On-premises AI and Retrieval-Augmented Generation that lets organizations query their own documents and knowledge while keeping data under local control.
SRV software projects are designed around specific working processes, with technology used to remove repetitive steps and make information easier to control.
Legal documents, internal procedures, archives and proprietary knowledge can be highly sensitive. Sending them to external services may conflict with confidentiality, data-location or cost requirements, while generic AI models do not automatically know the organization’s own private information.
SRV prototypes combine local/open models with Retrieval-Augmented Generation. Documents or other knowledge sources are indexed as vectors; a user question retrieves the most relevant content and the local model uses that context to generate an answer inside the controlled environment.
A concise view of the functions and design principles that make this application useful in day-to-day work.
Run document processing and model inference on local Linux/GPU infrastructure without relying on a cloud inference service.
Retrieve relevant source chunks before generation so answers can be grounded in the organization’s indexed material.
Use PostgreSQL with a vector extension for efficient semantic indexing and retrieval.
Coordinate ingestion, indexing, retrieval and model interaction through a practical Python-based application stack.
Ask natural-language questions about local documents and receive context-aware answers.
Use models for summaries, entity/date extraction, tagging and other document-assistance tasks.
A legal-document demonstration ran a quantized Qwen model locally on 2017-generation GTX 1080 Ti hardware.
Local model services can be designed to serve multiple internal users from shared infrastructure.
Select local/open models based on the organization’s hardware, language, privacy and performance requirements.
The exact configuration can be adapted, but the core process remains intentionally straightforward.
Load selected documents, text, transcripts or other internal knowledge sources.
Create embeddings and store searchable vectors with source metadata.
For each user question, retrieve the most relevant source content.
Pass that context to the local model and return an answer for user review.
Tell us what you are trying to automate, protect or simplify. We can discuss the product as it exists today and what may need to be adapted for your environment.