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Pacha

A new kind of API for your AI

Real-time data access for next-gen applications
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Data sprawl

You need data from multiple places

Your data is not in one place and might never be. Your AI will have to consume these silo-ed data and business logic as one-off tools.

Instead, if this fragmented reality was presented to your AI application as a unified query tool, performance would go up 10x.

PromptQL instantly gives you a natural language API which writes and executes a Python and SQL-like query on top of structured, unstructured and API data sources to find and retrieve the most relevant data.

Before
After
Issue $50 in credits to all my daily active users who have raised a support ticket in the last 1 week.
Authorization

You need an authorization model

Current authorization approaches are untenable to securely make your data available to AI applications, especially with fine-tuning, RAG or traditional text-to-SQL.

PromptQL with Hasura DDN provides a granular model level security mechanism on the semantic object model that sits outside of the source.

Semantic Object Model

Authorization policies are created and managed at this model layer. With attributed based policies to control access at a row, column and method level.

Pacha
Prioritize the Github issues assigned to me.
Flexibility

You need flexible planning and orchestration

AI changes the way we interact with our systems. We expect the flexibility of natural language upfront.

The PromptQL API has AI that can create plans to reliably and consistently access & operate on data, interleaved with LLM generation based on 3 key ideas:

Restricted to use a standardized SQL-like query language

This creates a 10x improvement in the “reasoning ability” required for autonomous planning because query language semantics are already embedded.

Self-healing python runtime to create multi-step orchestration plans

This enables LLMs to have an “analytical brain” to perform complicated data tasks, call downstream services, auto-correct itself, and perform bulk operations/iterations on data way beyond context windows.

PromptQL creates, saves and retrieves context in structured memory artifacts

This creates a 10x improvement in the “correctness” of plan execution, by eliminating context loss between multiple steps and scope for hallucinations.

Pacha
Anti-fragile

You need to reduce fragility with every failure

Each “failure” of your AI application to do the right thing, should result in an action that makes the end to end system better - from user to data.

Current approaches have too many variables, and improving the system increases the overall fragility.

PromptQL provides a query plan for every run, that helps you focus on 2 key aspects to drive improvement:

Improve the prompt given to PromptQL

PromptQL will not always be able to create the right query plans, but just like any LLM, it can easily be nudged and improved in the right direction.

Improve the data connected to Hasura DDN

PromptQL is able to “reason” about creating plans by leveraging the semantic documentation and the underlying data modeling (move between structured & unstructured data, use business methods for specific intents) that you do.

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Connectors

Talk to all your data, from everywhere

Leverage our ever-growing ecosystem of open source native data connectors, and get a PromptQL API on top of all your API sources, structured and unstructured data using PromptQL.

TimescaleMySQLYugabyteQdrantSingle StoreSQL ServerCitusDuckDBNeonDBMongoDB
Sendgrid
AzureSnowflakePostgresCockroachOracleClickhouseGraphQL
Go
Salesforce
Rippling
GitHub
Zendesk
TimescaleMySQLYugabyteQdrantSingle StoreSQL ServerCitusDuckDBNeonDBMongoDB
Sendgrid
AzureSnowflakePostgresCockroachOracleClickhouseGraphQL
Go
Salesforce
Rippling
GitHub
Zendesk
TimescaleMySQLYugabyteQdrantSingle StoreSQL ServerCitusDuckDBNeonDBMongoDB
Sendgrid
AzureSnowflakePostgresCockroachOracleClickhouseGraphQL
Go
Salesforce
Rippling
GitHub
Zendesk

Get started building AI applications

Our team can work with you to get PromptQL live, tailored to your specific use cases. Please provide a detailed description of your use case, and we will reach out within 24 hours if it aligns with PromptQL’s capabilities.