We're introducing the Finsight Client Opportunity Assistant, the first solution in Finsight's new Palantir Accelerators offering.

Built on Palantir Foundry and AIP, the accelerator helps capital-markets teams identify and explain actionable client opportunities by connecting information that is often spread across multiple systems.

It brings together client context, portfolios and positions, market observations, firm inventory, product eligibility, preferences and restrictions, then applies governed business logic to identify opportunities such as reinvestment, hedging, concentration, liquidity, inventory match, new issues, relative value and cross-sell.

A salesperson can ask:

“Why should I speak with Phoenix today?”

The assistant resolves Phoenix to the appropriate client, evaluates the current opportunities and explains the supporting evidence.

It can also work across the broader client population:

“List the top 10 opportunities across all active clients today.”

The assistant can identify active clients, run opportunity discovery across them and produce a prioritised cross-client summary.

Much of the thinking behind Finsight AI-Foundry did not start as a product. It started as a long-running research and engineering series — still ongoing, with new posts published regularly — written in public by our founder, Linxiao Ma, on Data Ninjago, under the category AI-Native Financial Data Foundation. Thirty posts and counting.

If you want to understand why AI-Foundry is designed the way it is — not just what it does — that series is the place to look, and the place to keep looking as the architecture evolves.

Building a real-time quantitative trading dashboard is traditionally a multi-week engineering effort — data pipelines, computation engines, streaming infrastructure, and visualization all need to be wired together. With QuantFlow, it takes about an hour.

Before I start talking about how effective this architecture can be at reducing infrastructure costs, I should first make the old point that there is really no free lunch. Compared with commercial cloud data platforms and warehouses such as Databricks, BigQuery, and Snowflake, an open lakehouse setup requires significantly more engineering effort to build, operate, and tune properly. You trade managed convenience for lower-level control, flexibility, and potentially much lower long-term costs.

We're excited to introduce the new Finsight Technologies website — reflecting who we are today: a specialist financial data, AI and governance company.

Many of you first found us through QuantFlow, our quantitative data and research platform. QuantFlow is still here — but it is now one part of a broader mission: helping financial institutions make their data understandable, trusted and governed for reliable AI use.

Why a New Website?​

Our work has expanded from quantitative data infrastructure into a bigger question facing every financial institution: existing data foundations were not designed for AI.

Schemas describe how data is stored, not what it means. Important data is fragmented and hard to discover. Outputs cannot be traced to the data, rules and evidence behind them. And promising AI demonstrations struggle to become governed, production capabilities.

The new website is built around how we address this.