangChain, and Langfuse within days of launch.
GPTBots.ai is not adding Jev as an afterthought. The platform already operated several decision-layer mechanisms. The Jev integration deepens and unifies these capabilities under a single, purpose-built model.
The architecture divides work by nature. The Decision Layer, powered by Jev, handles fast, high-volume judgments — routing, filtering, classification, relevance scoring — in under 500ms at a fraction of the cost of an LLM call. The Reasoning Layer handles complex reasoning, text generation, and open-ended dialogue using general-purpose LLMs such as GPT and Claude. Each task runs on the engine best suited for it.
Three existing GPTBots.ai capabilities now powered by Jev: Model Auto-Router — Picking the right brain before the task starts, Dynamic Top-K — Filtering noise out of knowledge retrieval by scoring each chunk's semantic relevance as calibrated probability, Intent Classification in FlowAgent and Workflow — Routing to the right business branch with confidence scores that determine automatic execution or escalation.
"We already had the building blocks — model routing, dynamic retrieval, intent classification," said Chris Lo, Founder and CEO of GPTBots.ai. "What Jev gives us is a dedicated decision engine that handles these judgments at a speed and cost point that changes the economics of the entire pipeline. Instead of paying for a full LLM call on every routing decision, we now pay a fraction of a cent per judgment — and get calibrated confidence scores to boot."
The two-layer architecture introduces a configurable confidence threshold system. Every Jev decision comes with a probability score. Enterprises can set their own thresholds per workflow — deciding which decisions execute automatically, which require review, and which escalate to human agents. A billing classification might auto-execute at high confidence; a compliance decision might require near-certainty.
What This Means for Enterprise Customers: Cost reduction with decision tasks costing fractions of a cent, Lower latency with sub-500ms responses, Calibrated confidence replacing subjective certainty, Zero type errors with schema-guaranteed outputs.
About GPTBots.ai
GPTBots.ai is an enterprise-grade AI agent platform by Aurora Mobile, offering no-code/low-code AI agent development, deployment, and management for enterprise clients. Through multi-model LLM integration and intelligent workflow automation, GPTBots.ai empowers organizations to enhance customer service, optimize knowledge management, and automate business processes. Global customers include Axios Management, GP Batteries, Ruko and many more.
About Aurora Mobile
Founded in 2011, Aurora Mobile is a leading provider of customer engagement and marketing technology services. The Company is dedicated to empowering global enterprises with stable, efficient, and intelligent customer interaction solutions. Through its flagship brand EngageLab and its robust AI infrastructure GPTBots.ai, the Company helps businesses achieve seamless customer reach and accelerate digital transformation worldwide.
