AI Without Retirement Domain Knowledge Is Just Noise.
Everyone is talking about AI transformation. We’re delivering it — because we understand the processes AI needs to automate, not just the models that power it.
The Model Is the Easy Part
AI models are commodities. The API call is the same whether you're processing insurance claims or retirement plan data. What separates an AI experiment that gets a polite demo from one that runs in production is domain knowledge — deep, specific, hard-earned understanding of the systems, processes, and edge cases that define your operations.
That's where most AI initiatives stall. The technology is there; the context isn't. And in retirement operations, context is everything.
We Have Both
We've spent 25 years inside the systems, processes, and data structures that power retirement recordkeeping. When we build an AI agent, it isn't a generic chatbot bolted onto a financial services workflow. It's a purpose-built tool that understands plan types, compliance rules, data formats, and the operational edge cases that only come from having lived inside these platforms.
Both technology-side expertise and process-side expertise are required. We operate at that intersection.
Why Most AI Experiments in Retirement Fail
The pattern repeats across industries: organizations invest millions in AI platforms, train models on generic data, and deploy tools that produce confident-sounding output that falls apart under scrutiny. In retirement operations, the stakes are higher — participant data, compliance requirements, and financial calculations don't tolerate “close enough.”
We've seen it firsthand. Even deeply resourced technology programs struggle in retirement when the team hasn't configured a plan conversion or debugged a nightly batch process at 3 AM. Scale helps — but platform depth is what decides outcomes.
AI amplifies this problem. Without deep domain knowledge, AI doesn't just make mistakes faster — it makes confident mistakes faster. And in a regulated industry where accuracy is non-negotiable, that's a liability, not a feature.
Our Proprietary AI Framework
Everything Here Runs on MAPTIVA™.
Every agent, every automation, every production result on this page runs on one thing: MAPTIVA™, our proprietary AI framework — built for retirement, not for data. It encodes 25 years inside platform migrations, payroll exception handling, field taxonomy, and code-dependency analysis into a single platform that arrives already knowing the terrain — without the months of discovery, the blank-page guesswork, and the false starts.
What Our AI Delivers in Production
These aren't concepts. They're the production applications our MAPTIVA™ framework powers — deployed with clients today, not waiting on a roadmap.
Payroll Processing Agents
AI agents that reconcile payroll files, identify contribution discrepancies, and resolve exceptions that today consume hours of manual review. Not a dashboard that surfaces problems — an agent that fixes them.
Conversion Testing Automation
AI-driven test generation and validation for platform migrations. We reduce QA cycles from weeks to days by automatically generating test scenarios based on actual plan configurations — not generic templates.
Data Mapping Intelligence
AI that understands the semantic relationships between fields across OMNI, TRAC, Relius, and proprietary platforms. When a field name in one system means something different in another, our agents know — because we taught them.
Plan Onboarding Automation
AI agents that process plan documents, extract rules, and configure recordkeeping systems. The hours your team spends manually keying plan provisions into your platform? We automate that — accurately.
Migration Impact Analysis
AI-powered assessment of OmniScript and custom code dependencies for FRP readiness. Instead of a manual code review that takes months, our agents analyze your entire script library and deliver a dependency map in days.
From Robotics to Cloud to Blockchain to AI.
Will Retirement Systems Ever Be Transformed?
The retirement industry has weathered every technology wave — from mainframes to client-server, from on-premise to cloud, from batch processing to real-time APIs. Each wave brought promises of transformation. Most delivered incremental improvement at best.
The reason is structural. The complexities and customizations of retirement plans — thousands of plan types, each with its own rules, vesting schedules, compliance requirements, and operational workflows — make it extraordinarily difficult to standardize processes and systems. Huge projects have failed on account of exactly this challenge.
AI may be the technology that finally cracks it. Not because the models are smarter, but because AI can bridge the gap between standardization and customization — helping systems adapt to individual plan rules without human intervention at every step. But only if the people building the AI understand the retirement domain deeply enough to encode it correctly.
That's the work we do. And it's why we exist.
The People Behind Our AI
Practitioners First. Then AI.
The agents we put in production are designed by partners who've operated the platforms they automate — people who knew these workflows long before they automated them.
Gerard (AG) Harris — Partner, Client Engagements
Leads Convergent's AI vision. 25 years systematically eliminating the inefficiencies that erode recordkeeper profitability — spanning automation, data analytics, and workflow.
Vishnu Tej — Chief AI Architect
15 years at Convergent. Architects multi-agent systems for retirement processing — networks of specialized AI agents that reason across plan documents, regulatory requirements, payroll data, and participant behavior to autonomously resolve exceptions.
Our Approach
We take on complex, intimidating retirement projects and simplify them. That's always been our thesis. AI is the newest tool — but the approach hasn't changed.
Identify
We map your operations end-to-end and pinpoint the processes where AI will deliver the highest ROI — not the flashiest demo.
Model
We encode deep retirement domain knowledge into every agent — plan types, compliance rules, data formats, and the operational edge cases that generic AI misses entirely.
Build
Working automation on real data, not slide decks. Our 500+ reusable retirement modules accelerate development so you validate results in weeks, not quarters.
Embed
We deploy directly into your existing infrastructure — OMNI, TRAC, Relius, or proprietary systems. No rip-and-replace. Your automated workflows integrate seamlessly with the batch jobs and file exchanges you already run.
From the Library
The research and engineering behind our AI work
The data infrastructure, participant-intelligence frameworks, and production engineering that ground every agent we build.
The Data Your Retirement Platform Already Has
Why the Retirement Industry Is Sitting on a Mountain of Underused Data — And How to Measure Whether Your Firm Is Leaving Value on the Table
Introducing the Retirement Data Execution Quality (RDEQ) framework — a 500-point assessment across five categories that measures how well a firm actually uses the data it already collects.
Unlocking Participant Intelligence
Why the Next Generation of Retirement Savers Demands a New Playbook — And What It Means for Every Recordkeeper, TPA, and Plan Sponsor in America
The retirement industry was built for the Baby Boomers. The workforce it now serves is fundamentally different — and the gap between what the industry offers and what the next generation expects is growing wider every quarter.
AI Won't Replace Your Retirement Operations Team. But It Will Replace the Parts They Hate.
Payroll file processing is the biggest time sink in retirement operations. AI-powered reconciliation is finally ready to fix it — but only if it's built for the complexity of retirement data.
Read articleAI for retirement operations starts with practitioners who’ve lived it.
Book a conversation with our AI team. We’ll show you what’s actually working in production — not what looks good on a slide.