The WealthTech stack is the collection of integrated software systems that advisory firms use to manage client relationships, construct and manage portfolios, connect to custodians, deliver financial plans, automate operations, and generate insights. For most firms, this stack has grown organically over years — each tool solving one problem — creating a fragmented ecosystem that struggles to share data across boundaries.
A firm running $1 billion in AUM might operate across a dozen software platforms simultaneously. Their CRM holds relationship history and pipeline data. Their portfolio management system tracks positions, performance, and rebalancing rules. Their custodian portals push account-level transactions. Their financial planning tool stores goal projections and recommendations. Each system operates in its own data model, with its own login, its own export format, and its own definition of what a "client" or "account" means.
The result is a firm that is simultaneously data-rich and insight-poor. The data exists — it is just scattered across seven systems that do not speak to each other. Understanding this structure is the first step toward fixing it.
The 7 Layers of a Modern WealthTech Stack
Every advisory firm's technology stack can be mapped to seven functional layers. Some firms have tools in every layer; others have gaps. Understanding which layer each tool belongs to — and what data it holds — is the foundation for diagnosing integration problems and building a more connected architecture.
Common Stack Configurations by Firm Size
No two advisory firms run the same stack, but patterns emerge strongly by size. Firm size determines vendor leverage, technical capacity, and how much of the stack the firm can build versus buy. Here is how the stack typically looks at each tier.
| Firm Tier | CRM | Portfolio | Planning | Data Layer |
|---|---|---|---|---|
| Solo / Small RIA$100M–$500M AUM | Redtail or Wealthbox | Orion or Schwab Advisor Center | MoneyGuidePro | Excel + manual exports |
| Mid-Size RIA$500M–$5B AUM | Salesforce or Redtail | Tamarac or Black Diamond | eMoney or MoneyGuidePro | Partial — some BI tooling, limited unification |
| Large RIA / Enterprise$5B+ AUM | Salesforce Financial Services Cloud | Orion or Black Diamond | eMoney + custom modeling | Full data warehouse (Snowflake) + Milemarker |
The pattern is consistent: as firms grow, they add more specialized tools in each layer but often do not add the infrastructure to connect them. A mid-size RIA running Salesforce, Tamarac, Fidelity, and eMoney has four sophisticated systems generating rich data — and no reliable way to view that data together without a full-time data analyst exporting spreadsheets.
The gap between the large enterprise tier and everyone below it is not primarily a tool gap — it is a data infrastructure gap. Enterprise firms have built or purchased the Layer 6 foundation that makes all their other tools exponentially more useful.
Where the Stack Breaks Down
The Integration Gap
Every tool in the WealthTech stack was built to solve a specific problem in isolation. CRMs were designed by relationship management teams. Portfolio systems were designed by investment operations teams. Custodian portals were designed by custody operations teams. None were designed with the assumption that their data would need to flow into a dozen other systems using a shared schema.
The result is a grid of point-to-point integrations — most of them partial, fragile, and asynchronous. The CRM might sync client names and phone numbers from the portfolio system, but not account-level performance. The portfolio system might pull transaction data from the custodian, but not household-level financial planning goals. Every boundary between systems is a place where data either stops flowing or requires a human to reconcile it.
Manual Reconciliation as the Default
When systems do not share data natively, the default integration becomes a person. Operations teams spend hours each week exporting data from one system, reformatting it in Excel, and importing it into another. Discrepancies surface when the same client appears under a different name in the CRM versus the custodian feed. Account numbers do not match. Positions are stale by 24 hours. The "integration" is a spreadsheet that someone owns and that will break when they leave.
The core problem: Each tool has its own data model and no native connection to adjacent systems. The CRM does not know what a "position" is. The portfolio system does not know what a "meeting" is. Without a shared data layer, these systems will never speak to each other reliably.
Why the Data Layer is the Missing Piece
The missing piece is not another point-to-point integration. It is a layer that sits beneath all the tools and ingests their data into a common, normalized warehouse. When Layer 6 exists, every other layer becomes more powerful — because the data from all layers can finally be combined, compared, and analyzed together.
Without it, adding a new custodian means building a new set of manual reconciliation workflows. With it, adding a new custodian means adding one new integration to the data layer, and every downstream report updates automatically.
Layer 6: The Data Foundation That Connects Everything
The data layer is not another application in your stack. It is the infrastructure beneath your stack. Where every other tool is purpose-built for a specific function, the data layer is purpose-built for one thing: ingesting data from everything else and making it available in a unified, queryable form.
How it Works
A data platform like Milemarker connects to every system in your stack through pre-built integrations — CRM, portfolio management, custodians, planning tools, compliance systems. It pulls data on a continuous or daily basis, normalizes it against a standard financial data model, and loads it into a cloud data warehouse such as Snowflake. From that warehouse, your team can run firm-wide reports, build dashboards, train AI models, and answer operational questions without touching the source systems.
- CRM data — client profiles, household structure, interaction history, pipeline
- Portfolio data — positions, performance, transactions, model drift, fees
- Custodian data — account balances, settlements, corporate actions, cash
- Planning data — goals, projections, probability scores, plan assumptions
- Compliance data — suitability records, audit trails, regulatory filings
All of this data — normalized, deduplicated, and joined on shared keys — becomes available for analytics, reporting, and AI. A question like "Which clients in the 55-65 age segment have more than 40% equity concentration and no financial plan on file?" goes from a multi-day manual project to a SQL query that returns in seconds.
Where Milemarker Fits
Milemarker is built specifically for this layer. It does not replace your CRM, your portfolio management system, or your planning tool — it connects all of them. With 130+ pre-built integrations covering the major tools at every layer of the stack, Milemarker delivers a production-ready unified data warehouse without custom development. Most firms are live within weeks, not months.
The result is a firm where operations teams stop spending hours reconciling spreadsheets, leadership can answer firm-wide questions in real time, and the AI layer finally has the clean, complete data it needs to produce reliable outputs.
Evaluating Your Stack: 5 Questions
Most firms know their stack is fragmented but struggle to diagnose exactly where the leverage points are. These five questions surface the structural gaps quickly.
Can you see a complete client picture in one place?
If answering a client question requires opening three different systems, you do not have a unified view. The data exists — it is just in the wrong architecture. A complete client picture means relationship history, portfolio performance, financial plan status, and compliance standing in a single query.
How long does it take to answer a firm-wide question?
If "how much AUM are we at risk of losing from clients over 75 with no named successor?" takes days of manual work, your data layer is absent or incomplete. In a properly architected stack, that question is a query — not a project.
What breaks when you add a new custodian or CRM?
If adding a new custodian means rebuilding reporting workflows from scratch, your integrations are point-to-point and brittle. A data layer absorbs new sources as additional inputs, leaving every downstream report untouched. If your answer is "a lot breaks," you are operating without Layer 6.
Can your team build reports without engineering help?
Self-service analytics — advisors and operations staff building their own reports without writing code or waiting for IT — is only possible when data is normalized and accessible through a BI tool connected to a unified warehouse. If every report request goes through a bottleneck, the data layer is missing.
Is your data AI-ready?
AI models require clean, consistent, complete data. If your data lives in seven siloed systems with inconsistent naming conventions and 30-day-old snapshots, no AI layer will produce reliable results on top of it. AI-readiness is not an AI problem — it is a data infrastructure problem. The answer starts at Layer 6.
