Sparky*
Sparky*
Sparky*
Document AI

GenAI Copilot for Structured Finance

The challenge
Structured finance analysts dig through term sheets, models, portfolio data, committee memos, and emails before real analysis can start. Knowledge sits in people’s heads and new hires take long to ramp up.

What we built
We built a GenAI copilot that works like an extra team member. Analysts ask questions in plain language and get clear answers from internal data, with links back to the original sources.

A secure multi-agent framework handles retrieval, reasoning, and compliance checks, with role-based access and monitoring of answer quality. The copilot explains deals, highlights risks, and compares scenarios across transactions, inside the tools the team already uses.

Business value

  • Less time searching, faster answers
  • Consistent use of information across the team
  • Faster onboarding of new analysts
  • Sensitive data protected by role-based access

Where else it applies
The same approach transfers to private equity, credit risk, insurance underwriting, and legal deal teams. It belongs to our Document and Knowledge AI work: software that reads, sorts, and answers questions from documents, so teams stop searching and re-entering information by hand.

Task

Give finance professionals a safe GenAI copilot for documents and models.

  • Strategy

    A multi-agent GenAI framework with RAG, role-based access, and continuous quality monitoring.

  • Design

    Domain-tuned agents connected to the data lake and document stores, a conversational UI, and a quality dashboard.

  • Client

    FinTech startup in New York (USA)

  • Tags

    chatbot, data lake, Finance, genai

Let’s build together

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