The problem: Most RAG demos answer "what does the doc say?". The hard question is the inverse: "who is violating the policy — and prove it."
Why a chatbot demo isn't enough for the enterprise (governance, audit, data residency)
Meet VeritasGraph Studio — one workspace, ten capabilities:
- Agents — Compose role-focused agents (orchestrator, reviewer, assistant) and wire exactly which capabilities each one gets — Knowledge Graph, Tools, Memory, Guardrails, and Data logging — with a per-agent context budget.
- Tools — Register callable tools (internal APIs, MCP servers, retrievers) with schema and auth, then run smoke tests before any agent can use them — so nothing ships un-validated.
- Knowledge — Manage indexed corpora with hybrid search plus graph-linking, and tune chunk size and overlap for high attribution confidence.
- Guardrails — Define hard-block and review policies — PII redaction, toxicity monitoring — enforced on every agent turn, with a live incident count for audit.
- Memory — Give agents session (short-term) and long-term memory scopes, with hygiene checks that flag stale, duplicated, or conflicting memories.
- Data — Connect operational data — SQL, files, events, object storage — and gate it with pre-index quality checks to reduce noisy retrieval.
- Evaluation — Benchmark every agent for relevance, faithfulness, latency, and policy compliance, and track a rolling quality trend across runs — so you catch regressions before release.
- Fine-tune — Queue domain-adaptation jobs on curated data slices, with enforced safety gates on every fine-tuned checkpoint.
- Playground — Test any agent live against its local model and watch the full orchestration pipeline — guardrails in, memory, knowledge graph, context budget, tools, guardrails out, data log — with citations on every answer.
- Knowledge Graph — Build a VeritasGraph knowledge graph from your documents and reason over it with verifiable citations and an explicit reasoning path.
The governed agent pipeline (guardrails in → memory → knowledge graph → context budget → tools → guardrails out → data log)
Citations + PROV-O lineage on every answer
Install & first run (local, Ollama):
pip install veritas-reason
pip install veritasgraph
Build a compliance agent end-to-end (create → wire tools/knowledge → evaluate → deploy)
Why enterprise teams pick it:
- Governed by design — guardrails, evaluation, and data-quality gates are first-class, not bolted on.
- Auditable — verifiable citations and PROV-O lineage on every answer for compliance and legal review.
- Air-gapped — runs entirely on your infrastructure via Ollama; no data egress, no third-party API keys.
- One workspace — build, test, evaluate, and govern agents in a single UI instead of stitching five tools together.
Wrap-up + repo link:https://github.com/bibinprathap/VeritasGraph
https://bibinprathap.github.io/VeritasGraph/studio/
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