Software development and AI pod teams for asset management

Ryz provides asset and wealth managers with senior Latin American engineers and dedicated AI pod teams that build investment data platforms, portfolio and client reporting, distribution tools and compliance review systems. Our pods have shipped AI marketing compliance review for a global capital management firm. Our engineers work on US business hours, including New York hours, alongside your investment operations, technology and compliance teams.

What we build for asset management teams

Asset managers run on data from many sources: custodians, administrators, market data vendors, the OMS, the CRM. Most engineering work is about making that data consistent, timely and explainable. Our teams build:

• Investment data platforms. Security master, positions, transactions and pricing in a single model, with IBOR and ABOR views that reconcile and lineage back to each source file.
• OMS and EMS integrations. Order flows into and out of Charles River, Bloomberg AIM or SS&C Eze, pre-trade compliance hooks and post-trade allocation feeds.
• Performance and attribution. Return calculation pipelines, composite maintenance and attribution reporting that performance teams can check against their own numbers.
• Client and investor reporting. Fact sheets, quarterly letters, capital account statements for private funds and portals for institutional clients and LPs.
• Distribution and CRM tooling. Salesforce and Dynamics integrations, RFP and due diligence questionnaire libraries, and the data feeds that power wholesaler and consultant relationships.
• Wealth management platforms. Advisor workstations, model portfolio rebalancing, proposal generation and integrations with Addepar, Orion, Envestnet or Black Diamond.
• Private markets operations. Capital call and distribution workflows, deal pipeline tracking and document extraction from fund financials and investor notices.

Where AI pods help in asset management

Asset managers sit on large volumes of documents and text: research, filings, client letters, prospectuses, questionnaires. An AI pod builds the retrieval, extraction and review systems around them, in your cloud, and runs them in production.

• Marketing compliance review. Checking fact sheets, presentations and commentary against the SEC Marketing Rule, FINRA Rule 2210 and firm policy, flagging performance presentation issues, missing disclosures and unsubstantiated claims. One of our pods built this for a global capital management firm: more than 8,000 documents reviewed, with review time cut from days to hours. See our case studies.
• RFP and DDQ drafting. Drafting answers from approved prior responses and policy documents, with citations so the investor relations team can verify each answer. The challenge is keeping the answer library current and stopping stale numbers from reappearing.
• Research retrieval. Search across internal research notes, earnings call transcripts and filings, with entitlement checks so information barriers between teams hold.
• Document extraction for private markets. Pulling figures from capital account statements, quarterly reports and K-1s into structured data, with exceptions routed to operations.
• Operations exception handling. Summarizing reconciliation breaks and trade exceptions and proposing the likely cause, so operations analysts clear queues faster.

Regulations and constraints our engineers work within

Our engineers have experience building within these rules, working with your chief compliance officer's team. Ryz does not certify systems and does not provide legal or compliance advice.

• SEC Marketing Rule, Rule 206(4)-1. Requirements for testimonials, endorsements, hypothetical performance and net-of-fee presentation in adviser advertisements.
• Books and records, Rule 204-2. Retention of advertisements, communications and supporting records for registered investment advisers, plus SEC Rule 17a-4 for affiliated broker-dealers.
• FINRA Rule 2210. Communications standards and filing requirements for fund distributors and broker-dealers.
• Regulation S-P amendments. Incident response programs and customer notification for unauthorized access to customer information, with compliance dates in December 2025 and June 2026 depending on firm size.
• Form PF and Form N-PORT. Regulatory reporting that depends on accurate position and exposure data.
• GIPS standards. Voluntary CFA Institute performance standards many firms claim compliance with, which affect how composites and returns are calculated and presented.
• EU and UK rules. UCITS, AIFMD, SFDR disclosures and GDPR for managers with European funds or clients.

Integrations and data

• Custodian and fund administrator files (SWIFT MT535 and MT950 style statements, CSV and proprietary formats), and the daily reconciliations against them.
• Market and reference data from Bloomberg, FactSet, LSEG and MSCI, including entitlement and licensing limits on redistribution.
• Portfolio platforms such as BlackRock Aladdin, SimCorp and SS&C, and FIX for order flow.
• Wealth platforms including Addepar, Orion, Envestnet and custodial APIs from Schwab and Fidelity.
• Data warehouses on Snowflake or Databricks, with dbt models for positions, performance and client data. See our data engineering work.

The recurring failure mode is timing. Custodian files land late, pricing vendors restate, and the same corporate action arrives in different forms from different sources. Our engineers build pipelines that load partial data, flag what is stale and rerun cleanly when a corrected file arrives, so the morning positions report says which numbers are final. They also respect market data licensing: what a vendor lets you display internally often differs from what you can put in a client report or send to a model.

How teams engage Ryz

Staff augmentation fits when your technology team owns the roadmap and needs more senior capacity: a Python data engineer for the investment data platform, a full-stack developer for the client portal, a Salesforce developer for distribution. Our asset management software developers page covers individual roles.

An AI pod fits when you want a specific system such as marketing compliance review or DDQ drafting built and running. A typical pod is around seven senior engineers including a tech lead, an ML engineer and backend engineers, with weekly demos in your environment. A dedicated development team fits a scoped non-AI build such as a new reporting platform.

Mid-level engineers run $7,000 to $10,000 per month, senior engineers $10,000 to $15,000 and leads $15,000 and up. A pod of six senior engineers and one lead for five months, at $12,000 and $16,000, is $440,000. Every quote is scoped per team, with names attached.

When Ryz isn't the right fit

• You want an investment operations outsourcing provider or a licensed portfolio platform. Ryz builds and runs systems with your team; it does not sell software or run middle office operations.
• You need a strategy engagement on operating model design. A management consultancy fits that better.
• You need coverage in London or Asia hours, or follow-the-sun support. A global network is a better match.

Related

• Best AI engineering teams for asset managers
• Financial services software development
• RAG development
• Forward-deployed engineers
• Fintech software development

FAQ

Can an AI pod build marketing compliance review on our own policies?

Yes. This is one of the systems our pods have built in production. The pod encodes your written policies and the rules your compliance team applies, builds evaluation sets from documents your reviewers already marked up, and keeps a reviewer in the loop on every approval.

How do your engineers work with confidential investment and client data?

They work in your environment and under your controls: your cloud, your SSO and device policies, your information barrier rules and your data access approvals. Your compliance team decides which data each engineer can reach. Ryz does not hold certifications on your behalf.

Do your engineers understand investment data?

Our engineers have worked with security masters, positions, transactions, corporate actions and performance data, and with the reconciliation problems that come with custodian and administrator feeds.

What does an asset management engineering team cost?

Typical rates are $7,000 to $15,000 per engineer per month, depending on seniority, with leads at $15,000 and up. Project cost is team size times duration times rate, and you receive a plan, a price and names before starting.

Can your engineers support market-hours operations?

Yes. They work on US business hours, including New York hours, so they can respond to pre-open data issues and end-of-day reporting the same day.

Questions we didn't answer? Email info@ryzlabs.com.