Software development and AI pod teams for financial services

Ryz staffs senior Latin American engineers and dedicated AI pod teams for financial institutions: capital markets firms, lenders, wealth managers and other regulated firms. Our teams modernize trading, lending and client platforms, build the data pipelines regulators and risk teams depend on, and ship AI systems to production in your own cloud. They work on US business hours, including New York hours, so code review and incident response happen the same day.

What we build for financial services teams

Financial services is one of many industries Ryz serves. The work is distinctive because records are permanent, numbers must reconcile to the cent, and a supervisor or examiner may read any of it years later. Our engineers work on:

• Trading and post-trade platforms. Order routing, FIX connectivity, allocation and confirmation flows, and the trade capture that feeds settlement, books and records, and CAT reporting.
• Lending and credit systems. Loan origination and servicing for commercial and consumer credit, covenant tracking, collateral management, and decision engines that produce adverse action reasons a compliance team can defend.
• Wealth and client platforms. Advisor desktops, client portals and mobile apps, account opening with KYC and suitability capture, and integrations with custodians and CRMs such as Salesforce Financial Services Cloud.
• Risk and finance data. Market and credit risk calculations, liquidity reporting, general ledger feeds and the lineage needed to explain a number back to its source.
• Core modernization. Moving COBOL and mainframe batch, legacy Java and .NET monoliths, and nightly file jobs to services and event streams without breaking the downstream reports that depend on them.
• Surveillance and compliance tooling. Communications capture, trade surveillance case management, AML alert queues and the reviewer workflows that sit on top of them.
• Payments and treasury operations. Wire and ACH origination, ISO 20022 message handling, cash positioning and reconciliation against custodian and correspondent statements.

Where AI pods help in financial services

An AI pod is a dedicated team of senior engineers that builds an AI system in your cloud and repos and runs it in production. In financial services, these are the use cases that tend to survive model risk review and reach production:

• Marketing and communications compliance review. Checking advertisements, pitch books and client letters against firm policy and the SEC Marketing Rule or FINRA Rule 2210. The engineering challenge is retrieving the right policy clause and showing reviewers why a passage was flagged. One of our pods built marketing compliance review for a global capital management firm that covers more than 8,000 documents and cut review time from days to hours.
• Document intake for lending and onboarding. Extracting fields from financial statements, tax returns, credit agreements and entity documents. The hard part is handling scanned and inconsistent layouts and routing low-confidence fields to a human.
• Analyst and advisor copilots. Retrieval over research, policies and product documents, with answers that cite sources and respect entitlements so a user never sees content they are not cleared for. See our RAG development work.
• Alert triage for AML and surveillance. Summarizing alert context and drafting narratives for investigators, while the investigator still makes and records the decision.
• Fraud and anomaly detection. Scoring transactions or documents in real time against rules and models, with feedback from investigators. Our pod that built fraud detection for a global fleet company surfaced $5.94M in fraud confirmed by the client's own fraud team.

Details on these engagements are on our case studies page. Client names are not public.

Regulations and constraints our engineers work within

Our engineers have experience building within the standards below, working alongside your compliance, legal and security teams. Ryz does not certify your systems or provide legal advice.

• SEC and FINRA recordkeeping. SEC Rule 17a-4 and FINRA Rule 4511 for broker-dealer records, including the WORM or audit-trail storage options, and SEC Rule 204-2 for investment advisers. This shapes how chat, email and AI-generated drafts are retained.
• FINRA supervision and communications. Rule 3110 supervision and Rule 2210 communications with the public, which apply to content that AI tools help produce.
• GLBA. Privacy and safeguards requirements for customer financial information, which determine encryption, access control and vendor oversight.
• Model risk management. The Federal Reserve and OCC's SR 11-7 guidance on model development, validation and ongoing monitoring, which most institutions now apply to machine learning and LLM systems.
• Fair lending and credit reporting. ECOA and Regulation B adverse action notices and FCRA permissible-purpose rules, which constrain what a credit model can use and how it explains a decision.
• BSA/AML and OFAC. Customer identification, transaction monitoring and sanctions screening obligations.
• NYDFS Part 500. New York's cybersecurity regulation for licensed entities, including MFA, access privilege and asset inventory requirements.
• SOX and EU DORA. Change control over financial reporting systems for public companies, and ICT risk and third-party requirements for firms with EU operations.

Integrations and data

Most of the effort in financial services engineering goes into connecting systems that were never designed to talk to each other. Our teams work with:

• FIX protocol sessions, OMS and EMS platforms, and market data from Bloomberg, LSEG and FactSet.
• SWIFT messaging and ISO 20022 (pacs, camt and pain messages), Fedwire and ACH file formats.
• Core processing platforms from Fiserv, FIS and Jack Henry, plus loan servicing systems and custodian feeds.
• CRM and onboarding stacks such as Salesforce Financial Services Cloud, nCino and KYC vendors.
• Data platforms including Snowflake, Databricks, Kafka and mainframe extracts in fixed-width and EBCDIC formats.

How teams engage Ryz

Staff augmentation fits when you have the architecture and engineering leaders and need senior people to move faster: a Java engineer on the post-trade team, a data engineer for regulatory reporting, an iOS developer for the client app. They join your standups and repos and take direction from your leads. If you are hiring for a fintech product team, our fintech developers page covers that.

An AI pod fits when you want a specific system built and in production, such as compliance review or document intake. A pod is often around seven senior engineers, including a tech lead, an ML engineer and backend engineers, working in your cloud with weekly demos. A dedicated development team fits a scoped non-AI system, such as a legacy modernization of a lending platform.

Cost is team size times duration times the monthly rate. Mid-level engineers run $7,000 to $10,000 per month, senior engineers $10,000 to $15,000, and leads $15,000 and up. Four senior engineers for six months at $12,000 each is $288,000. You get a plan, a price and the names of the people before you start.

When Ryz isn't the right fit

• You want a management-consulting or board-level transformation program, or a proprietary AI platform you license. A large consultancy or platform vendor is a better match.
• You need engineers in European or Asian time zones, or follow-the-sun support coverage. A global network covers those hours.
• You want hourly freelancers or a self-serve trial before talking to anyone. A freelance marketplace fits that.

Related

• Asset management software development
• Fintech software development
• Payments software development
• Best AI engineering teams for financial institutions
• AI legacy modernization

FAQ

Can Ryz engineers work with regulated customer data?

Yes. Our engineers work in your environment, under your controls: your cloud accounts, your identity provider, your VPN or VDI, your data access approvals and your logging. Ryz does not hold a certification on your behalf. Your security and compliance teams decide what data engineers can reach, and our engineers have experience working within GLBA, NYDFS Part 500 and SEC and FINRA recordkeeping requirements.

How do you get an AI system through model risk review?

The pod builds with validation in mind from the start: documented data sources, evaluation sets agreed with the business, performance monitoring, versioned prompts and models, and a human reviewer on decisions that need one. Your model risk team still runs its own validation under SR 11-7 or your internal policy.

Is financial services the only industry Ryz works in?

No. Ryz serves enterprises across industries, including healthcare, logistics, insurance and retail. Financial services is one vertical where we have production AI work and engineers with deep domain experience.

What does a financial services engineering team from Ryz 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, so a five-person senior team for four months at $12,000 each is $240,000. Quotes are scoped per team.

Can your engineers support New York trading hours?

Yes. Our engineers work on US business hours, including New York hours, which gives real-time overlap with your trading desks, operations staff and on-call rotations.

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