How ASIMOV Supports the Application Modernization Process

Architecture and Operating Model

From Fragmented Legacy Assets to a Validated, Maintainable Target System

ASIMOV connects source understanding, target intent, specification-as-code, agentic execution, continuous validation and retained engineering knowledge in one controlled lifecycle.

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The Challenge

People spend too much time searching for information, interpreting fragmented knowledge, switching between systems, and repeating routine work, all of which directly limit productivity. At the same time, enterprises are rolling out copilots and AI platforms that often lack role-specific context, trusted enterprise knowledge, or a clear adoption model. Instead of improving productivity, AI can become another tool people need to learn, verify, and work around.

What We Deliver

Role-aware AI assistance. We give employees and customers assistance across the moments where people lose time, context, and productivity: finding and applying knowledge, supporting decisions, completing common requests, and learning new ways of working with AI.
Knowledge and adoption foundation. We build the enterprise knowledge foundation and adoption model required for these capabilities to become trusted parts of everyday work and deliver sustained productivity improvement.
Trusted, grounded answers. Assistance draws on governed enterprise context rather than raw retrieval, so people get consistent, cited answers they can act on with confidence.

0

M+

Lines of legacy code modernized

0

Enterprise programmes

0

+ Years

Continuous field refinement

3

Faster modernization

40

Cost reduction versus conventional approaches

Source code, configuration, modules and build structures

Database schemas, procedures, files and data models

APIs, messages, batch jobs, schedulers and reports

Screens, journeys, roles, calculations and validations

Tests, runbooks, manuals, target standards and architecture blueprints

Structural truth

Applications, modules, classes, methods, calls, jobs, tables, interfaces and dependencies.

Behavioral truth

Business rules, calculations, process paths, data movement, exceptions and user journeys.

Engineering meaning

Descriptions, risk signals, duplicate logic, technical debt, confidence and review status.

Enterprise AI Strategy & Advisor

Shape and prioritize AI opportunities across people, processes, and products, with clear value, readiness, and execution paths.

AI-Led Legacy Modernization

Reconstruct complex applications, define the target state, and execute controlled modernization or software transformation with ASIMOV.

Forward Engineering

Carry modernized system knowledge forward into new features and services so the application can evolve without immediately rebuilding architectural debt.

Product Portfolio Rationalization

Use business and engineering evidence to decide where to invest, consolidate, re-engineer, maintain, or retire across the product portfolio.

AI-Augmented Software Engineering

Extend AI beyond coding into impact analysis, development, testing, documentation, review, and release.

SaaS Agentification & Embedded AI Features

Add intelligent capabilities and agentic experiences to existing SaaS products while respecting the product architecture, user experience, and operating constraints.

AI-Led Brownfield Support

Make complex live applications easier to understand, support, enhance, and transition toward modernization when the business is ready.

Graph Ingestion & Enrichment

Converts source assets into a connected system graph covering code, data, rules, dependencies and flows.

Business Logic & Dependency Intel

Identifies execution paths, validations, calculations, integration points and impact relationships.

Documentation Generation Engine

Generates functional, technical, integration, data and test documentation

Traceability & Evidence Layer

Links source components, generated artifacts, target requirements, decisions and validation evidence.

Knowledge Chat & Search

Teams ask questions across applications, generated documents, rules, dependencies and migration artifacts.

Expert Review & Delta Capture

Users review, edit, enrich and capture approved future-state changes or non-as-is requirements.

Retain, modify, replace, consolidate or retire decisions

Target components, service boundaries and platform patterns

Business-rule traceability and approved future-state change

Data, interface, security and non-functional requirements

Versioned review comments, exceptions and approvals

Discover

Retain, modify, replace, consolidate or retire decisions

Specify

Translate approved target architecture, mappings, rules and acceptance criteria into ASF.

Migrate

Agents transform, generate, compile and remediate within approved constraints.

Validate

Check architecture, functionality, interfaces, data and behavioural evidence continuously.