Introduction

Lantern is now available as an MCP server. You can now connect to Lantern via the AI tools you use everyday.

The model is interchangeable. The trust layer is not.

Every GP is looking at AI right now. Most are running internal experiments. The question every team is facing is the same one.

It is not whether the model is good. They all are.

It is what the model is reading.

The problem with most AI in private markets

Most teams trying AI in private markets point a model at a file share, SharePoint or a data feed. The output is articulate. The data underneath is a mess.

The same company name is spelt three different ways across three different admins. NAV is one figure in your data warehouse, another in the LP letter. Capital accounts have not been reconciled. There is no signal for when a number is wrong.

An agent reading from that data will still answer confidently. It will quote stale MOICs. It will reconcile figures that have not actually been reconciled. It will produce a sentence or paragraph where everything sounds right, but the right answer is really “this number is broken, and here is why.”

That is a data trust problem, and does not get solved by using a different model.

The trust layer

Lantern’s MCP delivers validated data, not raw data. Before an agent ever queries it, the data has been:

  • Ingested and normalised across all of the systems that you use, such as eFront, Investran, Allvue, iLevel, Chronograph, and of course, Excel.
  • Validated through 2 million daily tests across investor, fund and asset level data.
  • Resolved at the entity level, so the same company spelt three ways across three admins becomes a single record.
  • Traceable to source. Any figure can be traced back to the document it came from, with a full audit trail across the GP and fund admin workflow.

Three things follow.

Answers you can trust, because the layer underneath is governed. The data has been reconciled. The model is no longer guessing. It is reporting.

When the data is wrong, you get the root cause. Ask why Fund III’s TVPI doesn’t match across two sources, and the agent traces it back to a misbooked equity transaction in a single portfolio company, because Lantern has already mapped how metrics interrelate across company, fund, and investor levels. No hallucinated explanation. The actual root cause.

Output is workflow-ready. LP letters open in Word. Portfolio analysis opens as a deck. Every number traceable back through Lantern’s data quality layer. Not raw output. Drafts ready to use.

What this means for your week

Before a board meeting. Ask for the latest on a portfolio company before you walk into the room. Operational metrics, investment commentary, and visualisations — sourced and traceable.

When you need the fund picture. Pull Fund III’s current TVPI, DPI, and NAV alongside quarterly trends. See how individual company performance is driving fund-level movement — and where data quality issues might be distorting the numbers.

When a number doesn’t look right. Ask why, and the agent traces it back through company, fund, and investor levels to the root cause — so your team can fix it at source, not paper over it downstream.

When an LP asks a question. Ask the agent and get a sourced answer in minutes — investor commitments, fund performance, distribution history. No chasing ops, no digging through documents. The data is already validated and ready.

When the quarterly letter is due. Pull current performance across the portfolio. Highlight the top and bottom movers. Pre-fill investment commentary. Open it in Word, ready to review and send.

The model is interchangeable. The layer underneath is not.

Claude today. ChatGPT, Gemini or Perplexity tomorrow. A model your team has not heard of yet, the year after. The agent layer will keep moving.

The data underneath does not. Whichever model your team is using next year, it is only useful in private markets if the data it reads has been ingested, validated, resolved, and traceable to source.

That is the layer Lantern builds. Models come and go. Trusted data is the product.

Built for the way private markets actually run

Lantern does not replace your TPA. It does not replace your portfolio monitoring or fund monitoring platforms. It works alongside the systems you already have, pushing validated data out through Perform, Excel, Snowflake, API, SFTP, and now MCP.

Everyone is racing to use AI effectively. Lantern’s MCP is what makes that possible in private markets – trusted data the agent can actually act on.

Get connected

Lantern is available in any MCP client today. To set it up for your team, book a demo or see the platform

Trust your data. Automatically. Wherever your team is working.