DocQA is engineered specifically for fully air-gapped, high-security environments. The entire infrastructure—including the Large Language Model (LLM via local Ollama), the vector database (ChromaDB), and document storage—runs completely on your local hardware. There are zero cloud API calls, zero telemetry, and zero network egress. Your sensitive documents never touch an external server. 

You can ingest your organization's proprietary or custom standards simply by dropping the reference PDFs into the dedicated folder and re-indexing. 

Unlike generic chat AI, DocQA utilizes a deterministic, multi-agent pipeline. Documents are reviewed through independent specialized lenses (Structure, Content, Quality, Compliance). To minimize noise, the system employs seven built-in false-positive reduction layers, including contradiction detection and policy-driven triage. Crucially, every finding is tied to a specific text passage (chunk_id) for absolute traceability. All findings are intended to support — not substitute — qualified human review.

DocQA ships with a native, user-friendly installer for Windows (.exe), macOS, and Linux. The only prerequisites are that Docker must be running on the host machine (which the setup wizard automatically verifies) and Ollama must be serving your chosen local model. No manual container orchestration or command-line configuration is required. 

DocQA's core analysis pipeline is structurally deterministic: the same triage rules, scoring model, and profile policy are applied consistently every run. However, the LLM Pre-Filter — an additional quality layer that cross-checks "missing content" findings against the rest of the document — is probabilistic by nature. This layer may suppress slightly different findings across runs (typically ±1–2). Importantly, suppressed findings are never silently discarded: they are always preserved in the report's Suppressed Findings section with full reasoning, allowing engineers to review and override any decision they disagree with.