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README.md

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-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Solvren AI
+
+AI forward deployment for mid-market regulated teams. One production AI workflow per engagement, built inside your own cloud in 6 to 8 weeks.
+
+## What we do
+
+- Our engineers embed with your team and build one production AI workflow per engagement.
+- The build runs inside your own AWS, Azure or GCP account, on models you already approved.
+- You keep the code, the infrastructure and the runbooks.
+- Systems we have built: an AI skills registry with security scanning, agentic payments on Amazon Bedrock AgentCore, and Class 1 Decider, a small routing layer in front of LLM calls.
+
+We work with biotech, healthcare, defense, legal and fintech teams. Founded in 2026, based in San Diego.
+
+## Open models & datasets
+
+| Artifact | What it is | Result |
+|---|---|---|
+| [class1-router-qwen3-0.6b-mlx](https://huggingface.co/SolvrenAI/class1-router-qwen3-0.6b-mlx) | Qwen3-0.6B LoRA request router, MLX | 0.88 accuracy on 50 hand-written requests (untuned Qwen3-4B: 0.90), a third to half the p50 latency |
+| [class1-router-synthetic](https://huggingface.co/datasets/SolvrenAI/class1-router-synthetic) | Router training data, no real PII or PHI | 1,536 template rows + 50 hand-written test rows |
+| [bge-base-en-v1.5-hipaa](https://huggingface.co/SolvrenAI/bge-base-en-v1.5-hipaa) | HIPAA retrieval embeddings | NDCG@10 0.907 vs 0.834 for the base model, 99 held-out queries |
+| [hipaa-retrieval-pairs](https://huggingface.co/datasets/SolvrenAI/hipaa-retrieval-pairs) | Query-passage pairs over 45 CFR Parts 160 and 164 | 912 pairs over 304 regulation chunks |
+
+Read the numbers with care:
+
+- These are demos trained on synthetic or LLM-generated data.
+- The test sets are small (n=50 and n=99), so one error moves the router's accuracy by 2 points.
+- Router latency was measured on one shared laptop that was running other jobs, so it is approximate.
+- On the HIPAA eval the fine-tuned embedding model also scores ahead of bge-large-en-v1.5 (0.864) and snowflake-arctic-embed-m-v1.5 (0.865), with no meaningful change on BEIR SciFact.
+
+Each model card covers the eval setup and its limitations.
+
+## Code
+
+- [solvren-ai/class1-router](https://github.com/solvren-ai/class1-router): data generation, LoRA training and evaluation for the router
+- [solvren-ai/domain-embedding-finetune](https://github.com/solvren-ai/domain-embedding-finetune): local embedding fine-tuning pipeline
+
+## Compliance and partners
+
+We build for teams working under HIPAA, SOC 2, ITAR and CMMC. Compliance and any certification or authorization stay with your organization and your auditors.
+
+Solvren AI is an OpenAI Select Partner and an AWS Partner Network member.
+
+## Citing our work
+
+Each model and dataset card has a Citation section with a BibTeX entry and upstream credits. Please cite the specific artifact you use.
+
+## Contact
+
+[solvren.ai](https://solvren.ai) · [Contact](https://solvren.ai/contact/) · [nitya@solvren.ai](mailto:nitya@solvren.ai)