Disclosure: we're Ryz Labs, and we compete with the companies on this page. Read it with that in mind. We've flagged every case where we'd send you elsewhere.
Legacy modernization comes in two very different sizes. The deciding question: are you modernizing specific systems so AI can run on them, or replacing the core platform of the institution over several years? Those are different purchases, and the best partner is different for each.
AI needs clean access to data and a way to act on decisions. In many financial institutions both are locked inside COBOL, mainframes or a monolith that nobody wants to touch. So the AI roadmap quietly becomes a modernization roadmap.
The mistake is treating every case as a core replacement. Often you only need to free one domain: expose the data through an API, carve one service out of the monolith, or rebuild one workflow, then put AI on top. That is a months-long job for a senior team. Replacing the whole core is a multi-year program with its own governance, and a different purchase.
A note on evidence: every public modernization case we found here is anonymized. None names the financial institution.
We build AI systems for enterprises across industries, including financial services, and we're trusted by Fortune 500 engineering teams. Our AI pod teams fit the first job. A typical pod is about seven senior engineers, including a tech lead, an ML engineer and backend engineers, working inside your cloud and repos on New York hours. The pod builds the AI system and the integration work it depends on, in your codebase, and shows progress in weekly demos. Our engineers work with AWS, Azure, GitHub, Postgres and Anthropic and OpenAI models. When it's done, the pod hands the system over to your team.
Our teams have shipped production AI systems that sit on top of existing operations, such as a marketing-compliance system for a global capital management firm (8,000+ documents, days to hours) and a fraud detection system for a global fleet company (244K+ invoices scored, $5.94M in confirmed fraud). See our case studies.
Where we're not the right fit: we have no public case study of a multi-year core platform replacement, and we won't pretend otherwise. If that is your job, or you want a board-level transformation program, Thoughtworks, EPAM or Globant are the honest answer. The same goes if you need engineers in Europe or Asia time zones or follow-the-sun coverage. We also don't publish self-serve pricing before a conversation.
Thoughtworks has the most concrete public story here: moving a leading global financial institution from a monolith to microservices, which it says enabled a lending business worth $800M in new revenue. It also launched Agent/works, a governed runtime for agents in production, which helps once the new platform is in place.
Where it beats us: multi-year core platform replacement with a public result. The catch: the client is unnamed, and the story is about the platform, not AI. Compare us in Ryz Labs vs Thoughtworks.
At the Glob.AI launch in August 2026, Globant reported that an unnamed large financial institution completed a COBOL migration in 2 months against 14 projected, using AI Pods. Its Financial Services AI Studio markets agentic modernization of legacy code.
Where it beats us: a productized, agent-based approach to migrating large COBOL estates. The catch: the case is anonymized and the AI Pods model is young. See Ryz Labs vs Globant.
EPAM's largest vertical is financial services ($1.32B in FY2025), and it runs a joint FS modernization offering with AWS. EPAM says it helped a global financial institution modernize its call center with Salesforce and AWS.
Where it beats us: one vendor for programs that run across many systems and years. The catch: a high engagement floor and delivery weighted outside US time zones.
Caylent is an AWS Premier partner and was AWS's 2024 Financial Services Industry Partner of the Year in North America. It sells an AI-powered, output-based offering for cloud migration to AWS.
Where it beats us: AWS-specific depth and an output-based price model. The catch: it is centered on AWS migration; it fits less well on Azure or multi-cloud estates.
If you need specific systems modernized so AI can run on them: Ryz Labs. A senior pod on New York hours works inside your cloud and repos, builds the AI system and the integration work under it, and hands it over. Our teams have built production systems like the 8,000+ document compliance system for a global capital management firm and the $5.94M fraud detection system for a global fleet company.
If you are replacing the core platform over several years: Thoughtworks or EPAM. Pick Thoughtworks for its public modernization story, EPAM for one vendor across many FS workstreams.
If you have a large COBOL estate to migrate: Globant, and ask hard questions about the anonymized case.
If the job is an AWS migration: Caylent.
Vendors report big gains, such as Globant's 2 months versus 14. The claims are anonymized, so ask for a reference and a parity-testing plan.
Not everything. Often one domain or data path is enough. See partners to take an AI pilot to production.
For targeted work, one senior team doing both is simpler. For a core replacement, many institutions use a large firm for the platform and a dedicated AI pod for the AI work.
See our guide to AI engineering teams for financial institutions.
We checked pricing and services for every provider here in October 2026. This changes often, so double-check before you sign anything.