Parsed builds custom, interpretable large language models (LLMs) for specific enterprise workflows. Our platform offers superior performance and reduced costs through continual learning and domain-specific adaptation, enabling businesses to develop proprietary AI capabilities.
Funding
$3.7M 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
General-purpose large language models (LLMs) often underperform on specialized enterprise tasks due to their broad training and lack of domain-specific adaptation. This leads to suboptimal accuracy, increased inference costs, and a failure to continuously improve performance over time, hindering the development of truly effective AI solutions for specific workflows.
Solution
Parsed develops custom, interpretable LLMs tailored to specific enterprise workflows, offering superior performance, reduced operational costs, and accelerated adaptation compared to off-the-shelf models. Our platform focuses on continual learning, enabling models to improve with every interaction and adapt to evolving business needs. We ensure transparency and reliability through enterprise-grade inference and a commitment to ongoing intelligence gains, allowing businesses to build proprietary AI capabilities.
Target Audience
Businesses across regulated industries such as healthcare, insurance, and legaltech, as well as enterprises seeking to optimize specific operational workflows with high-performance, transparent, and continuously improving AI solutions.
Features
- **Domain-Specific LLM Training:** Custom LLMs are trained on proprietary data and aligned precisely with specific business workflows, achieving over 50% cost reduction and 2-3x speed improvements compared to generalist models.
- **Continual Learning & Adaptation:** Models learn from every input and automatically improve over time without traditional development cycles, generating compounding intelligence gains.
- **Mechanistic Interpretability:** Built-in interpretability techniques, including attention-based attribution and domain-specific Sparse Autoencoders (SAEs), provide granular insights into model behavior and decision-making processes.
- **Enterprise-Grade Inference:** Multicloud inference infrastructure ensures 99.99% uptime and supports secure, scalable API calls for mission-critical applications.
- **Parameter-Efficient Fine-Tuning (PEFT):** Utilizes techniques like LoRA for efficient model adaptation, enabling significant parameter savings and faster iteration cycles while maintaining high performance.
- **Evaluation-Driven Development:** Employs rigorous, domain-aligned evaluation frameworks to guide model training and deployment, ensuring performance metrics directly reflect business objectives.
- **Compliance and Security:** Adheres to enterprise security standards including SOC 2, ISO 27001 certification, HIPAA alignment, and GDPR compliance.