GT.M/MUMPS & Core Banking Consulting · AI Systems Engineering

Two practices, one standard of rigor

Rare depth at both ends of enterprise technology.

Most AI consultancies have never seen a MUMPS global. Most legacy specialists have never built an agent. Velastra is fluent in both — each practice stands on its own.

Practice 01 — Legacy Systems

GT.M / MUMPS & Core Banking

Optimization, integration, and modernization of the database technology that still runs core banking and healthcare worldwide.

GT.M database optimizationGlobal structure tuning, transaction throughput, and lock contention analysis for high-volume environments.

Core banking platform consultingArchitecture review, system integration, and migration planning for GT.M-based core banking platforms.

Legacy modernization roadmapsPhased, risk-mitigated modernization — assessment, dress-rehearsed migration, clean reconciliation.

Payments & messagingISO 8583 payment switch integration and ISO 20022 migration advisory.

GT.M / MUMPS consulting in detail →

Practice 02 — Enterprise AI

AI Systems Engineering

Architecting, delivering, and governing agentic AI systems that meet enterprise standards for security, auditability, and reliability.

AI systems architectureEnd-to-end design of agentic AI systems — orchestration patterns, integration strategy, model selection, and governance architecture.

AI readiness & advisoryStructured assessment of where AI creates measurable value in your operations, and a pragmatic, sequenced roadmap to get there.

AI solution deliveryDesign-to-production build of agentic applications, knowledge retrieval systems, and system integrations — engineered like production software.

AI operations & governanceEvaluation frameworks, monitoring, guardrails, and human-in-the-loop controls for AI systems in production.

AI systems engineering in detail →

Delivery framework

Operational rigor, either practice

STEP 01

Assess

Deep-dive audit — database schema, transaction locks, and utilization for legacy work; data, tools, and risk surface for agentic work.

STEP 02

De-risk

Structured risk analysis under peak-load and failure constraints. Guardrails, rollback paths, and dress rehearsals before anything touches production.

STEP 03

Deploy in phases

Phased rollout with minimized operational impact — measured, reconciled, and documented at every stage.

Advisory reviewA scoped, fixed-duration technical assessment — architecture, performance, or AI-readiness — with a written findings report.

Project deliveryDefined-outcome engagements: an AI system, a migration plan, an optimization program — delivered end to end.

Retained specialistOngoing senior capacity for teams that need GT.M/MUMPS or AI systems depth on call, without a full-time hire.

Deep in legacy cores.
Deep in agentic AI.