Carry useful context through model churn instead of rebuilding from scratch.
Semantic infrastructure for teams building serious AI systems
What your AI learns should outlast the model that learned it.
Slarty AI builds 82d and Firehose so memory, retrieval, and search do not reset when the model layer changes. We give teams a durable semantic layer for private knowledge, public corpora, and long-lived context, so what an AI system knows stays queryable, reusable, and operational across providers, tools, and time.
Move across providers without sacrificing the retrieval layer underneath the product.
Work across private knowledge, public data, and long-horizon context from one queryable layer.
Use the same semantic system for memory, search, and next-step decisions.
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