Context Without Limits.
Memory Without Loss.
Drop-in OpenAI SDK API providing infinite window context length and zero-forgetting associative memory on the Volumetric Lattice Network. Process multi-million token document archives with sub-quadratic scaling.
from openai import OpenAI # Initialize OpenAI client pointed at Infinite Context API bridge client = OpenAI( base_url="https://icx.api.caleralabs.com/v1", api_key="icx_live_your_api_key" ) # Associative memory recall across large document archives response = client.chat.completions.create( model="icx-v1", messages=[ {"role": "system", "content": "You access associative memory on the Volumetric Lattice Network."}, {"role": "user", "content": "Recall exact contractual indemnity cap across all enterprise MSAs."} ] ) print(response.choices[0].message.content)
Architected for Unbounded Recall
Traditional transformer attention scales quadratically O(N^2), forcing aggressive context truncation. ICX uses geometric associative memory to maintain absolute fidelity across arbitrary sequence lengths.
Infinite Context Window
Stream massive repositories, codebases, and legal archives into memory without token window caps or sliding context loss.
Zero-Loss Memory
Hebbian lattice crystallization guarantees exact fact retrieval and provenance tracking without statistical catastrophic forgetting.
Drop-In SDK Integration
100% compatible with official OpenAI client SDKs in Python, TypeScript, Go, and Rust by simply overriding baseURL.
Technology & Trust Boundary
Calera Labs separates memory substrate physics from external language generation.
Memory Lattice Substrate
All facts, tokens, and associations are indexed directly into the Volumetric Lattice Network. Retrieval is deterministic and backed by full origin provenance.
BYOK Generation Boundary
Optional third-party LLMs (such as Google Gemini or OpenAI) act exclusively as external generation layers (BYOK). Customer keys stay local to your session and never modify Calera lattice state.
Sub-Quadratic Scaling
Geometric sparse distributed representations (SDRs) allow sub-quadratic time complexity on long context sequences without memory exhaustion.
4-Phase Memory Lifecycle
How documents transition into permanent, zero-loss associative memory structures.
Ingest
Documents and text streams are parsed into dense sparse distributed representations (SDRs).
Crystallize
SDR representations are mapped into geometric lattice nodes with Hebbian co-activation weights.
Recall
Wave propagation over the lattice identifies relevant context spans with zero needle-in-a-haystack decay.
Scale
Sub-quadratic computational complexity allows memory clusters to expand infinitely with stable latency.
Subscription Tiers
Select a developer preview tier. All subscriptions include single-account management and multi-domain billing on Calera Labs.
Developer
For individual developers testing long-context workflows.
- 50,000 active tokens in memory
- 30 requests/minute burst
- OpenAI SDK Compatibility
- Community Forum Support
Team
For engineering teams building memory-augmented AI applications.
- 5,000,000 active tokens in memory
- 150 requests/minute burst
- Shared Team Memory Spaces
- Real-Time Memory Inspection UI
- Priority Slack Support
Enterprise
For large organization archives requiring dedicated lattice instances.
- High-capacity token memory
- 1,500 requests/minute burst
- Dedicated Cloud Run Worker Nodes
- Dedicated SLA & Isolation
Dedicated Enterprise Worker Nodes
Provision isolated, high-throughput Cloud Run worker instances with guaranteed 1,500 req/min burst rates, dedicated CPU/memory specs, and private memory space isolation for enterprise security requirements.