README.md

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title: CoDynamics Lab Corporation
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  <img src="https://www.codynamicslab.com/logo.png" alt="CoDynamics Lab" width="180" />
  <h1>CoDynamics Lab Corporation</h1>
  <p><strong>Eliminating the Long-Context Tax in enterprise AI.</strong></p>

  <a href="https://www.codynamicslab.com">🌐 Website</a> &nbsp;|&nbsp;
  <a href="mailto:mike@codynamicslab.com">✉️ Contact</a> &nbsp;|&nbsp;
  <a href="https://huggingface.co/CoDynamicsLab/LATCH-Qwen2.5-14B">🔒 Request Model Access</a>
</div>

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## What We Build

Standard LLMs impose a compounding penalty as context grows — linear prefill cost, high latency, and expensive re-ingestion every single query. We built **LATCH** (Latent Activation Token Cache Handoff) to eliminate this entirely.

LATCH is a proprietary, model-agnostic inference layer that compiles documents once into a persistent latent representation and hands it directly into the decode path — achieving **constant-time performance regardless of document length**.

The result: responses that begin in under 120 milliseconds, infrastructure costs that collapse, and cross-document reasoning that scales.

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## LATCH Performance — Verified Results

| Model Family | Status | Avg. TTFT Speedup | E2E Speedup | Multi-Doc Pass Rate |
|---|---|---|---|---|
| **Qwen 2.5 14B** | ✅ Production Ready | **42.9×** | **5.2×** | **91.7% (11/12)** |
| **Mistral Nemo 12B** | ✅ Verified | **104.0×** | **19.7×** | **83.3% (10/12)** |
| **Llama 3.1 8B** | ✅ Verified | **116.3×** | **12.9×** | **83.3% (10/12)** |
| **DeepSeek R1 Distill** | ✅ Verified | **43.0×** | **3.7×** | **75.0% (9/12)** |

> **Headline:** Time-To-First-Token on Qwen 2.5 14B reduced from **23.1s → 0.11s** (210× improvement on cold load).  
> **Persistent cache reload:** **0.0016s** — a 246× speedup over standard re-ingestion.

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## The Economics

Every query against an uncompiled document re-pays the full prefill cost. LATCH breaks this model.

- **Break-even at 0.0051 queries** — the cost of compilation is recovered in the first fraction of a single query
- **~30 GB VRAM** to run Qwen 2.5 14B (vs ~61 GB baseline) — more models per node
- Strongest amortization case: **28.5× end-to-end cost reduction** at scale

For teams running high-volume document analysis — M&A due diligence, legal review, compliance monitoring, financial research — this is a structural cost advantage, not a marginal one.

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## Built For

| Use Case | What LATCH Changes |
|---|---|
| **M&A / Private Equity Due Diligence** | Compile the data room once. Query hundreds of documents in seconds per session. |
| **Legal Document Review** | Cross-contract reasoning at constant latency across large clause sets. |
| **Compliance & Regulatory Analysis** | Persistent document memory means re-runs are nearly free. |
| **Financial Research** | Multi-document synthesis with sub-second response on dense filings. |

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## Deployment Options

**🔒 Self-Hosted License — $79**  
Locked model weights + inference runtime for your own A100/H100 infrastructure. Data never leaves your environment. License key delivery via [Gumroad](#) after purchase.

**☁️ Managed Instance — from $5.00 A100 / $10.00 H100 /hr**  
Spin up a LATCH-ready GPU instance directly. Includes batch JSON query interface — upload your document set, submit a structured prompt list, export results. Billed by wall-clock second. Coming soon.

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The LATCH compilation method and neural representation format are proprietary to CoDynamics Lab Corporation.

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## Licensing

CoDynamics Lab Corporation operates under a **Proprietary & Commercial Licensing** model.

- **Gated Access:** Model weights and inference adapters are provided via approved repository requests only
- **Commercial Use:** Production or commercial deployment requires a separate license agreement
- **Research Inquiries:** Academic or research access requests considered case-by-case

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<div align="center">
  <strong>Commercial Inquiries & Gated Access Requests</strong><br/>
  <a href="mailto:mike@codynamicslab.com">mike@codynamicslab.com</a> &nbsp;|&nbsp;
  <a href="https://www.codynamicslab.com">www.codynamicslab.com</a>
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