AI Expert Amol Walvekar Builds First-Ever RAG-Powered, Custom AI for Finance Processes

Anchor, from Adra Technologies, builds the first retrieval-augmented generation to the finance back office built by a product leader who shipped enterprise-grade AI at JP Morgan, WePay, Lead, Bolt, and Insight Data Science.

San Francisco, CA, August 7th, 2026— Amol Walvekar, an AI expert who has spent his career building enterprise-grade machine learning systems, today announced the launch of Anchor’s industry first RAG for finance teams, and custom agents built specifically for finance processes. Anchor by Adra Technologies Inc. enables finance teams to build, operate, and scale AI agents for complex back-office work directly within their ERP, CRM, and data warehouse.

Understanding RAG, Its Enterprise Value, and Proven ROI

Retrieval-Augmented Generation (RAG) is an architectural framework that enhances the functionality of Large Language Models (LLMs) by integrating external data retrieval mechanisms. Rather than relying solely on static training data or forcing models to guess. The core business value of RAG lies in its ability to deliver secure, highly accurate, and audit-ready outputs across complex structured and unstructured corporate data.

By grounding LLM responses in an organization’s internal datasets—such as contracts, policies, and systems records—RAG dramatically reduces hallucinations, eliminates the risks of stale static knowledge, and maintains strict enterprise-grade compliance without the high expenses of continuous model retraining.

Leading industry implementations and technical benchmarks highlight the significant operational ROI of optimized RAG systems:

• Error Reduction: Advanced semantic filtering has demonstrated the ability to reduce incorrect or partially correct RAG outputs by up to 80% while boosting baseline retrieval accuracy by over 20 percentage points.
• Frontier Accuracy: In standardized end-to-end evaluations like the RAG-QA Arena, optimized systems utilizing fully integrated architectures consistently outperform general frontier models.
• Efficiency Gains: Deep-dive development platforms focused on enterprise AI evaluation loops have successfully accelerated engineering cycles, delivering up to a 10x improvement in model prototyping, validation, and production deployment speeds.

Walvekar’s expertise was forged inside some of the most demanding enterprise AI environments in financial services. At WePay, through its acquisition by JP Morgan, he built enterprise-grade ML for risk and payments systems where a model’s decisions carry real financial consequences and must hold up at bank-level standards of accuracy, auditability, and scale. He continued that work across Lead and Bolt, applying machine learning to payments infrastructure where reliability is non-negotiable. As an AI Fellow at Insight, he built and shipped an abstractive text summarization platform for knowledge workers — early, hands-on work in the language-model techniques that now power Anchor.

“Finance processes have resisted automation because generic AI doesn’t know your chart of accounts, your contracts, your ERP, or your policies,” said Walvekar. “Anchor is different by design: it’s retrieval-augmented and custom to each finance team, grounded in their own systems and documents. That’s how you get the accuracy and reliability of a trained analyst. The model isn’t guessing, it’s retrieving and reasoning over the customer’s actual financial data.”

RAG, purpose-built for the finance back office

Anchor’s retrieval-augmented architecture connects directly to the systems where finance work lives including NetSuite, Salesforce, Workday, Microsoft Dynamics, Sage, Snowflake, and more than a dozen other platforms and grounds every agent in the customer’s own data, documents, and processes. Finance teams create custom agents in natural language, with no developer effort, to automate work arising from reconciliations, budget management, sales and marketing spend, commissions, and revenue compliance. The platform is SOC 2 Type II compliant, meeting the highest requirements for data protection and privacy.

The result is AI that behaves the way enterprise AI has to behave: precise, explainable, and dependable at production scale now applied to the finance back office.

Beyond building Adra, Walvekar is an active angel investor and writes on enterprise AI adoption, agentic architectures, and market structure. He holds a graduate degree from Boston University.

About Amol 

Amol is the Cofounder of Adra Technologies Inc. is the company behind Anchor, the first RAG-powered, custom AI platform for finance processes. The company is headquartered in San Francisco. Learn more at www.getadra.com.

Media Contact: founders@getadra.com