AI-Powered Finance & Agentic Automation

The gap between AI demos and finance-grade AI

Most enterprises have run AI experiments. Few have moved them into the close, the forecast, or the board pack — because finance demands traceability, deterministic controls, and SOX-defensible evidence that generic AI projects don't produce. The job descriptions hiring CFOs are publishing today are explicit: they want gen AI and agentic AI embedded in record-to-report, FP&A, and reporting, with model governance baked in.

• Forecasting cycles that still take weeks because driver-based models live in disconnected workbooks
• Close narratives, variance commentary, and board decks rebuilt by hand every period
• Policy, contract, and ledger questions that take days to answer because the knowledge isn't retrievable
• AI pilots that can't pass internal audit because prompts, retrieval sources, and outputs aren't versioned

Our approach

We design AI for finance the same way we design financial systems: with controls, evidence, and accountability as first-class requirements. We start with a high-leverage workflow, instrument it end-to-end, and only then scale across the close, the forecast, and the reporting stack.

Agentic FP&A workflows

Forecast, scenario, and variance agents that read your driver model, propose updates, and route to a human approver — with full prompt and decision logging.

Autonomous close acceleration

Account reconciliation, journal proposal, flux explanation, and accrual estimation copilots tied directly to your ERP and reconciliation tooling.

RAG over finance knowledge

Retrieval pipelines over policy, contract, ledger, and prior-period commentary so analysts get cited, source-grounded answers — not hallucinations.

Narrative reporting copilots

Board pack, MD&A, and executive summary generation using your numbers, your tone, and your governance posture — with human-in-the-loop sign-off.

Model risk & auditability

Versioned prompts, evaluation harnesses, golden datasets, and evidence packages aligned to NIST AI RMF and emerging finance AI controls.

Continuous monitoring

Drift detection, hallucination scoring, and outcome tracking so AI quality is measured the same way close quality is measured.

Platforms and stack

We are deliberately platform-fluent. Most engagements span a foundation model provider, an orchestration framework, a vector store, and the finance system of record — and we meet each client where their security perimeter already is.

• Foundation models: Azure OpenAI, AWS Bedrock, Google Vertex AI, Anthropic Claude, OpenAI
• Orchestration & agents: LangChain, LangGraph, Semantic Kernel, LlamaIndex, Microsoft Copilot Studio
• Retrieval & vectors: Pinecone, pgvector, Azure AI Search, OpenSearch, Databricks Vector Search
• Finance systems: Oracle Fusion, SAP S/4HANA, Workday, NetSuite, Federal financial systems
• FP&A & EPM: Anaplan, Workday Adaptive, OneStream, Oracle EPM, Pigment
• Governance & MLOps: MLflow, Weights & Biases, Azure AI Foundry, Bedrock Guardrails, NIST AI RMF

Outcomes we measure

We commit to outcome KPIs at the start of every engagement and instrument them from day one. These are the metrics finance leaders are asking us — and their internal teams — to move.

• Faster — Close cycle days reduction via AI-assisted reconciliation and flux
• Higher — Forecast accuracy vs. baseline driver model
• Lower — Manual hours on narrative reporting and variance commentary
• Audit-ready — Versioned prompts, retrieval sources, and decision logs

Why Artisan Analytix for AI in finance

We don't bolt AI onto a finance practice — we built our finance practice for the AI era. Our team has run enterprise financial systems at federal scale, designed FinOps and TBM programs across 65+ agencies, and operates under ISO 27001 and 9001 controls that AI risk officers respect on day one.

• ISO 27001:2022, 9001:2015, 20000:2018, and 22301:2019 certified — the security posture finance and audit leaders require for production AI
• Federal scale credibility: $100M+ programs, multi-supplier governance, and audit-defensible documentation by default
• Vendor-neutral — we'll build on the foundation model, cloud, and EPM platform you already own
• TBM and FinOps disciplines applied to AI itself, so token and infrastructure spend stays defensible to the CFO

Frequently Asked Questions

Is this just another AI pilot?

No. We scope every engagement around a production target — a workflow that goes live, with owners, controls, and KPIs. Pilots that don't have a path to production aren't worth your CFO's time.

How do you handle model risk and audit?

We version prompts, retrieval sources, evaluation datasets, and decision logs from day one, mapped to NIST AI RMF and the model risk framework your internal audit team already uses. Evidence packages are produced as a deliverable, not an afterthought.

Do you work in federal and regulated environments?

Yes. We work inside FedRAMP-authorized and accredited boundaries, with experience across federal financial systems, multi-supplier IT environments, and commercial regulated finance.