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Primeval IT Solutions

We Get the IT Job Done

AI-Enabled Software Engineering Company

Engineering Excellence.

Software engineering. Platform engineering. Cloud native. AI-Driven Engineering. Dedicated teams that ship production systems.

  • Software Engineering
  • Platform Engineering
  • Cloud Native
  • AI-Driven Engineering
  • DevSecOps
  • Dedicated Teams
Explore

Primeval IT Solutions is an engineering company - not a consulting firm. From hubs in India and New Zealand, we deliver software craftsmanship, scalable platforms, DevSecOps, modernization, and AI-assisted engineering productivity for enterprise clients worldwide.

  • 2Engineering hubsIndia & New Zealand delivery centres
  • 7Engineering practicesSoftware, platform, cloud, AI, QA, DevSecOps, telecom
  • 6Industry domainsTelecom, banking, insurance, healthcare, retail, technology
  • EnterpriseDelivery focusProduction systems, not prototypes

Engineering outcomes

Representative programmes taken to production

Anonymised examples of modernization, platform engineering, and AI-assisted operations - the kind of work we are accountable for in production.

Representative outcomes from comparable programmes. Client names are published only with written approval.

  • Banking & financial services

    Lending platform modernization on Amazon EKS

    Challenge

    High-volume e-credit and personal loan journeys ran on unsupported AngularJS, while credit-ops teams depended on a Visual Basic desktop tightly coupled to legacy data stores.

    Approach

    Rebuilt customer and credit-ops experiences as React applications, introduced Spring Boot domain services with MS SQL, and productionised the platform on Amazon EKS with CI/CD, observability, and staged cutover.

    Outcomes

    • Legacy AngularJS and VB fronts retired without service interruption
    • Unified modern UI for lending and credit operations
    • Containerised services running on Amazon EKS with automated releases

    Stack

    • React
    • Spring Boot
    • MS SQL
    • Amazon EKS
    • CI/CD
  • Telecommunications

    Network operations intelligence and runbook acceleration

    Challenge

    NOC and operations teams were overwhelmed by alarm noise and ticket volume, while critical knowledge remained concentrated in a small set of SMEs.

    Approach

    Delivered an operations intelligence layer that summarises incidents, retrieves cited runbooks, and supports investigation workflows - read-only first, with governed automation paths for later stages.

    Outcomes

    • Improved MTTR on priority incident classes
    • Faster onboarding for network engineers using grounded knowledge
    • Security-accepted pattern for AI-assisted operations tooling

    Stack

    • Knowledge retrieval
    • Ops workflows
    • Observability
    • DevSecOps
  • Insurance

    Agency service platform re-architecture

    Challenge

    Agents supported customers on a monolithic Visual Basic system tightly coupled to Oracle and past vendor support - limiting change velocity and raising continuity risk.

    Approach

    Introduced a microservice architecture with React micro-frontends, Node.js services against Oracle, and Red Hat 3scale for API governance, identity, and rate control.

    Outcomes

    • Out-of-support VB desktop retired
    • React MFE agent experience in production
    • Enterprise API governance with 3scale

    Stack

    • React MFEs
    • Node.js
    • Oracle
    • Red Hat 3scale
  • Healthcare

    Provider data platform rebuild on Oracle Cloud

    Challenge

    Provider records lived in an unsupported legacy application that constrained change, slowed operations, and elevated continuity risk for a critical healthcare dataset.

    Approach

    Rebuilt the provider experience in React with Java Spring Boot services, migrated to Oracle Cloud Infrastructure, and established automated testing and release controls for regulated change.

    Outcomes

    • Unsupported legacy application fully replaced
    • Modern React and Spring Boot provider-data platform
    • Secure, scalable delivery on Oracle Cloud Infrastructure

    Stack

    • React
    • Spring Boot
    • OCI
    • Automated testing
  • Enterprise technology

    Cloud-native platform engineering for release velocity

    Challenge

    Product teams shipped through fragmented pipelines, inconsistent environments, and manual release gates that slowed delivery and increased production risk.

    Approach

    Stood up a platform engineering foundation - shared CI/CD, infrastructure as code, golden paths for services, and DevSecOps controls - so product squads could ship safely without reinventing the platform each sprint.

    Outcomes

    • Standardised build and deploy paths across squads
    • Reduced environment drift with IaC and policy checks
    • Faster, more predictable production releases

    Stack

    • Kubernetes
    • Terraform
    • GitOps
    • DevSecOps
    • Observability
  • Retail & commerce

    Dedicated engineering team for omnichannel scale

    Challenge

    Peak commerce events exposed brittle integrations between storefront, inventory, and fulfilment - with insufficient automated coverage and no dedicated squad ownership.

    Approach

    Embedded a dedicated engineering team for commerce services: API hardening, event-driven inventory sync, performance testing, and continuous delivery with clear SLOs.

    Outcomes

    • Stabilised peak-season order and inventory flows
    • Automated regression coverage on critical journeys
    • Accountable squad ownership with measurable SLOs

    Stack

    • Node.js
    • Event-driven APIs
    • Performance testing
    • CI/CD
  • Financial services

    AI-assisted quality engineering for payment releases

    Challenge

    A payments platform team struggled to keep API and journey coverage current as product changes accelerated - regression packs lagged releases, and change-impact analysis depended on a few senior QA engineers.

    Approach

    Introduced an AI Testing assistant into the quality workflow: generated draft API and UI regression cases from OpenAPI specs and recent commits, mapped change impact to risk-ranked suites, and kept humans as owners of suite selection, assertion review, and release sign-off.

    Outcomes

    • Regression packs updated within the same sprint as feature work
    • Earlier detection of payment-journey regressions before production
    • Reduced reliance on tribal QA knowledge for impact analysis

    Stack

    • OpenAPI
    • Playwright
    • API testing
    • CI gates
    • AI-assisted QA
  • Enterprise software

    AI Engineering Assistants for a dedicated delivery squad

    Challenge

    A multi-year platform programme had slow story refinement, thin architecture decision records, and long pull-request cycles - senior engineers spent disproportionate time on documentation and review boilerplate.

    Approach

    Embedded role-based AI Engineering Assistants into one dedicated squad: BA support for acceptance criteria, architect support for ADR drafts, developer support for refactor and unit-test scaffolding, and DevOps support for pipeline evidence packs - all reviewed before merge under existing Definition of Done.

    Outcomes

    • Shorter refinement-to-ready cycle on complex stories
    • Consistent ADRs and release notes without extra headcount
    • Measurable drop in PR turnaround while review ownership stayed with engineers

    Stack

    • AI-Assisted SDLC
    • ADRs
    • Unit testing
    • CI/CD
    • Dedicated squad

Engage with Primeval Engineering

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Delivery hubs in India & New Zealand · Engineering programmes across Australia, New Zealand, India, and global enterprises.

Tell us about your architecture, delivery model, modernization roadmap, or dedicated team needs.

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Emailcontact@primevalit.comPhone+1 814-294-1414
Delivery hubsIndia & New Zealand
Global reachAustralia, New Zealand, India & beyond