Olive provides an AI platform that processes speech by preserving interactional information like timing, emphasis, and dialect features. This linguistically structured output mitigates covert bias often introduced when speech is prematurely normalized to text. The platform offers developers a cost-effective solution for integrating more nuanced and fair voice understanding into existing systems.
Funding
Funding not disclosed
Founders
Product
Problem
Large Language Models (LLMs) often struggle with natural dialogue, lacking the intuition to fully grasp user intent and context, leading to static and unengaging interactions. This necessitates extensive manual prompt engineering, consuming significant development resources without consistently achieving human-like conversational quality.
Solution
Olive provides a dynamic prompt engineering layer that enhances LLM applications by injecting real-time contextual hints. This approach tunes LLM responses based on user tone and conversational nuances, drastically reducing the development overhead associated with static prompt optimization. The platform supports both text and audio modalities, enabling more intuitive and adaptive AI-driven conversations. Olive's proprietary technology analyzes linguistic features such as prosody and sociolect to inform prompt adjustments, creating a more empathetic and contextually aware AI.
Target Audience
The primary customers are developers and product teams building LLM-powered applications who require enhanced conversational capabilities and reduced prompt engineering effort.
Features
- Dynamic prompt injection for real-time LLM response tuning based on conversational context.
- Support for both text and audio input/output modalities via a unified API.
- Analysis of prosodic cues (intonation, stress, pitch) to interpret emotional states and emphasis.
- Sociolect recognition capabilities to adapt to diverse linguistic backgrounds and speech patterns.
- Patent-pending approach trained on non-Internet data to foster deeper conversational understanding.
- Integration capabilities with existing applications through a single API.
- Contextual hint generation to improve LLM performance and reduce edge case handling.