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  • ASIMOV_v8_1750x875_1x

    Modernize complex enterprise software with context, control, and confidence

    ASIMOV reconstructs how the legacy estate works, maps it to an approved target blueprint, executes migration through specialized agents, and validates the result with traceable engineering evidence.

    Explore how ASIMOV Works Book a technical demonstration

Overview

Smarter People focuses on the individual experience of work. We help employees and customers find the right information faster, understand context, make better-informed decisions, complete routine interactions with less effort, and adopt AI confidently. The goal is to improve individual productivity by reducing cognitive load and time lost to search, interpretation, and repetitive work, giving people more capacity for judgment, creativity, customer interaction, and higher-value work. 

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

Modernization risk lives between the files

A legacy application is a connected operating system of code, data, jobs, screens, rules, interfaces, reports and years of operational learning. Failures occur when those relationships are missed—not when developers type too slowly.

Hidden business semantics

Rules, calculations and exceptions are embedded across modules, UI flows, batch jobs and data structures.

Uncontrolled target decisions

Architecture intent is applied inconsistently when transformation is split across independent tickets and coding tasks.

Weak evidence

Compilation proves buildability—not behavioural equivalence, data correctness or preservation of business intent.

Delivery accountability

A modernization delivery system—not another developer tool

Customers do not receive a licence and carry the burden of making it work. Accion Labs configures the platform, operates the agentic migration factory, manages quality gates and owns the agreed delivery outcome.

Source intelligence and target blueprint established before migration

Agents perform the code transformation and self-correction cycle

Engineers improve agents, patterns, prompts and validation rules

Human experts govern ambiguity, architecture and release acceptance

ASIMOV - One Intelligence Core
ASIMOV - One Intelligence Core

Agentic lifecycle

The developer improves the factory—not each converted file

Once scope, specification and target architecture are approved, specialized agents perform the migration. Human experts control decisions and exceptions rather than manually rewriting the target application.

Process Intelligence and Discovery

Discover

Build structural and behavioural truth from code, data, jobs, interfaces, documents and tests.

ground

Specify

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

engineer or Migrate

Migrate

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

Validate

Validate

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

Maintain

Maintain

Carry migration knowledge into the target graph for impact-aware engineering.

AI-Native Product Engineering

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

AI-Led Legacy Modernization

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

Forward Engineering

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

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.

One intelligence core

Engage at any stage. Retain the same system context

Use ASIMOV for discovery, migration readiness, full modernization, rationalization or continuous maintenance.

AI-Native Product Engineering

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

AI-Led Legacy Modernization

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

Forward Engineering

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

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.
01

Discovery & documentation

Reconstruct application knowledge and make business rules, dependencies and flows reviewable.

02

Migration readiness

Establish scope, target mappings, sequencing, complexity and a representative proof wave.

03

Full modernization

Execute agentic source-to-target transformation with continuous validation and delivery evidence.

04

Maintain & converge

Use the target graph for impact analysis, onboarding, change, rationalization and consolidation.

Programme-scale proof

Complex transformation across technologies and industries

Representative programme experience spans Java framework modernization, COBOL and AS400 transformation, Delphi desktop-to-web migration, VB.NET monolith re-architecture, and more.

AI-Native Product Engineering

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

AI-Led Legacy Modernization

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

Forward Engineering

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

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.
ASIMOV Case Study_370x208
SaaS / Healthcare

3.2M-line Java framework modernization

Struts, Hibernate and EJB components transformed to Java Spring MVC with automated unit tests and zero user-experience impact.

Read more
Upgrading a Warehouse Management Platform from Java 7 to Java 21 Using ASIMOV
Supply Chain & Logistics

Java 7 to Java 21 Modernization

A global warehouse management provider modernized 1M+ lines of mission-critical Java 7 code using ASIMOV, addressing security risks, performance constraints, and technical debt.

Read more
Modernizing-a-3M-Line-Delphi-Platform-with-ASIMOV-for-a-Leading-European-EdTech-Provider_642x642_1x
Edu Tech

3M-line Delphi to cloud-native .NET 8

Mission-critical desktop estate reconstructed into a scalable web platform with ~60% effort reduction vs manual.

Read more

One intelligence core

Engage at any stage. Retain the same system context

Use ASIMOV for discovery, migration readiness, full modernization, rationalization or continuous maintenance.

AI-Native Product Engineering

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

AI-Led Legacy Modernization

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

Forward Engineering

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

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.
01

Discovery & documentation

Reconstruct application knowledge and make business rules, dependencies and flows reviewable.

02

Migration readiness

Establish scope, target mappings, sequencing, complexity and a representative proof wave.

03

Full modernization

Execute agentic source-to-target transformation with continuous validation and delivery evidence.

04

Maintain & converge

Use the target graph for impact analysis, onboarding, change, rationalization and consolidation.

Proof Points

Documented Smarter Products engagements span brownfield modeling, continuous engineering, and program-scale modernization.

01

Brownfield application intelligence

A 2M+ line codebase modeled in approximately 2-3 weeks; impact analysis across a 1.6M-line application graph completed in about eight minutes.

02

Continuous engineering

93.4% test coverage generated from the model, with 23% fewer defects reported on the same team and codebase after adoption.

03

Modernization at scale

15M+ lines modernized across mainframe, Java, and desktop systems.

Why Accion

People productivity first

We start with knowledge friction, cognitive load, and repetitive effort rather than leading with chatbot or automation features.

Shared enterprise context

Semantic Engineering, SKG, and ECL create reusable grounding so people get more consistent answers across AI experiences. 

Assist first, act where useful

Copilots support knowledge and decisions; SPEX is brought in only where a user interaction needs governed action or deeper engineering. 

Built into existing work

We integrate with the productivity, service, knowledge, and business platforms people already use instead of creating another destination. 

Adoption is part of the solution

KAPS, AI Prism, role-based enablement, and SPEX adoption capabilities connect opportunity selection, user support, evidence, and scale.

Scale behind every engagement

The combined experience of approximately 5,000 engineers and a proven platform set stands behind each rollout, so what works for one team can extend across functions with the same grounding and governance.

Relevant Links

Preserve maturity. Replace technology. Own the outcome

ASIMOV modernizes the application and retains the knowledge required to operate, change and improve it.

Discuss your modernization programme

FAQs

Smarter People is Accion Labs' approach to improving how employees and customers find and use enterprise knowledge, make decisions, complete routine interactions, and adopt AI in their everyday work. It combines enterprise intelligence, role-based copilots, self-service, and workforce enablement on a governed foundation. 

Enterprise AI Strategy & Advisory; AI Copilot & Knowledge Assistants; Customer and Employee Self-service; AI Enablement and Workforce Adoption; Enterprise Intelligence & Knowledge Layer. 

Semantic Engineering structures enterprise context as governed knowledge and relationships. SKG and ECL provide reusable grounding and source evidence, while role-based access, validation, and human oversight keep assistance appropriate to the user and use case.