For example, we know that the pain of building new API integrations scales with the level of fragmentation and number of competing "standards". In the current meta, we see this pain with a lot of AI startups who invariably need to connect to their customers data, but have to support 50+ integrations before they even scale to 50+ customers.

This is the process for an AI startup to add a new integration for a customer:

- Pore over the API docs for each source application and write a connector for each

- Play email tag to find the right stakeholders and get them to share sensitive API keys, or give them an OAuth app. It can take 6+ weeks for some platforms to review new OAuth apps

- Normalize data that arrives in a different formats from each source (HTML, XML, text dumps, 3 different flavors of markdown, JSON, etc)

- Figure out what data should be vectorized, what should be stored as SQL, and what should be discarded

- Detect when data has been updated and synchronize it

- Monitor when pipelines break so data doesn’t go stale

This is a LOT of work for something that doesn’t move the needle on product quality.

That’s why we built Psychic.dev to be the fastest and most secure way for startups to connect to their customer’s data. You integrate once with our universal APIs and get N integrations with CRMs, knowledge bases, ticketing systems and more with no incremental engineering effort.

We abstract away the quirks of each data source into Document and Conversation data models, and try to find a good balance to allow for deep integrations while maintaining broad utility. Since it’s open source, we encourage founders to fork and extend our data models to fit their needs as they evolve, even if it means migrating off our paid version.

To see an example in action, check out our demo repo here: https://github.com/psychic-api/psychic-langchain-tutorial/

We are also open source and open to contributions, learn more at docs.psychic.dev or by emailing us at [email protected]!