Production AI agent use cases
Generic vector search retrieves text that looks like the question. / ml delivers what is true now, the exact date it changed, and the verified source evidence behind each answer with 80% fewer tokens. Explore how engineering teams deploy / ml across production agent architectures.
Five everyday agent workflows
Pasting full files and chat logs into every prompt is slow, expensive, and confuses models when facts change. Here is how / ml solves memory for each team.
Productivity tools & workspace assistants
Connect Slack, Linear, Notion, and meeting notes into one shared team memory so your assistant always returns the latest decision and owner.
Customer support & success agents
Combine tickets, live chat, and Stripe plan changes into one timeline per customer so bots and human agents never ask repeat questions.
Coding agents & IDE assistants
Give Claude Code, Cursor, and Codex persistent memory for repo rules, past bug fixes, and architecture choices across sessions.
Knowledge bases & company brains
Keep internal wikis, handbooks, and runbooks up to date, filter out weak search matches, and link every answer to the live page.
Legal & compliance intelligence
Track contract amendments alongside base agreements, filter clauses by effective date, and cite the exact section and version.
Why standard RAG breaks down in production
Standard vector search only finds text that looks similar to your question. Real assistants need to know what is current, what changed, and where the fact came from.
Equip your agents with long-term memory
Start free with 10,000 ingests and 1,000 retrievals per month. Connect via native MCP in 60 seconds or integrate the Python SDK.