A complete guide to the technology layers that power today's advisory firms — and where they break down.

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.

Layer 01

CRM — Client Relationship Management

Salesforce Financial Services Cloud · Redtail · Wealthbox

The CRM is the system of record for every client relationship. It stores contact data, household structure, interaction history, meeting notes, tasks, pipeline stages, and referral sources. The CRM defines who your clients are and what has happened in your relationship with them. Data it generates: client profiles, interaction logs, pipeline records, segmentation tags, household rollups, and referral attribution.

Layer 02

Portfolio Management System

Orion · Black Diamond · Tamarac

The portfolio management system is where investment data lives. It tracks positions, performance, rebalancing rules, model portfolios, trading history, and fee billing. Orion (orion.com) operates as a comprehensive platform spanning portfolio management, trading, performance reporting, and client portal functionality. Data it generates: holdings, transactions, performance returns, realized/unrealized gains, fee calculations, model drift, and trade history.

Layer 03

Custodian Connections

Charles Schwab · Fidelity · Pershing

Custodians hold the actual assets. Their data feeds — delivered via daily files, APIs, or direct feeds — provide account-level balances, positions, transactions, and cash flows. Most RIAs connect to multiple custodians, meaning this layer generates multiple parallel data streams that must be reconciled into a unified view. Data it generates: account balances, position detail, settlement records, corporate actions, margin data, and cash sweeps.

Layer 04

Financial Planning

eMoney · MoneyGuidePro · RightCapital

Financial planning tools model the future. They store client goals, income and expense assumptions, tax projections, insurance coverage, estate planning scenarios, and retirement readiness scores. This layer generates the forward-looking context that gives portfolio data meaning. Data it generates: goal progress, probability of success scores, cash flow projections, tax estimates, insurance gap analysis, and plan assumptions.

Layer 05

Compliance & Risk

Compliance Systems · Surveillance Tools · Risk Platforms

Compliance systems enforce suitability rules, flag surveillance alerts, manage regulatory filings, and maintain audit trails. Risk platforms overlay factor models, stress tests, and concentration limits against the portfolio layer. This layer is often underinvested relative to its operational importance. Data it generates: suitability records, surveillance alerts, regulatory filing history, KYC/AML records, and investment policy statement compliance tracking.

Layer 06

Data Infrastructure

Milemarker · Snowflake · Data Warehouse

The data layer is the connective tissue of the entire stack. It ingests data from every other layer, normalizes it into a common schema, and stores it in a structured data warehouse. Unlike every layer above it, the data layer does not replace any tool — it connects all of them. This is the layer most firms are missing, and its absence is the root cause of nearly every integration problem. Data it enables: unified client views, cross-system reporting, firm-wide analytics, and AI-ready datasets.

Layer 07

AI & Intelligence

Navigator · AI Agents · Natural Language Query

The AI layer sits at the top of the stack and depends entirely on the quality of data from the layers below it. With clean, unified data from Layer 6, AI can surface next-best-action recommendations, flag at-risk clients, generate meeting summaries, automate reporting commentary, and enable natural language queries across the firm's data. Without Layer 6, AI produces unreliable outputs. Data it consumes: everything from Layers 1–5, normalized and unified by Layer 6.

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.

Solo / Small RIA $100M–$500M AUM

• CRM: Redtail or Wealthbox

• Portfolio: Orion or Schwab Advisor Center

• Planning: MoneyGuidePro

• Data Layer: Excel + manual exports

Mid-Size RIA $500M–$5B AUM

• CRM: Salesforce or Redtail

• Portfolio: Tamarac or Black Diamond

• Planning: eMoney or MoneyGuidePro

• Data Layer: Partial — some BI tooling, limited unification

Large RIA / Enterprise $5B+ AUM

• CRM: Salesforce Financial Services Cloud

• Portfolio: Orion or Black Diamond

• Planning: eMoney + custom modeling

• Data Layer: 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.

