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  • Validation and Quality gates_v8_1750x875_1x
    ASIMOV Validation Framework

    Generation Creates Output. Validation Creates Confidence

    ASIMOV validates transformed software against approved system understanding, target architecture, engineering standards and behavioral evidence, while making exceptions and decisions visible.

    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

Assurance Principles

Evidence Before Autonomy

Autonomy increases only when the migration system demonstrates reliable performance within a defined scope. Compilation is necessary but insufficient.

Validate against business and architecture intent

Keep uncertainty and exceptions visible

Connect requirements, rules, code, tests and outcomes

Retain accountable human decisions at material gates

Assemble release evidence by scope and specification version

Validation Approach
Validation Approach

02 · AGIE

Build a semantic source graph—not a folder index

Language-aware parsing and enrichment convert code and supporting assets into connected entities, relationships and evidence that agents and reviewers can use.

ASF

The developer improves the factory—not each converted file Turn knowledge and target intent into specification-as-code

The ASIMOV Specification Format connects selected scope, source-to-target mappings, architectural constraints, approved business deltas, transformation rules, test expectations and acceptance criteria.

Process Intelligence and Discovery

Discover

Retain, modify, replace, consolidate or retire decisions

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.

Ten Quality Gates

Control quality from input readiness to release evidence

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

Input & Ingestion Readiness

Confirm repositories, schemas, jobs, interfaces, documents, dependencies and test assets are available and usable.

02

Graph Coverage & Integrity

Measure parser success, unresolved calls, data linkage, component coverage and high-risk unknowns.

03

Functional Understanding

Review extracted functions, business rules, calculations, workflows and exceptions with evidence and SMEs.

04

Target Blueprint & Scope

Approve target components, mappings, service boundaries, architecture constraints and migration decisions.

05

Generated-Code & Build

Check compilation, dependencies, static analysis, security policy and target-platform conformance.

06

Unit & Component

Validate transformed functions, services, jobs, rules, edge conditions, errors and contracts.

07

Integration & Data Reconciliation

Validate APIs, messages, jobs, files, field mappings, data outcomes, retries and downstream effects.

08

Regression & Behavior

Compare critical journeys, calculations, permissions, reports and exception paths against source behavior and approved deltas.

09

Non-Functional Readiness

Confirm performance, resilience, observability, security, privacy, deployment, rollback and support readiness.

10

Release Evidence & Approval

Assemble scope, ASF version, target blueprint, artifacts, tests, reconciliation, exceptions and approvals.

Two Truths

Validate Both Structural Fidelity and Behavioral Fidelity 

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.

Structural Truth

Dependencies, architecture, interfaces, data relationships, build integrity and platform conformance.

Behavioral Truth

Business rules, calculations, process outcomes, permissions, data results, journeys and intentional deltas.

Accelerated Development with Verified Legacy Modernization
Accelerated Development with Verified Legacy Modernization

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

Review a Validation Feedback Loop on Your Own Representative Module

A proof wave establishes real automation, exception and assurance rates before program-scale execution.

Review a Validation Feedback Loop on Your Own Representative Module

A proof wave establishes real automation, exception and assurance rates before program-scale execution.

Review Validation in Action

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.