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AI enablement

A quick, low-risk transition to AI.

We help organizations adopt AI fast, in two ways: transforming legacy systems into modern platforms, and integrating AI into the solutions they already run.

Our approach

AI where it pays off.

We start with your business, not the technology.

  1. Where can AI help?

    Discover

    We map your processes with you and find where AI saves time, cost or risk.

  2. Will it pay off?

    Justify

    Each use case gets a business case and success measures agreed up front.

  3. How do we get there?

    Build and streamline

    We build it and streamline the workflows around it.

  4. What do you get?

    Deliver

    Live in production, with a return you can measure.

Legacy to modern transformation

Replace aging platforms with modern, AI-native ones, without a multi-year program.

  • Any legacy stack, desktop or web
  • AI analyzes legacy code and derives its business rules
  • AI-driven data migration into redesigned databases
  • Responsive, offline-capable platforms with AI built in

Seen in: platform modernization, tech stack revamp, evaluation platform rebuild

AI in existing solutions

Keep the systems that work. We connect AI to them and automate the work around them.

  • AI agents and chat on top of your data
  • Workflow automation with human approval gates
  • AI-driven matching, reconciliation and decisions
  • Integrations through APIs and MCP connectors

Seen in: POS-to-ERP integration, RFQ response automation

How we work

Senior engineers and AI agents, with people at every gate.

Engineers own

  • Architecture and domain boundaries
  • Business-critical decisions
  • Review of every change

AI agents handle

  • Specs and documentation
  • Legacy code analysis and deriving its meaning
  • Code and test generation

AI-driven, human-gated

  • PR approvals
  • Rollouts
  • Commercial approvals in automated workflows

Full transparency. AI and MCP integrations with your Jira, Azure DevOps or ServiceNow tracker keep every work item visible.

Our accelerators

Proven building blocks, so nothing starts from scratch.

Our teams work from a shared Jillella Group framework: the practices, standards and templates behind every engagement.

AI-driven SDLC

From requirement to release

One AI-native lifecycle from requirement to release.

C4 architecture models

Context down to code

Every system mapped at four levels before code is written.

SDD and DDD artifacts

Constitution and model rules

Guidelines and customizable industry standards.

Best practices

Proven design patterns

Learnings from integrations and app development.

Standards built in

Security and regulatory

Coding, security and compliance applied by default.

Reference architectures

Auth, RBAC and roles

Standard authentication, permissions and roles.

Core platform elements

PWA, mobile, offline

Responsive, tablet-ready and offline-capable.

Ready-to-deploy tests

Functional and performance

Functional and performance suites, ready to run.

Infrastructure as code

Cloud, hybrid or on-premises

Templates for public, private or hybrid environments.

Case studies

Selected work.

Legacy systems we have transformed and solutions we have brought AI into. Client names withheld.

US

Platform modernization

Legacy to live2–3 months

Before

  • A legacy codebase built up over 15+ years
  • Needed specialized, dedicated developers
  • Releases took about a month
  • No AI capabilities, losing ground to competitors

After

  • A modern, AI-native platform
  • AI chatbots and agents
  • AI-driven decisions and pricing recommendations
  • Seamless third-party integrations
<1 dayrelease cycle
2engineers, a lean team
−40%run and maintenance cost
+50%traffic and registrations

US

Tech stack revamp

Legacy to live3 months

Before

  • 20+ years on an aging technology stack
  • An outdated user experience
  • Releases took 2–3 weeks
  • No path to AI

After

  • A modern, cloud-ready platform
  • AI integration with external research databases
  • AI-verified accuracy of research records
  • AI-assisted data migration
1–2 daysrelease cycle
−50%maintenance and infra cost
2 FTElean support team

US

Evaluation platform rebuild

Legacy to live~2 months

Before

  • A desktop-only client
  • Releases took about a month
  • Hard to scale, no AI

After

  • A modern web and mobile platform
  • AI-generated evaluation summaries
  • Learns from previous evaluations
  • AI-targeted checks where risk is highest
3–4 hrsrelease cycle
1person to run it
Automatedsupport workflow

Global

Operations platform modernization

Legacy to live3 months

Before

  • 8+ years on a spreadsheet-style system
  • Releases took months
  • Partly paper-based
  • No real-time reporting

After

  • Modern session management
  • Tailored experiences for every role
  • AI document scanning and video analysis
  • Real-time reporting
~10 minclient onboarding
Hoursto build a client plan
1–2 daysrelease cycle

India

Monitoring platform evolution

Legacy to live4 months

Before

  • Every change needed a developer
  • Releases took 2–3 weeks
  • Reports and alerts built by hand

After

  • An AI-driven, workflow-based platform in the cloud
  • Drag-and-drop and chat-based reporting
  • AI agents build monitoring, alerts and rules
  • IoT and digital twin integration
Hoursrelease cycle
Self-servicereports and alerts

US

Finance and operations integration

Legacy to live1–2 months

Before

  • Manual data exchange between systems
  • Hours of daily reconciliation per location
  • Delayed financials and revenue recognition

After

  • Near real-time integration
  • AI resolves mismatches automatically
  • AI verifies every transaction
  • Half an FTE monitors alerts
3–4xoperational efficiency
2xaccuracy
1/8of the original FTE cost

India

RFQ business process automation

Legacy to live2 months

Before

  • 10+ requests for quotation a week
  • 3–4 days to respond to each
  • 3+ dedicated staff

After

  • AI extracts requests from email and documents
  • AI agents draft designs and bills of materials
  • A rules engine selects the best offer
  • Human approval on every offer
1–2 hrsper proposal
0.5 FTEto run the pipeline

Start with one module or workflow. See it live in 2–3 weeks.

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