Cross-model work plane
Chat is a conversation. The work sits outside it.
You ask a mouth. The mouth compiles Search. Hits are source addresses — or an honest miss.
The human never queries the silo. There is no search box here.
Honest demos
An agent and a user, reaching a result.
Public silos show retrieve. They are not the whole product, and they are not a coverage promise.
The mouth gathers intent, compiles a retrieval job, and returns inspectable addresses.
Papers
Clarify, then the titles change
User
Find useful graph neural network research.
Mouth
What application, and do you want paper candidates or claims about the best model?
User
Network intrusion detection. Paper candidates first, from the 2021-onward slice.
Mouth
Five candidate titles, each a source address. Generic GNN papers were a fair first cut; they were not this job.
Title-level candidates, not recall. Concept pin: Graph neural network (wikipedia:Graph_neural_network) is an identity address, not an explanation of the papers.
Filing
Company to 10-K to the risk opening
User
Help me research Apple before a sales conversation.
Mouth
Which filing, and do you need source entry points or supported risk conclusions?
User
The available 2025 10-K. Company context, then the risk section.
Mouth
Source path, not three operational risks. The opening window is not the full section.
- Apple Inc.
wikipedia:Apple_Inc. — title identity
- Apple 10-K
edgar:0000320193-25-000079 — this available filing
- Item 1A opening: “Item 1A. Risk Factors The following summarizes factors…” — extract window, not a landed section
Source locator. Not bounded Item GO. Not three supported risks from this window.
Sidecar
NVIDIA ships after the opt-in
User
Help me research NVIDIA.
Mouth
Leftover Wikipedia has no Nvidia title. That miss is real. It is not a related page.
User
Opt this project in. Fill the sidecar.
Mouth
Identity ships on the sidecar this project opted into. Leftover warehouse is unchanged. Filing extract windows also return.
- Leftover identity:
not_in_silo
- Nvidia — sidecar pin
project:path-reset:wikimedia:Nvidia
- NVIDIA 10-K Item 1 / 1A — extract windows, not landed sections
The sidecar is this project’s opt-in, not a leftover hose. Serving-topic papers still do not retrieve. Not bounded Item GO.
82d
One semantic layer for memory, retrieval, and routing.
Most AI stacks generate well but retain poorly. Context gets trapped in prompts. Embeddings get tied to one vendor.
Retrieval becomes brittle the moment the stack changes. 82d solves that by giving you a shared layer where memory stays portable,
retrieval stays reliable, and routing stays consistent as the system evolves.
Portable memory
Keep what the system already knows
Preserve semantic value across provider changes instead of rebuilding the memory layer after every shift.
Cross-model retrieval
Search without starting over
Use one shared semantic layer instead of trapping knowledge inside one provider-specific index.
Semantic routing
Use the same layer to decide what happens next
Route tools, tasks, and workflows from the same system that powers memory and retrieval.
Firehose
The corpus layer that keeps knowledge live.
Firehose is how 82d connects your system to what it needs to know. It turns internal knowledge, research collections, and large public sources into
continuously queryable infrastructure for assistants, agents, research workflows, and search-heavy products.
Private knowledge
Turn internal corpus into governed retrieval
Make documents, notes, transcripts, and archives searchable inside a system built for reuse and control.
Public sources
Work with more than one corpus
Bring external datasets into the same retrieval layer instead of splitting your stack across disconnected tools.
Operational retrieval
Support real product flows, not just storage
Put corpus retrieval to work inside assistants, agents, research flows, and search-driven user experiences.
Why Slarty AI
Because knowledge is where AI systems break first.
The hard problem is not producing one good answer. It is keeping useful context alive as models change, knowledge grows, and workflows become more complex.
We built 82d and Firehose for that layer.
Reduce the interoperability tax
Stop paying to rebuild memory and retrieval every time the model landscape shifts.
Make retrieval portable
Keep the retrieval layer useful as providers, embeddings, and workflows evolve.
Let knowledge compound
Carry forward what the system already knows instead of relearning the same ground.
Keep control of the layer beneath the model
Build on infrastructure you can shape, govern, and keep over the long term.
Built for
Built for teams turning AI into durable infrastructure.
AI product teams
For copilots, agents, and search-driven experiences that cannot afford brittle memory or fragile retrieval.
- Assistant and agent products
- Retrieval-heavy user experiences
- Multi-model product stacks
Enterprises with private knowledge
For organizations that need governed access to internal corpora without surrendering the memory layer to one provider.
- Private corpora
- Governed retrieval
- Provider-flexible architecture
Research and data teams
For teams whose advantage depends on keeping large knowledge collections live, searchable, and reusable.
- Research collections
- Cross-silo search
- Persistent semantic context