Finance AI implementation

AI for Finance

Arvya helps financial firms turn AI into working systems: one verified memory built from the firm's own email, calls, CRM, and documents, with cited outputs and human approval on every consequential action.

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AI for finance works when it connects to the real workflow instead of sitting in a chat box. Arvya's approach: the gold is already in the firm (every deal and relationship left email, calendar entries, calls, and documents behind), so build one verified memory from those sources, cite every fact, and turn the memory into briefs, CRM updates, trackers, and client updates that a human approves. It runs inside the firm's own cloud tenant, and it enriches the systems the firm already owns rather than adding another silo.

// who this is for

Financial firms looking for a practical AI implementation partner rather than another generic AI tool.

Investment, coverage, and operations teams whose context is fragmented across CRM, email, meetings, trackers, and documents.

Leaders who want AI results they can verify (cited, permissioned, approved), not another experiment.

The problem

  • Most AI tools sit in a chat box and do not know the firm's systems, relationships, sources, or approval rules.
  • Each point tool (CRM, data room, tracker, notetaker) keeps its own state, so people carry the real context by hand between them.
  • Teams want AI output they can trust, but generic summaries lack citations, permissions, and a human approval step.

What Arvya does

  • Starts with one high-friction workflow and connects the sources, entities, permissions, and output formats behind it.
  • Builds a verified memory (a Deal Brain per deal or workflow) so every answer and every proposed update carries its source.
  • Ships reviewable outputs inside existing tools: briefs, CRM updates with read-back receipts, digests, and approval queues, deployed in the firm's own cloud tenant.

// how it works

01

Start with one workflow that already costs the team time: CRM cleanup and enrichment, pre-call briefs, meeting capture, status updates, or relationship memory.

02

Connect approved sources (Outlook, Teams, the CRM, SharePoint, data rooms), keeping the tools the firm already owns.

03

Launch the first cited workflow, measure the result against a baseline, and expand into adjacent use cases.

// works with

OutlookTeamsMicrosoft GraphSalesforceDealCloudAffinitySharePointOneDrivePitchBookVDRs

Frequently asked questions

What is the best way to use AI in finance?

Start where people repeatedly gather context from many systems to produce an update, brief, or record: CRM upkeep, pre-call preparation, meeting capture, status reporting. Those workflows have visible value and clear trust requirements: the output must cite its source and a human must approve the write. Arvya starts there because the firm's own email, calls, and documents already contain the raw material.

How is Arvya different from a generic AI assistant for finance?

A generic assistant answers from whatever it can retrieve, in a window disconnected from the work. Arvya maintains a verified memory built from the firm's real sources, respects its permissions and approval paths, and closes the loop: staged updates a human approves, written to the CRM the firm already owns, read back as a receipt. The output is a trustworthy record, not just an answer.

One live mandate

See the transaction become a live operating system.