Corethink provides a neuro‑symbolic reasoning layer that augments any large language model with stepwise symbolic processing, delivering up to 60% higher accuracy while cutting token usage by 40–70%. The engine generates human‑readable decision traces for explainability and integrates directly with IDEs and enterprise AI platforms, reducing inference latency and GPU costs.
Funding
$1.3M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Developers and enterprises using large language models (LLMs) often encounter hallucinations, unstable planning, and high GPU costs due to inefficient token usage, while also needing explainable reasoning for critical applications.
Solution
Corethink offers a neuro‑symbolic reasoning layer that sits on top of any LLM to improve accuracy, reduce token consumption, and provide human‑readable decision traces. The engine replaces pure token‑based guessing with symbolic reasoning steps, delivering up to a 60 % boost in task performance across domains such as web development, backend coding, and text‑to‑SQL generation. By cutting token usage by 40–70 %, it lowers inference latency and GPU expenses, making large‑scale deployments more cost‑effective. Explainability is built in: each inference produces a clear, step‑by‑step trace that can be inspected by developers or compliance teams. The solution integrates directly with popular IDEs (e.g., Cursor) and enterprise platforms, enabling rapid adoption and scalable, low‑latency inference.
Target Audience
Primary customers are software development teams, AI product engineers, and enterprise AI deployments that require reliable, cost‑effective LLM performance and audit‑ready explanations.
Features
- Neuro‑symbolic reasoning engine that augments any LLM with symbolic stepwise processing
- Token‑efficiency module that reduces token consumption by 40–70 % per request
- Up to 60 % accuracy improvement on high‑value tasks such as code generation and text‑to‑SQL
- Human‑readable reasoning traces for full explainability of model decisions
- Plug‑and‑play integration with leading coding IDEs and enterprise AI platforms
- Linear scalability of accuracy gains as model size or workload increases