Atlanta-based software and AI consulting
QRUV Corp helps small teams and businesses move AI ideas from prototype to production. We focus on the engineering harness around AI systems: retrieval pipelines, APIs, evaluation, observability, cost controls, and workflows that make LLM applications reliable enough to use.
Company
QRUV Corp is based in Atlanta and works with clients on production AI, retrieval, automation, and backend systems.
Focus
RAG, retrieval, LLM applications, backend automation, evaluation, observability, and production readiness.
Contact
support@qruvcorp.comMost AI projects fail after the demo because production requires permissions, interfaces, observability, evaluation, fallback behavior, and cost controls. QRUV works on that surrounding system. The model matters, but the harness around the model is what determines whether users can trust it.
Chunking, permissions, ranking, citations, and refresh behavior are designed so teams can explain why an answer appeared.
We define task-specific test sets, failure cases, acceptance thresholds, and regression checks before a feature reaches users.
Model choice, prompt size, caching, fallbacks, and routing are treated as design decisions, not cleanup work after the bill arrives.
Logging, observability, fallback behavior, and review workflows are part of the system from the start.
We help teams turn prototypes into maintainable systems, especially around RAG, retrieval, search, LLM workflows, and backend automation.
View servicesProduct features, internal tools, support agents, document workflows, and automation systems with clear boundaries between deterministic code and model behavior.
Document ingestion, metadata strategy, hybrid search, permissions-aware retrieval, citations, re-ranking, and answer evaluation.
Test sets, review queues, cost dashboards, traces, quality checks, and release criteria that let teams improve AI behavior intentionally.
APIs, databases, queues, integrations, file processing, and admin workflows that support practical business operations.
Practical writing on the parts of AI projects that usually get discovered too late: retrieval quality, evals, cost behavior, and handoff.
AI coding agents may write the code, but humans still have to set up the environment: permissions, tools, tests, sandboxes, guardrails, and observability.
Why the useful part of an AI project is often the APIs, workflows, evals, logs, permissions, and fallback behavior around the model.
A practical look at retrieval failure modes: chunking, permissions, stale data, missing evals, citations, and user trust.
Send a short note about the workflow, users, data sources, and where the current system breaks. QRUV will respond with practical next steps.