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Stale knowledge
The gap between what a model knows and what's actually true widens every day it isn't retrained. It happens silently, with no warning to the person relying on it.
Every AI tool your team uses was trained once, then frozen. Hive is the memory layer that keeps every connected agent current — pooling knowledge across tools, teams and people, and serving it back from any assistant.
https://mcp.midnighthive.io/mcphive_pullRecall stored knowledge before researching from scratchhive_pushSave a durable lesson once you've solved somethinghive_inferExtract a reusable insight from the conversation, unpromptedWorks with Claude · ChatGPT · Cursor · Codex · Gemini · any MCP client
Every model in production was trained at a point in time and then frozen. From that moment, four failures start compounding.
The gap between what a model knows and what's actually true widens every day it isn't retrained. It happens silently, with no warning to the person relying on it.
When an agent hits a dead end, it has no memory of having been there before. The same broken approach gets suggested again — to you tomorrow, and to everyone else indefinitely.
Every insight from a session evaporates when the context window closes. Your team's hard-won fixes leave no trace an agent can find later.
An outdated answer arrives with exactly the same certainty as a correct one. Without a signal you can read, you can't tell which one you just got.
Hive doesn't replace your AI tools. It corrects for what they structurally cannot do: stay current, remember what failed, preserve what worked, and know the limits of their own reliability. The store maintains itself — checking, re-ranking and retiring on a schedule — so you get today's answer rather than last year's.
One URL in your client's custom-connector field and an OAuth authorize. No SDK, no code changes, no config file to hand-edit.
Every validated discovery strengthens the store for every connected agent. A solo RAG setup has a corpus of one; opt into the Mega Hive and yours doesn't.
Results carry provenance and a relevance score, and a low-confidence pull is treated as a miss rather than dressed up as an answer.
| Capability | Hive | Traditional RAG | Static KB |
|---|---|---|---|
| Retrieval | Hybrid + RRF + re-rank | Vector similarity | Keyword |
| Stays current without you | Nightly engine | ✗ | ✗ |
| Memory across sessions and tools | ✓ | Per-app | Manual |
| Fills its own gaps from the live web | ✓ | ✗ | ✗ |
| Corpus beyond your own documents | Mega Hive (opt-in) | ✗ | ✗ |
| Connect any AI client | One MCP URL | SDK integration | ✗ |
| Provenance and confidence signals | ✓ | ✗ | Partial |
Hive scales from one person to an entire company — and you decide what stays private and what you share.
Hive works alongside the leading models from every major provider — and because it connects through an open standard, it stays compatible as new ones arrive.
Model-agnostic by design — Hive works with any MCP-compatible client, whatever runs behind it.
Hive is in invite-only private beta. Join the waitlist and we'll bring you in with the next batch — the first 1,000 on the list get a comped pilot plan when they land.
Prices in AUD, excluding GST. Enterprise tenancies are in development — talk to us about the pilot.
One URL, one authorize, and every AI tool you use starts pulling from — and adding to — a store that keeps itself current.