Sineps provides lightweight NLP models that can replace general‑purpose large language models in AI applications, cutting costs and lowering latency. Their core products include an Intent Router that classifies query intent for routing and a Filter Extractor that pulls structured filters from queries, enabling faster, more reliable inference for developers.
Funding
Funding not disclosed
Founders
Product
Problem
Many AI applications rely on large language models that are expensive to run and introduce high latency, making them unsuitable for cost‑sensitive or real‑time use cases. Additionally, extracting specific intents or structured filters from user queries often requires custom prompting or post‑processing, which can be unreliable and slow.
Solution
Sineps offers lightweight natural‑language processing models designed to replace general‑purpose large language models in AI workflows, delivering lower inference costs and reduced response times. Its platform includes an Intent Router that automatically classifies incoming queries into predefined routes, enabling applications to direct requests efficiently. A Filter Extractor parses user input to produce structured filter values for specified fields, simplifying downstream processing. Both components run on high‑speed inference infrastructure, providing more predictable performance for real‑time services. The service is accessible through an open‑beta console, allowing developers to test and integrate the models without upfront commitments.
Target Audience
Primary users are developers and product teams building AI‑driven applications that require fast, cost‑effective language understanding, such as chatbots, search interfaces, and recommendation engines.
Features
- Lightweight NLP models optimized for low compute overhead and fast inference
- Intent Router that captures query intent and maps it to a designated route
- Filter Extractor that generates structured filter parameters from free‑form text for targeted fields
- Open‑beta console for immediate trial and API access
- Support for synthetic data generation and model fine‑tuning to adapt to specific domains