Phinite for Financial Services

AI agents for financial services

Your supervisors rarely ask whether the number is right. They ask where it came from, who checked it, and what the desk read to get there. Phinite records all three before the work leaves the desk.

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Challenges

Four pressures reshaping the front and middle office

Margin, deadlines, and supervision all press on the same middle office. Phinite puts agents on that work without loosening a single control.

Fee compression squeezes a model that never shrinks

Margin falls every year while the operating model that produces it stays the same size. Adding headcount to the middle office is the one lever that scales with volume, and it is the lever finance directors keep asking you not to pull. Phinite puts agents on the preparation work so the model stops growing with the book.

A break you once worked overnight now clears inside the day

Shorter settlement windows turned an overnight task into an intraday one, and the queue does not care what time the confirmation arrived. Phinite agents pick up breaks as they appear, gather both sides of the record, and put a proposed fix in front of operations before the cut-off.

Every client-facing page needs a reviewer

Reports, pitches, and messages all need supervision, and the volume grows faster than the review team. Phinite tests client-facing material against your policy library, returns exceptions with the failing line marked, and holds anything client-facing until a named supervisor releases it.

Your truth is whichever reconciliation ran last

Positions live with a custodian, books with an administrator, and the answer depends on which feed you asked. Phinite connects each as a typed tool with scoped credentials, so an agent reads only what the mandate entitles it to, and every read is logged against the work it fed.

Fee compression squeezes a model that never shrinks

A break you once worked overnight now clears inside the day

Every client-facing page needs a reviewer

Your truth is whichever reconciliation ran last

What are AI agents for financial services?

`Phinite is the Operating System for Multi-Agent AI, used across asset management, wealth management, brokerage, and private markets to run research support, trade lifecycle operations, client reporting, and supervision as governed agent systems, with every action evidenced against the mandate it serves.`

Functions

Six parts of the investment lifecycle agents already prepare

Phinite runs research support, trade operations, client reporting, and supervision inside one governed system.

Investment research support
Read filings, transcripts, and broker notes, and assemble the pack an analyst would otherwise build by hand.
Investor and mandate onboarding
Gather subscription documents, confirm investor status, and send anything ambiguous to a named reviewer.
Trade lifecycle operations
Match confirmations, chase breaks, and prepare a fail for someone to clear before the cut-off.
Client reporting and reviews
Build performance, holdings, and commentary into the pack your client expects, in your house format.
Adviser and relationship support
Prepare the briefing, the history, and the open items before an adviser sits down.
Regulatory reporting and surveillance
Draft returns and surveillance summaries from source data, and mark what a supervisor needs to sign.

Investment research support

Read filings, transcripts, and broker notes, and assemble the pack an analyst would otherwise build by hand.

Illustration for Order support and WISMO

Investor and mandate onboarding

Gather subscription documents, confirm investor status, and send anything ambiguous to a named reviewer.

Illustration for Returns, refunds, and exchanges

Trade lifecycle operations

Match confirmations, chase breaks, and prepare a fail for someone to clear before the cut-off.

Illustration for Conversational shopping and discovery

Client reporting and reviews

Build performance, holdings, and commentary into the pack your client expects, in your house format.

Illustration for Cart recovery and lifecycle messaging

Adviser and relationship support

Prepare the briefing, the history, and the open items before an adviser sits down.

Illustration for Catalog and content operations

Regulatory reporting and surveillance

Draft returns and surveillance summaries from source data, and mark what a supervisor needs to sign.

Illustration for Fraud, chargebacks, and fulfillment exceptions

Enterprise platform

Built for secure, enterprise AI at scale

Deploy AI agents with enterprise-grade security, governance, observability, and infrastructure designed for production from day one.

Multi-Tenant Workspace Isolation

Every organization operates in its own isolated workspace with dedicated knowledge, permissions, and execution environments.

Enterprise Deployment

Deploy across development, staging, and production environments with controlled releases and predictable scaling.

Governance & Auditability

Track every agent action with role-based access, approval workflows, and comprehensive audit logs.

Observability & Guardrails

Monitor agent performance in real time while enforcing security policies, human oversight, and operational guardrails.

Usecases

Three processes that have to clear a deadline

Put agents on the middle-office work that carries a hard cut-off, where a late answer costs as much as a wrong one.

Settlement Break Resolution

Research Pack Preparation

Quarterly Client Reporting

Settlement break resolution

Pick up the break, gather both sides of the record, and hand operations a proposed fix inside the settlement window.

Both Sides of the Record

Proposed Fix

Inside the Window

Research pack preparation

Turn filings, transcripts, and internal notes into a sourced briefing, with every assertion traceable to the document behind it.

Sourced to Document

Filings and Transcripts

Analyst-Reserved Judgment

Quarterly client reporting

Compile performance, attribution, and commentary per account, and route the pack for supervisory release.

Performance and Attribution

House Format

Supervisory Release

Deployment path

How to deploy AI agents for financial services

Start with one middle-office process, then extend the same governed pattern across research, reporting, and supervision.

Choose the deadline

Begin with a middle-office process that has a hard cut-off and a clean source of record.

Model the desk

Lay the process out in Agent Graph Studio, and connect your OMS, custodian feeds, and document stores as typed tools.

Backtest a closed period

Run the agent in Evaluation over a period you have already closed, and compare its output against what your team produced.

Go live under supervision

Switch on with release approvals and retention already in place, then extend the same pattern to the next process.

Integrations

Connect your investment technology stack

Order management, portfolio accounting, custodian and administrator feeds, CRM, and document stores arrive as typed tools. An agent reads only what the mandate it is working entitles it to read.