Enterprise Application Modernisation & ERP Consulting
AI agents can only act on data they can reach. For most enterprises, that means ERP, CRM, and internal applications need to be integration-ready before AI deployment can begin.
- SAP, Dynamics 365, Odoo — implemented and customised for Indian enterprises
- Strangler-fig migrations — not big-bang rewrites
- API enablement and event streaming for agent-ready systems
- Fixed-price discovery sprint before any build commitment
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Why Enterprise Systems Need to Modernise Before AI Can Deliver
Most AI initiatives stall not because of the AI — but because the enterprise systems underneath don't expose data in a form agents can consume. Legacy ERPs, CRMs, and internal applications were not designed with external API access or real-time event streaming in mind.
ITMTB modernises enterprise applications and ERP systems so the systems you've built over the last decade remain operational while new capabilities are layered on top — without the risk of a big-bang rewrite.
Typical enterprise application activities
Engagements cover discovery, API layer design, progressive migration, and post-delivery operations — helping enterprises extend the life of existing systems while making them ready for the next decade of tooling.
Engagement Methodology
Every engagement follows a defined sequence — no build commitment before the discovery sprint is complete.
Discovery Sprint
- Architecture and dependency review
- API exposure and integration capability audit
- Risk register and migration path recommendation
- Written cost estimate and timeline
- Rewrite vs. refactor vs. wrap recommendation
API & Integration Layer
- Versioned API layer over legacy data stores
- Event streaming for real-time agent access
- Agent integration interface specification
- Security and access control architecture
Progressive Migration
- Strangler-fig approach: domain-by-domain
- Legacy system runs through the transition
- Framework and runtime upgrades per domain
- CI/CD pipeline maintained throughout
Selected work in enterprise applications
a global supply chain leader managing 100,000+ SKUs across 35 countries
Empowering Supply Chain Businesses with Advanced AI: A Look at Our Image Matching Technology
99% operational efficiency gain in pattern matching — 70% more accurate, under 3 minutes
Fintech Portfolio — Legacy Brokerage and Emerging Startups
Digitally Transforming Legacy and Startup Fintechs: A Journey Towards Innovation
3 of 5 KPIs met or exceeded post-implementation across fintech partners
Startup Investment Platform connecting Entrepreneurs and Investors
Pioneering a New Era in Startup Investing: Streamlining Connections between Entrepreneurs and Investors
3.8/5 average platform experience rating post digital deal room launch
Building the Integration Layer That AI Agents Need
AI agents require access to live operational data — inventory levels, order status, customer records, financial positions. If your ERP or CRM doesn't expose this through a clean, versioned API, agents either operate on stale data or can't act at all.
The integration layer we build is not a workaround. It is a versioned, testable API over your existing data stores — with event streaming added where agents need to react to real-time state changes rather than polling. This layer outlasts any specific AI tooling you adopt.
Systems we have built integration layers for include SAP Business One, Microsoft Dynamics 365, Salesforce, Tally, proprietary Java EE applications, and legacy .NET systems — all without replacing the underlying system before the business is ready.
Comparing Modernisation Approaches
Understanding the structural difference between a big-bang rewrite, a lift-and-shift, and a progressive strangler-fig migration.
| Big-Bang Rewrite | Lift-and-Shift | Strangler-Fig Migration ITMTB approach | |
|---|---|---|---|
| Risk | High — entire system replaced at once | Medium — infrastructure changes, logic unchanged | Low — legacy runs until each domain is replaced |
| Business continuity | Disruption during cutover | Minimal operational disruption | Continuous — no single cutover event |
| Timeline | Longest — full rebuild required | Shortest — infrastructure only | Moderate — phased by domain count |
| AI readiness result | Depends on new architecture decisions | Poor — same integration constraints remain | High — API layer built into migration |
| Cost profile | High upfront, lower long-term maintenance | Low upfront, unchanged maintenance overhead | Moderate upfront, reduced maintenance overhead |
| Recommended for | Greenfield replacement only | Infrastructure-constrained migrations | Most enterprise modernisation engagements |
How We Modernise Enterprise Systems
A structured four-phase process — scoped before any build commitment is made.
Discovery Sprint
2 weeks · Fixed price
- Architecture review and dependency mapping
- API exposure and integration capability audit
- Risk register for chosen migration approach
- Cost estimate and timeline before build commitment
- Written recommendation: rewrite vs. refactor vs. wrap
API & Integration Architecture
Weeks 3–6
- Versioned API layer design over legacy data stores
- Event streaming architecture (Kafka, Azure Event Hub, EventBridge)
- Agent integration interface specification
- Security and access control design
Progressive Migration
Phased delivery
- Strangler-fig approach: domain-by-domain migration
- Legacy system runs until each domain is replaced
- Framework and runtime upgrades (Python 3, .NET 8, Spring Boot)
- CI/CD pipeline maintained through migration
Handover & Operations
Post-delivery
- Architecture documentation and runbooks
- Knowledge transfer and team training
- 4–8 week post-launch warranty period
- Optional transition to managed services
Engagement Model
Every engagement starts with a fixed-scope, fixed-price discovery sprint — typically two weeks. You leave with a written roadmap, risk register, cost estimate, and migration path recommendation before committing to a build.
Build engagements are then scoped based on discovery findings and offered as fixed-scope, time-and-materials, or outcome-linked retainers — depending on the nature of the migration and your team's involvement. Post-launch, managed services engagements are available to maintain and evolve the systems we deliver.
If the discovery sprint doesn't justify proceeding, you can stop there — with a written report that is useful regardless of whether you engage further.
Our Capabilities
Agent-Ready Enterprise Integration
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ERP Implementation and Customisation
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Legacy System Modernisation
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Custom Enterprise Application Development
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Application Performance and Reliability
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Our Work
ITMTB helps companies build, operate, and improve business-critical technology systems through software engineering, cloud operations, cybersecurity, AI deployment, and managed services.Let us talkand explore how we can help.
Related capabilities
Managed IT Services
Operations management and production support for the systems we build or take over — yearly engagements with weekly reporting.
Learn more →Cloud Infrastructure
Architecture, migration, and operations for the cloud layer your modernised applications run on.
Learn more →AI Agent Deployment
Deploying AI agent systems that connect to the enterprise integration layers we build.
Learn more →Frequently asked questions
Before you engage
Engagement Model
How we work
Discovery sprints, fixed-scope builds, T&M, retainers, and outcome-linked models — with pricing logic and what to expect at each stage.
Read the engagement guide →Cost Guide
What custom software costs in India
Pricing models, total cost of ownership, QA investment, and support SLA economics — a full breakdown for enterprise buyers evaluating a build.
Read the cost guide →Start the conversation
Start with a Discovery Sprint
Two weeks, fixed price. We review your architecture, audit integration exposure, map dependencies, and deliver a written migration recommendation — before you commit to a build.
Request a discovery sprint →