Concurrent document processing across Go and Python services, with retrieval-augmented generation over legal documents.
We do not publish client names
Legal teams needed to query, generate and act on large volumes of documents, which meant handling many concurrent document operations without the system falling over.
A distributed microservices architecture in Go and Python for concurrent document processing, with a Redis Cluster caching layer for retrieval performance and Redis pub/sub driving asynchronous processing. Retrieval-augmented generation built on a vector database with function calling. Prompts for document generation, partial update and deletion were developed through dedicated research.
A working legal assistant capable of querying, generating and acting on documents, with full API documentation for client and team onboarding.
Atlas turns scattered documents, meeting notes, and transcripts into a connected map of the people, projects, and facts inside them. Ask a question in plain language and Atlas…
Read the case study Atlas — Your company’s evergrowing knowledge, mappedDistributed Go services processing regulatory rules for search, sharing and management on one platform.
Four microservices powering retrieval, generation, configuration and memory as a knowledge agent inside a larger agentic system.
Describe the system and the constraint you have hit. You will get a technical reply, not a sales sequence.
