Induction provides an API‑compatible middleware that sits between applications and large language models to automatically extend context windows using summarization and caching, without requiring code changes.
Funding
Funding not disclosed
Founders
Product
Problem
AI models with limited context windows require frequent prompt truncation or chunking, leading to higher latency, increased token usage, and higher inference costs. Developers must modify code or manage complex pipelines to extend context, which adds engineering overhead.
Solution
Induction offers a drop‑in, API‑compatible service that sits between an application and an AI model, automatically managing and extending the model’s context window. The platform transparently streams and caches relevant prior interactions, enabling longer effective contexts without changing existing code. By optimizing token usage and reducing redundant processing, it speeds up response times and lowers per‑inference costs. Integration is achieved via standard API calls, so existing workloads can benefit immediately from improved efficiency and reduced operational expenses.
Target Audience
Primary customers are developers and engineering teams building applications that rely on large language models, such as SaaS platforms, conversational agents, and enterprise AI solutions.
Features
- API‑compatible middleware that intercepts and augments model calls without code changes
- Automatic context window extension using intelligent summarization and caching
- Real‑time token optimization to minimize prompt length while preserving information
- Latency reduction through selective retrieval of relevant prior interactions
- Cost‑saving inference engine that reduces token consumption and compute load
- Monitoring dashboard providing metrics on context usage, latency, and cost savings