Grep AI (formerly Parcha) provides a platform for automating research-intensive, high-stakes knowledge work using AI agents. The system combines 250+ skills, 100 trusted data integrations, and deep research capabilities into a custom agent harness that selects the optimal frontier or open-source model for each step. It is engineered for reliability, security, and cost efficiency, with 99.7% accuracy and isolated data access.
Funding
$6.8M 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.
CCCFC+4Founders
Product
Problem
High-stakes knowledge work—such as compliance, due diligence, and deep research—is slow, labor-intensive, and prone to human error. Organizations in regulated industries struggle to scale these workflows without compromising accuracy, data security, or budget.
Solution
Grep AI provides a platform that turns complex, research-intensive workflows into custom AI agents. The system combines over 250 built-in skills and 100 trusted data integrations into a custom agent harness that automatically selects the best frontier or open-source model for each step of a task. Agents are engineered for reliability, with 99.7% accuracy, and security, with isolated data proxies that ensure credentials are never exposed. The platform also optimizes economics by routing each step to the most cost-effective model, delivering up to 5× more output per dollar.
Target Audience
Primary customers are global financial institutions, including banks and fintechs, that need to automate compliance workflows and other high-stakes knowledge work where accuracy, security, and regulatory compliance are critical.
Features
- 250+ built-in skills covering research, due diligence, and compliance tasks
- 100+ trusted data integrations for accessing vetted information sources
- Custom agent harness that dynamically selects the optimal frontier or open-source model per step
- 99.7% accuracy rate engineered for high-stakes, regulated work
- Isolated data proxy architecture that prevents agents from seeing user credentials
- Full call scoping, logging, and revocation for complete data control
- No training on customer data, ensuring data sovereignty
- Model routing that maximizes output per dollar, delivering 5× more output per $ compared to single-model approaches