Dave Rossi ???
Founder
Pansynapse
Pansynapse offers CosmicMind, a cognitive intelligence platform that expands any large language model’s context window to 20 M+ tokens, enabling a single inference pass over massive text streams. By handling retrieval, indexing, and inference internally, it cuts inference costs by up to 2,500× while maintaining over 99% accuracy, and provides full observability of cognitive pathways for enterprise AI applications.
- Artificial Intelligence
- Developer Tools
- Enterprise Software
Updated 5 months ago
Funding
Funding rounds are not available yet.
Founders
Product
Problem
Current large language models are limited by small context windows (typically 1 M tokens) and high costs for processing long inputs, requiring complex chunking, retrieval, or summarization pipelines that degrade accuracy and increase expense.
Solution
CosmicMind is a cognitive intelligence platform that extends any AI model’s effective context window to 20 M tokens or more, enabling a single “cognitive pass” over massive text streams. By handling retrieval, indexing, and inference internally, it eliminates the need for external chunking or summarization layers while preserving model accuracy. The platform achieves up to 2,500× cost reduction compared to standard API pricing, delivering near‑zero cost when paired with local open‑source models. It also provides full observability into retrieval paths and reasoning steps, allowing developers to monitor and debug cognitive workflows. The result is a scalable, high‑accuracy AI system that can ingest and reason over extremely large documents in real time.
Target Audience
Primary customers are enterprise AI teams, SaaS providers, and developers building applications that require processing of very large text corpora, such as legal, research, or knowledge‑base platforms.
Features
- Extends any LLM’s context window to 20 M+ tokens in a single inference pass
- Reduces inference cost by up to 99.9% versus commercial APIs (e.g., GPT‑5.4)
- Maintains >99% accuracy across 5 M–20 M token inputs, with 100% accuracy on benchmarked test points
- Provides built‑in retrieval, indexing, and cognitive pathway observability without external tooling
- Supports zero‑cost operation on local open‑source models
- Delivers sub‑300 ms retrieval latency at scale