At IP Copilot, we’re revolutionizing intellectual property invention discovery & IP management by processing patents and internal communications in real time. Our AI platform helps legal teams and companies navigate the complex world of IP by making patent analysis faster, more accurate, and more accessible than ever before.
AI is fundamental to everything we do. Our platform processes vast amounts of patent data, analyzes legal documents, and provides intelligent insights that would take human experts weeks to generate.
As we’ve grown, our infrastructure needs have become more complex and demanding. We need systems that can scale with us:
- Handle diverse GPU types and pricing models to keep costs manageable
- Support rapid experimentation with multiple models
- Provide flexibility for compliance and customer requirements
Challenges of Scaling AI for Intellectual Property Management
1. Scaling while keeping costs low
Processing large amounts of patent data is expensive. Our platform analyzes thousands of patents and legal documents daily, requiring significant GPU time.
Running these workloads on traditional on-demand GPUs quickly becomes cost-prohibitive. We needed a smarter approach to GPU utilization that could adapt to different workload requirements.
2. Fast iteration cycles
The legal AI space moves quickly. At times we test 6–7 different AI models per week to find optimal solutions for specific legal use cases. Each model might excel at different aspects—patent claim analysis, prior art discovery, or legal document summarization.
In this competitive landscape, the ability to rapidly deploy and test new models directly without downtime directly impacts our ability to serve customers and stay ahead of competitors.
3. Compliance needs require infrastructure flexibility
Legal data has strict sovereignty constraints. Local laws and regulations require that data is processed in specific regions. This means the compute infrastructure for our models had to be designed around the data sovereignty requirements of each customer.
Even after co-locating compute and data, customer requirements vary significantly. Enterprise legal departments might need isolated, dedicated AI clusters for sensitive patent analysis, while smaller law firms prefer cost-effective shared resources. Others may require running the models in their own cloud. This requires a highly flexible infrastructure stack that could run anywhere to meet the needs of each customer.
“Every customer is unique. From their integration requirements, data volume, to the way they write. Having the ability to dynamically scale our AI infrastructure is key to being responsive and cost effective. Skypilot enables us to be more accurate, secure and cost effective compared to AI alternatives.”
—Austin Walters, CEO, IP Copilot
How SkyPilot Powers IP Copilot’s AI Infrastructure
SkyPilot has allowed us to build a flexible, multi-cloud foundation for our AI infrastructure that can adapt to the needs of our customers, while keeping costs down and development velocity high.
1. 80% cost savings through intelligent spot instances
Previously, we relied entirely on on-demand GPU instances. SkyPilot’s auto-recovery feature for Managed Jobs enables us to switch to spot instances, cutting our compute costs by 80% while maintaining high reliability even during spot instance preemptions.
Further, SkyPilot allows us to easily experiment with different GPU types – A100s, H100s, and others – to identify the most cost-efficient option for each specific workload. Switching GPU types is now as easy as changing a flag:
Copied!# task.yaml resources: accelerators: H100:8 # Change this line to switch to A100s use_spot: true # Add this line to switch to spot instances
2. 3x faster development cycles
One of the biggest bottlenecks to fast iteration is the manual process of provisioning a GPU instance, setting up the environment, making sure the dependencies align, running the job, collecting the logs and tearing down the instance.
SkyPilot automated all these steps by allowing us to declaratively defining a job and its dependencies in the task YAML:
Copied!# Example task.yaml serving with vLLM resources: accelerators: H100:8 use_spot: true image_id: <docker-image>:latest env: - name: MODEL_NAME value: "llama3.1-8b-instruct" setup: | pip install -r requirements.txt run: | python -m vllm.entrypoints.openai.api_server \ --model-name $MODEL_NAME \ --port 8000 \ --host 0.0.0.0
SkyPilot enables our team to iterate through multiple models in a single day, dramatically accelerating our development process. Everything runs behind an Application Load Balancer on AWS, making model swapping seamless. Our developers can focus on model performance rather than setting up the deployment.
“Before SkyPilot, I could evaluate three models a day. With SkyPilot, ten models a day with is easy. I can now also constantly tweak model parameters which means ten models + fine tuning iterations.”
—Jeremy Goodsitt, Lead Machine Learning Engineer, IP Copilot
3. Bring-your-own-compute for control and flexibility
In regulated industries like legal technology, maintaining complete control over the compute infrastructure is critical. SkyPilot’s Bring-Your-Own-Cloud (BYOC) approach allows us to tightly control the infrastructure that runs our models, ensuring we meet all compliance requirements.
Data sovereignty requirements can also be easily met by configuring SkyPilot to launch in specific regions (e.g., us-east-1). We still have the flexibility to expand to other regions or clouds as customer needs evolve. Whether a Fortune 500 company needs an isolated compute setup for confidential patent analysis or a startup law firm wants cost-effective shared resources, SkyPilot makes it straightforward to adapt our infrastructure to each customer’s requirements.
Results: 80% lower costs, 3x faster development, and flexible infrastructure
SkyPilot has fundamentally transformed how we develop and deploy AI for intellectual property analysis:
- 80% cost reduction: By intelligently using spot instances and optimizing GPU selection, we’ve significantly reduced our compute costs while maintaining service reliability.
- 3x faster development velocity: We can now iterate through multiple AI models in the same day, testing new approaches to patent analysis and legal document processing without infrastructure delays.
- Customer-focused flexibility: The ability to quickly adapt infrastructure to customer needs – from dedicated clusters for sensitive IP analysis to shared resources for cost-conscious clients – has become a competitive advantage.
- Simplified multi-cloud operations: SkyPilot handles the complexity of managing diverse infrastructure, letting our small team focus on improving AI models rather than fighting with deployment issues.
Most importantly, SkyPilot enables us to focus on what matters most: building better AI tools for intellectual property discovery. Instead of spending engineering time on infrastructure management, we can concentrate on improving patent analysis accuracy, developing new legal AI capabilities, and serving our customers’ evolving needs.
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