Q 01

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.

Q 02

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.

Q 03

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.

Q 04

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.

Q 05

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.

Frequently Asked Questions

What is a WealthTech stack?

A WealthTech stack is the collection of integrated software systems that an advisory firm uses to run its business — CRM, portfolio management, custodian connections, financial planning, compliance, data infrastructure, and AI tools. Most firms have built their stack organically over years, adding one tool at a time to solve specific problems, resulting in a fragmented ecosystem where data rarely flows cleanly between layers.

What tools do most RIAs use?

Most RIAs use a CRM (Salesforce, Redtail, or Wealthbox), a portfolio management system (Orion, Black Diamond, or Tamarac), at least one custodian platform (Schwab, Fidelity, or Pershing), and a financial planning tool (eMoney, MoneyGuidePro, or RightCapital). Larger firms add compliance systems, data warehouses, and AI tooling. The specific combination varies significantly by firm size and growth trajectory.

How do I integrate my tech stack?

Most point-to-point integrations between WealthTech tools are limited, fragile, and require manual reconciliation when they break. The more durable approach is to implement a data layer — a platform that ingests data from every tool in your stack, normalizes it into a common schema, and exposes it for analytics and downstream use. This is how Milemarker approaches integration: not replacing tools, but connecting all of them through a single data foundation.

What is the data layer in a WealthTech stack?

The data layer (Layer 6) sits underneath all other tools in the WealthTech stack. It ingests data from your CRM, portfolio system, custodians, and planning tools, normalizes it into a unified schema, and stores it in a data warehouse (such as Snowflake). From there, your team can run firm-wide reports, build dashboards, train AI models, and answer operational questions without switching between systems. Without this layer, each tool remains a silo.

How much does a full tech stack cost?

A full WealthTech stack for a mid-size RIA ($500M–$5B AUM) typically runs $150,000–$500,000 per year in combined software fees, covering CRM, portfolio management, custodian connectivity, financial planning, compliance, and data infrastructure. Enterprise firms ($5B+) often exceed $1M annually. Costs vary widely by vendor, seat count, and the number of integrations required. The hidden cost is operational overhead from manual reconciliation when tools do not share data cleanly.

Can I replace tools without disrupting everything?

Yes, but only if your data is not locked inside the tool you are replacing. Firms with a dedicated data layer can swap out a CRM or portfolio system without losing historical data or disrupting reporting, because the data layer has already captured and normalized the records. Firms without a data layer often find that replacing a single tool requires months of data migration work and breaks dozens of downstream reports.

What is the role of AI in the WealthTech stack?

AI sits at Layer 7 of the WealthTech stack and depends entirely on the quality of data from the layers below it. With clean, unified data from the data layer, AI can surface next-best-action recommendations, flag at-risk clients, generate personalized insights, automate meeting summaries, and power natural language querying across the firm's data. Without unified data, AI models produce unreliable outputs because they are trained or operating on incomplete, inconsistent records.

How do I evaluate my current stack?

Five questions surface most stack weaknesses: (1) Can you see a complete client picture in one place? (2) How long does it take to answer a firm-wide question? (3) What breaks when you add a new custodian or CRM? (4) Can your team build reports without engineering help? (5) Is your data AI-ready? If any of these expose a gap, the root cause is almost always the absence of a unified data layer connecting the tools you already use.

Related guides

Part of the Wealthtech Guides series:

• How to Build an RIA Technology Stack in 2026: A Complete Guide

• Best WealthTech Platforms in 2026: A Category-by-Category Guide

• How to Choose a WealthTech Platform: A Buyer's Guide for Advisory Firms

• Data Infrastructure for Breakaway Advisors: Start Your RIA With the Right Foundation

• The 2026 WealthTech Software Guide: 30+ Tools Every Modern RIA Should Evaluate

• The Real Cost of Disconnected WealthTech (And How to Fix It)