Consulting and development of IT solutions. We turn complex data into actionable intelligence using AI, RAG architectures, and modern development practices.
A language model's trained knowledge does not automatically track changes in the world. Prices, filings, product releases and events keep changing. Without fresh input, a model can produce a fluent answer that is outdated or incorrect.
Production systems close this gap at run time. Retrieval pipelines (RAG) search an organization's documents before the model answers, and agents call tools and APIs to read current data before they act. The model contributes reasoning and language; the data contributes the facts.
The quality of an AI system is therefore bounded by its data: which sources it reads, how current each value is and where it came from. Teams need to evaluate coverage, freshness and reliability alongside model quality and system design.
s0nar labs works on both sides of this problem. We build the pipelines that turn documents and internal systems into knowledge a model can retrieve, and we operate data feeds that give agents current facts with a recorded source and timestamp.
From intelligent document pipelines to blockchain-verified data feeds, we build systems that improve how organizations use their data.
LLM integration, RAG architectures, AI agent design, and knowledge base engineering. We help organizations adopt AI responsibly with deterministic, observable pipelines.
End-to-end PDF pipelines: OCR, parsing, spatial text extraction, batch conversion strategies, and structured output. From raw documents to vector-store-ready knowledge.
Modern web applications with FastAPI backends, Next.js frontends, SQL Server integration, and streaming AI diagnostics. Legacy system modernization with enterprise-grade reliability.
Deterministic data extraction, on-chain verification, integrity hashing, and regularly updated data feeds. Production-grade pipelines with circuit breakers, rate limiters, and failure monitoring.
Kubernetes deployment, real-time log analysis, monitoring dashboards, CI/CD automation, and AI-assisted error diagnosis.
Agents and models need current facts with a traceable origin. We collect data from public sources, normalize it into one schema and deliver it as structured feeds that agents can cite and teams can audit.
The feeds cover markets, prediction markets, on-chain activity, company filings and macro data, news and attention signals, and real-world events. Records keep their source identifiers and timing information so applications can track updates across feeds.
Every solution goes through a rigorous design phase before a single line of code. We measure quality deterministically and evaluate outcomes with LLM-as-judge pipelines.
We study your data, systems, and goals. PDF analysis, architecture mapping, and feasibility assessment before any development begins.
YAML-driven design documents, conversion strategies, and working prototypes. Every decision is documented and traceable.
Deterministic quality scoring (96-98%), LLM-as-judge evaluation via Langfuse, and automated test coverage over 80%.
Kubernetes deployment with observability built in. Real-time monitoring, live log analysis, and continuous improvement through measurable feedback loops.
We choose proven, production-grade technologies.
Let's talk about how AI can transform your operations, data, and decision-making.