How financial advisors can use AI with client data

Your AI assistant can access a client's financial data — with their explicit consent, scoped to specific accounts, and with every interaction logged. Era Context makes this possible through the Model Context Protocol (MCP), giving advisors a structured, auditable way to bring AI into their practice.

Era Financial Advisors LLC is SEC-registered (CRD #334404). This article describes how the technology works, not legal or compliance advice. Consult your compliance team before adopting any new tooling.

The problem with advisor prep today

Before a client meeting, most advisors spend 15 to 30 minutes pulling up account summaries, scanning recent transactions, and trying to spot anything worth discussing. You log into a custodian portal, export a CSV, open a spreadsheet, and do the same thing you did last quarter.

AI should be able to do this for you. The barrier has always been access — how do you give an AI assistant structured access to a client's financial data without handing over credentials or violating privacy expectations?

How shared views work

Era Context lets any user create a shared view — a read-only window into specific accounts. A client who uses Era can grant their adviser access to selected accounts. The adviser sees only what the client has chosen to share. Nothing more.

Shared views are:

• Read-only. The adviser can query data but cannot modify transactions, create rules, or move money.
• Scoped. The client selects which accounts to include. A client with five accounts can share two.
• Revocable. The client can remove access at any time, from any device.
• Audited. Every query the adviser's AI agent makes against the shared view is logged in the activity trail.

This is not a workaround or a screen-share. It is a first-class feature designed for exactly this use case.

What an adviser can ask their AI

Once a shared view is connected, the adviser can use any MCP-compatible AI client — Claude, ChatGPT, OpenClaw, or any other — to query the client's data in natural language. Here are some practical examples.

Pre-meeting prep

• "Summarise this client's financial activity for the last 90 days"
• "What are the largest transactions this quarter?"
• "Show me their recurring charges sorted by amount"
• "Has their spending in any category increased by more than 20% compared to last quarter?"

Cash flow review

• "What is their average monthly cash flow over the last six months?"
• "Are there any months where outflows exceeded inflows?"
• "What does their spending forecast look like for the next 30 days?"

Subscription and recurring charge audit

• "List all recurring charges across their shared accounts"
• "Have any subscriptions changed price recently?"
• "What is the total monthly cost of all recurring charges?"

Spending patterns

• "What are their top five spending categories this year?"
• "Compare their dining and entertainment spending this month versus last month"
• "Flag any transactions over $500 in the last 60 days"

Each of these queries runs through Era Context's 33 MCP tools. The AI client sends the request, Era Context processes it against the shared view, and the response comes back — all within the conversation.

The audit trail

Every interaction between an AI agent and Era Context is logged. The activity log records what was asked, when it was asked, and which accounts were queried. This is not a summary or a digest. It is a line-by-line record of every tool call.

For advisers operating under regulatory oversight, this matters. You have a record of exactly what data your AI accessed and when. The client has the same visibility.

Cross-agent memory for ongoing relationships

Era Context includes cross-agent memory. If an adviser tells Claude "this client's goal is to pay off their car loan by March," that context persists. The next time the adviser opens ChatGPT and asks about the same client, that goal is already known.

This eliminates the repetitive context-setting that makes AI assistants frustrating for ongoing relationships. Your AI remembers the client's goals, preferences, and financial context — across conversations and across AI clients.

Memory is private to the adviser's account. It is never shared with other users and never used to train models.

Setting it up

The setup is straightforward:

1. The client connects their bank accounts to Era Context through MX, which supports thousands of financial institutions.
2. The client creates a shared view and selects which accounts to include.
3. The client shares the view with their adviser.
4. The adviser connects their AI client to Era Context at https://context.era.app.
5. The adviser can now query the shared view in natural language.

The adviser does not need access to the client's login. The adviser does not see accounts the client has not shared. The client stays in control.

What this is not

This is not a replacement for your custodian, your financial planning software, or your compliance system. Era Context gives your AI assistant structured access to client financial data. It is one tool in your practice — the tool that makes your AI actually useful for the work you do every day.

It is also not a robo-adviser. There is no automated investment advice, no portfolio rebalancing, no trade execution through shared views. This is data access for human advisers who want to use AI to work faster.

Pricing for adviser use cases

Era Context offers four tiers. For adviser workflows involving shared views:

• Basic (free) supports 2 accounts and 1 view — suitable for evaluating the product.
• Organize ($14.99/month) supports 15 accounts and 5 views with full read-write MCP access.
• Automate ($29.99/month) adds money transfers and rule-triggered automation with 2 shared views.
• Optimize ($49.99/month) includes unlimited shared views — designed for advisers managing multiple client relationships.

Each client would maintain their own Era account and choose their own tier based on the features they need.

Getting started

If you are an RIA or independent adviser exploring how AI can fit into your practice, Era Context is worth evaluating. Connect your own accounts first, try the prompts above, and see what your AI can do with structured financial data. When you are ready to bring clients in, shared views make it possible without compromising privacy or auditability.

Era works with any MCP-compatible client — Claude, ChatGPT, OpenClaw, and dozens more. You are not locked into a single AI provider, and neither are your clients.