Today we’re launching Forge, our AI Native Service, at Datasaur. https://lnkd.in/gHkCzV92 Forge is for regulated enterprises that cannot send sensitive data to a third-party model endpoint. We embed Datasaur engineers directly inside financial, healthcare, insurance, legal and government organizations to build AI systems that run entirely inside the customer’s own infrastructure. No customer data is sent to Datasaur or to any model vendor for inference, fine-tuning, or evaluation. Customers can run frontier APIs, open-weight models, or their own fine-tuned models — and swap them as the frontier evolves. The future of enterprise AI is private, model-agnostic, and customer-owned. That future is here. And you own it.
Datasaur
Software Development
San Francisco Bay Area, California 3,675 followers
The AI-native services firm for Private AI in the enterprise
About us
Datasaur builds and deploys Private AI for organizations that can't send their data to someone else's cloud — banks, hospitals, government agencies, and enterprises handling regulated, proprietary, or competitively sensitive information. Private AI means the model, the data, and the inference run inside the customer's perimeter, on their terms, under their controls. We are an AI-native services firm. Traditional systems integrators learned AI as an add-on; we were built around it. Every engagement starts from the assumption that the harness around the model — the data pipeline, the evaluation loop, the agent scaffolding, the deployment infrastructure — is where the real engineering lives. The model is commoditized. The system around it is not. Our work spans three layers: Data — labeling, curation, evaluation, and the feedback loops that keep models accurate as the world drifts. Model — fine-tuning, distillation, and selection work that matches capability to the customer's constraints (latency, cost, sovereignty, risk). Agent — the orchestration, tool use, and guardrails that turn a model into a system the operations team actually trusts. Customers come to us when they have tried the off-the-shelf approach and hit a wall — usually around compliance, accuracy in their domain, or the integration work no vendor demo prepares them for. We get them from there to production. If you are evaluating Private AI for your organization, we should talk.
- Website
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http://www.datasaur.ai
External link for Datasaur
- Industry
- Software Development
- Company size
- 51-200 employees
- Headquarters
- San Francisco Bay Area, California
- Type
- Privately Held
- Founded
- 2019
Locations
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Primary
Get directions
San Francisco Bay Area, California, US
Employees at Datasaur
Updates
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Datasaur reposted this
Datasaur rolled out a fresh new look and clarified how we can help with building Private LLMs and Agents. Ask for help: let me know if it makes sense or resonates! Trying to strike the right balance between high-level explanation of our offerings and some technical clarification on what makes our approach unique. https://datasaur.ai/ Our NLP labeling platform continues to be served at datasaur.ai/studio
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🚀 Powering Smarter, Faster Data Labeling As we move past 2025, we’re excited to share new Datasaur features built to make data labeling more efficient, flexible, and intelligent. Whether you’re annotating for NLP, managing complex workflows, or building datasets for LLM training. These updates are designed to help teams work smarter and scale faster. 👉 Read what’s new: https://lnkd.in/gpvQQphA
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From Agents to Autonomy: A Practical Framework for Agentic AI Agentic AI isn’t binary. Autonomy exists on a spectrum, and understanding where a system sits is critical to building AI that’s useful, safe, and reliable. Our latest post introduces five levels of Agentic AI, from deterministic task bots to fully autonomous problem solvers: providing teams a shared language to design, evaluate, and govern AI systems responsibly. 🔗 Read the full framework: https://lnkd.in/edEadTQU
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From 50 hours to 5. Grey Group, a global creative agency, transformed their market-insights workflow using Datasaur—cutting classification time by 10× while improving consistency and accuracy. By replacing manual review with Datasaur's AI, their team: - Reduced audits from ~40–50 hours to under 5 - Eliminated manual classification of their data - Achieved efficiency gains while enjoying improved accuracy 📊 This case study breaks down how Datasaur AI made it possible . 👉 Read the full case study: https://lnkd.in/gna3zRGT
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Datasaur reposted this
We just wrapped a busy stretch of conferences at Datasaur across tech, legal, banking, healthcare, and insurance. One thing kept coming up in conversations with people from each industry. The sentiment around AI trust has really changed since these same events last year. Last year, most conversations were cautious. People were still figuring out what AI could safely touch. Now the vibe is different. There’s a real sense of confidence. After months of InfoSec, third party audits, and vendor assessments, companies have learned how to safely enable enterprise versions of ChatGPT, Claude, and Gemini for about 70-80% of their workflows and tasks. The remaining 20-30% is where things stand still due to privacy and compliance. • Privileged client communications for law firms • Regulatory correspondence and audit findings in banking • Medical claims with patient identifiers in healthcare • Claims histories with VIN data in insurance These are just some of the high-utility, high-impact workflows that are not being ignored. They are being protected. The future of enterprise AI is not just Enterprise ChatGPT, Claude, or Gemini. It is Enterprise ChatGPT, Claude, or Gemini AND Private LLMs. The commercial enterprise options drive employee productivity and adoption. Private LLMs power the heavily compliant, nuanced projects that deliver highly measurable ROI. Together, they complete the puzzle of secure and responsible AI adoption. Organizations that recognize this shift early are already completing the puzzle. The rest will be playing catch-up. #AI #GenerativeAI #ResponsibleAI #AIForBusiness #EnterpriseAI