# Ricky-7-Yan/intelligent-audit-system

AuditPilot: auditable enterprise AI agents for evidence-grounded workflows, governed tools, evaluation harnesses, human review, and remediation delivery.

Repository: https://github.com/Ricky-7-Yan/intelligent-audit-system
Canonical: https://ross.abutalabs.com/products/intelligent-audit-system
Language: Python
License Family: other
Topics: agent-runtime, agentic-rag, ai-agent, audit, evaluation-harness, fastapi, human-in-the-loop, knowledge-graph, llmops, mcp, python, rag, agent-evaluation, multi-agent
Last push: 2026-08-12T18:32:23+00:00

## Health v2 (maintenance only)
Score: 60/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 97, release rhythm 35, longevity 22
- inputs: {"age_days": 309, "days_push": 21, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1166, forks 110 (observed 2026-08-28T04:03:50.310243+00:00)

## What it is
AuditPilot is an enterprise AI agent workbench for audit delivery, combining evidence-grounded agentic RAG, governed tool execution, evaluation harnesses, human review, and remediation tracking in one traceable workflow. It ships with six built-in audit templates (SOX ITGC, ERP access, data security, change management, backup/BCP, third-party risk) built on Python and FastAPI.

## Use cases
- run SOX ITGC audits with AI-assisted evidence collection
- build auditable enterprise AI agents with human review gates
- evaluate agent outputs with an evaluation harness before release
- track audit findings and remediation tasks to closure
- ground LLM answers in page-level evidence citations
- audit ERP access and segregation-of-duties conflicts
- manage control matrices and sampling plans with versioned templates

## When to choose
- you need evidence-grounded, traceable AI agents for regulated audit or compliance work
- you want built-in evaluation harnesses and human-in-the-loop review before delivering AI outputs
- you need governed tool execution with RBAC, tenant isolation, and audit logs
- you want out-of-the-box audit templates covering SOX, ISO27001, COBIT, and data-security standards

## When to avoid
- you need a general-purpose chatbot or conversational assistant rather than structured audit workflows
- you require semantic vector search as the primary retrieval method (vector search is optional; core retrieval is TF-IDF + keyword)
- you need a mature product with proven production deployments and quantified accuracy claims
- you want a lightweight library to embed in an existing app rather than a full workbench application

## Facets
- artifact type: application
- maturity: active
- function: agent-framework, rag, llm-inference, mcp, web-framework, search-engine, testing, monitoring, auth, authorization, rate-limiting, caching, chatbot
- domain: artificial-intelligence, large-language-models, erp, legal, developer-tools, self-hosted
- platform: python, self-hosted
- tags: audit-automation, human-in-the-loop, evaluation-harness, evidence-grounded, fastapi, knowledge-graph, llmops, multi-agent, sox-itgc, remediation-tracking, ai-agents, retrieval-augmented-generation, audit, web-server, docker

## Member repositories
- Ricky-7-Yan/intelligent-audit-system (main) score 60

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:50.310243+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:28:58.319659+00:00, confidence not recorded.
  - readme: https://github.com/Ricky-7-Yan/intelligent-audit-system (fetched 2026-08-28T04:03:50.310243+00:00, sha ca7203b96b3b)
- Data as of 2026-08-30T08:39:29.467469+00:00.