| <div align="center"> |
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| # Cosavu |
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| ### Context Intelligence for Enterprise AI |
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| Build AI systems that are **cheaper**, **more reliable**, and **production-ready** through intelligent context allocation. |
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| <p align="center"> |
| <img src="image.png" alt="Cosavu Banner" width="100%"> |
| </p> |
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| [Website](https://cosavu.com) • [Documentation](https://docs.cosavu.com) • [Hugging Face](https://huggingface.co/Cosavu) |
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| </div> |
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| --- |
|
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| ## AI doesn't have an intelligence problem anymore. |
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| It has a **context problem**. |
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| Today's LLMs are incredibly capable, yet most production AI systems still suffer from: |
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| - Rising inference costs |
| - Hallucinations from weak retrieval |
| - Context window bloat |
| - Unnecessary model calls |
| - Poor governance and observability |
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| The bottleneck isn't the model. |
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| It's **what reaches the model.** |
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| --- |
|
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| # What is Cosavu? |
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| Cosavu is a **Context Intelligence Layer** that sits between enterprise data and Large Language Models. |
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| Instead of sending everything into an LLM, Cosavu determines: |
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| - What information should be retrieved |
| - What should be ignored |
| - What must remain verbatim |
| - What can be compressed |
| - Which model should execute the task |
| - How much context is actually required |
| - How the output should be governed |
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| The result is AI that costs less, responds faster, and produces more reliable answers. |
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| --- |
|
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| # Core Technologies |
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| ## STAN |
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| **Synergistic Token Allocation Network** |
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| Our decision engine that dynamically allocates context, optimizes token usage, routes requests across models, and controls output quality. |
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| --- |
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| ## Hybrid Retrieval |
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| Cosavu combines multiple retrieval techniques instead of relying solely on vector search. |
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| - Vector Retrieval |
| - Hash-based Retrieval |
| - Structured Data Access |
| - N-Gram Search |
| - Policy-aware Ranking |
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| --- |
|
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| ## Context Intelligence |
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| Instead of retrieving the **most similar** information, |
| Cosavu retrieves the **minimum trusted context** required for a decision. |
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| --- |
|
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| # What Cosavu Optimizes |
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| - AI Inference Cost |
| - Context Window Utilization |
| - Output Token Efficiency |
| - Hallucination Reduction |
| - Enterprise Governance |
| - Model Routing |
| - Workflow Observability |
| - Secure Context Management |
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| --- |
|
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| # Built for Enterprise AI |
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| Cosavu integrates with existing AI stacks and enterprise infrastructure. |
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| Compatible with |
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| - OpenAI |
| - Anthropic |
| - Google Gemini |
| - OpenRouter |
| - Azure OpenAI |
| - Self-hosted LLMs |
| - Enterprise Knowledge Bases |
| - SQL Databases |
| - Vector Databases |
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| --- |
|
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| # Research Areas |
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| We actively research |
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| - Context Intelligence |
| - Context Allocation |
| - AI Cost Optimization |
| - Enterprise Retrieval Systems |
| - Reinforcement Learning for AI Infrastructure |
| - Agentic AI Infrastructure |
| - Hybrid Retrieval Architectures |
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| --- |
|
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| # Vision |
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| We believe the next generation of AI infrastructure won't be defined by larger models. |
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| It will be defined by **better context.** |
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| Just as operating systems manage compute resources, |
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| **Cosavu manages intelligence resources.** |
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| --- |
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| <div align="center"> |
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| ### Enterprise AI starts with Context. |
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| **https://cosavu.com** |
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| </div> |
