Toptal creates AI search solution for IP startup, enabling 5x faster data processing.
An early-stage IP technology company turned to Toptal to develop a scalable, cost-efficient RAG architecture that could power semantic search across millions of patent claims.
Client
A US-based IP intelligence startup offering AI-driven search and analytics over patent and IP data.
Industry
TechnologyDelivered Services
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The company’s reliance on outdated keyword search hindered semantic accuracy, delayed MVP delivery, and threatened investor traction in a fast-moving market.
Solution
Modular Pipeline Architecture
Toptal implemented a scalable retrieval-augmented generation (RAG) framework using Azure Databricks, Spark, and open-source embedding models to ingest, chunk, and semantically index patent data with precision.
High-performance Search Stack
Toptal deployed a Pinecone-backed vector retrieval system orchestrated by LangChain, optimizing throughput with GPU-accelerated micro-batching and Terraform-provisioned infrastructure.
Outcome
Accelerated MVP Delivery
The system achieved a 5x improvement in embedding speed—processing 500 claims/second—and enabled a demo-ready MVP in 10 weeks, securing critical investor interest.
Competitive Market Advantage
The semantic-first pipeline boosted recall by 60% over traditional search and reduced NLP infrastructure costs by 70%, positioning the startup for expansion into verticals like pharma and energy.
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