postgrad.io

What It Is

PostGrad positions itself as a “data/knowledge hub for AI agents.” Based on the crawled page content, it is not a general-purpose chatbot or foundation model. Instead, it extracts real business experience from meeting notes into structured knowledge, then makes it available to agents via a REST API and MCP Server. Each knowledge item includes a title, category, content, tags, confidence score, reinforcement count, and last updated time. The goal is to give agents fresher business context rather than relying on static documents, crawlers, or fine-tuning.

Core Capabilities and Integrations

Its core capabilities center on knowledge Feed subscriptions and retrieval. Developers can browse and subscribe to Feeds by category or industry, then query knowledge entries through the API. Search capabilities vary by plan: Starter supports keyword search, Pro adds semantic search, and Scale supports keyword, semantic, and hybrid search. The API design is fairly engineering-oriented, supporting specified Feeds, automatic selection of the richest Feed, merged search across all subscribed Feeds, and filtering by category. For integration, it offers a REST API, MCP, Webhooks, Python/TypeScript SDKs, and endpoints such as feeds, knowledge, search, usage, categories, and stats, making it suitable for embedding into Agent workflows.

Pricing and Trial

Pricing is transparent and relatively accessible: Starter is $5/month with 1,000 queries/month; Pro is $19/month with 10,000 queries/month and priority support; Scale is $49/month with 50,000 queries/month and dedicated support. All tiers can access subscribed Feeds and categories. The main differences are request rate limits, monthly query volume, and search modes. The page does not mention any free allowance or free trial.

Pros and Cons

Its strengths are a clear positioning, with a specific focus on solving the lack of external domain knowledge for Agents; support for both API and MCP, which lowers integration costs; and structured knowledge entries with confidence scores and reinforcement counts, making it easier for programs to assess quality. The drawbacks are also clear: the website does not specify the full coverage of its “10+ domains,” its knowledge-source review mechanism, authorization and privacy compliance terms, or Chinese-language support. Output quality also depends heavily on its meeting-extraction and content-supply system, but the page does not provide accuracy figures or evaluation results.

Who It’s For and Access from China

PostGrad is suitable for AI Agent developers, bot product teams, SaaS tool developers, and teams that need to call business knowledge in scenarios such as due diligence, deal evaluation, sales workflows, pricing strategy, growth, and hiring. The page does not state its accessibility from China, and both network connectivity and payment options are unknown. If access or payment is restricted, alternatives include building an in-house RAG knowledge base, using a vector database with internal documents or licensed data pipelines, or looking for MCP/API knowledge-base services that support Chinese and local payment methods.