Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Deploy AI agents to mine R&D notes, patent filings, and technical documents to uncover hidden IP, innovation opportunities, and infringement risks.
Your company's most valuable assets are locked in legacy PDFs, internal wikis, and decades of R&D notes. We build AI systems that automatically surface undiscovered intellectual property, potential patent infringements, and white-space innovation opportunities from these unstructured archives.
Transform your archival data from a compliance burden into a strategic IP asset and innovation engine, reducing manual review time by 80% and accelerating time-to-patent.
Our approach integrates domain-specific language model (DSLM) training on your proprietary corpus and Retrieval-Augmented Generation (RAG) infrastructure connected to trusted knowledge bases. This ensures findings are accurate, traceable, and actionable. For related data architecture, see our services on Unstructured Data Lakehouse Architecture and Enterprise Knowledge Graph Construction.
Our AI agents don't just find data—they deliver measurable business value by uncovering hidden assets, mitigating risk, and identifying new revenue streams locked within your archives.
Identify previously overlooked inventions, processes, and trade secrets within R&D notes and technical documents that can be patented, licensed, or commercialized to create new revenue.
Systematically scan internal archives and public patent databases to identify potential infringement risks before they become costly litigation, ensuring freedom to operate.
Surface prior internal research, failed experiments, and tangential discoveries to prevent redundant work and provide new starting points for current innovation projects.
Provide a complete, AI-validated inventory of a target company's intellectual property assets and liabilities, far beyond standard legal reviews, for more accurate valuation.
Strategically map your innovation landscape to identify whitespace and guide R&D investment towards building stronger, more defensible intellectual property portfolios.
Automatically flag technical data and IP subject to ITAR, EAR, or other export controls buried within archives, preventing unintentional compliance violations.
A typical 6-8 week engagement to deploy AI agents for mining R&D archives, patent filings, and technical documents to surface undiscovered IP and innovation opportunities.
| Phase & Key Deliverables | Timeline | Inference Systems Team | Client Commitment |
|---|---|---|---|
Discovery & Archive Assessment | Week 1 | Technical deep-dive on data sources, formats, and volume; Initial IP taxonomy definition | Provide data access and subject matter expert interviews |
AI Pipeline Architecture & Agent Design | Weeks 2-3 | Custom agent workflow design; Vector database and RAG system setup; Security and access controls | Review and approve technical architecture |
Model Fine-Tuning & Validation | Weeks 4-5 | Domain-Specific Language Model (DSLM) tuning on proprietary corpus; Hallucination mitigation testing | Validate model outputs against known IP examples |
Pilot Deployment & Insight Generation | Week 6 | Deploy agents to pilot archive subset; Generate first report of discovered IP candidates and potential infringements | Review initial findings and provide feedback |
Full-Scale Deployment & Integration | Weeks 7-8 | Scale pipeline to full archive; Integrate findings into existing IP management systems (e.g., Anaqua, Clarivate) | IT support for system integration |
Ongoing Monitoring & Reporting | Post-Deployment | Monthly insight reports; Agent retuning as new data arrives; Optional SLA for 99.9% uptime | Designate internal point of contact |
Typical Project Investment | 6-8 weeks | $75K - $150K (scope-dependent) | Internal resource allocation for SME access |
Our AI-powered intellectual property discovery systems are engineered to deliver precise, actionable insights from your most complex archives, accelerating innovation and protecting your most valuable assets.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Get specific answers on how our AI-powered intellectual property discovery service works, from timeline and security to outcomes and support.
Our engagement follows a structured 4-phase process: Discovery & Scoping (1 week), Pipeline Development & Agent Training (1-2 weeks), Discovery Execution & Analysis (1-2 weeks), and Reporting & Handoff (1 week). A typical project from kickoff to final report is completed in 4-6 weeks. This includes deploying specialized AI agents to mine your specified archives (R&D notes, patent filings, internal documents) and delivering a structured report of findings.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
The first call is a practical review of your use case and the right next step.
