AI agents on private data perform poorly with real user queries
| Query | Assistant | Status |
|---|---|---|
| What are my Uber receipts from October? | Gemini | Failed |
| What products have I bought in the last 3 months? | Rufus | Failed |
| Extract any common identified pain points for my opportunities | Einstein | Failed |
| Go through the recent activities in Acme opp and summarize action items | Einstein | Failed |
The problem with canned search-based retrieval
Today’s AI assistants in closed domains rely on search-first algorithms, often missing data that doesn’t fit opaque/rigid criteria. The moment users go off script, these assistants falter.
“Find all receipts from October”, might not respond with anything useful because October was not vectorized.
PromptQL’s agentic approach to retrieval
With agentic query planning, PromptQL will retrieve data like a human - first gathering relevant emails from last week, then applying the right LLM to classify if there are follow-ups required, accurately. Just like you would.
PromptQL out-of-the-box on your GitHub data
Build with us: AI solutions on your data
Have a use case for PromptQL and Agentic Data Access? Interested in evaluating your existing AI agents / assistants against the ADAB? Let's connect!
We would love to collaborate with you to build your first agent - for free.
