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  • ASIMOV Enagement Model_v8_1750x875_1x
    Evidence-Led Program Formation

    Start With Evidence. Prove the Factory. Scale With Accountability

    ASIMOV engagements establish fit, reconstruct the system, prove an end-to-end migration wave and then scale through a governed modernization factory.

    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. 

Five Stages of an ASIMOV Engagement

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.

Fitment & Alignment

Confirm business objective, source estate, target direction, evidence, deployment and success criteria.

Intelligence Baseline

Build the source graph, functional understanding, dependency views and risk profile.

Controlled Proof Wave

Transform a bounded representative module and measure automation, exceptions and quality.

Modernization Factory

Scale across modules and waves using shared patterns, quality gates and programme governance.

Operate & Transfer

Refresh target intelligence, train customer teams and transition ownership at an agreed pace.

Representative Proof Wave

Meaningful Enough to Expose Risk. Bounded Enough to Validate

The proof module should represent the source patterns, dependencies, target architecture and validation difficulty that matter at scale.

Clear but non-trivial module boundary

Representative business rules and data interactions

Known upstream and downstream dependencies

Available build, test and reconciliation path

Relevant target architecture patterns

Acceptable operational and release risk

methodology
methodology

Shared Accountability

ASIMOV Owns the Modernization System. You Own Business and Production Authority

Delivery succeeds when target decisions, evidence, access, environments and acceptance responsibilities are explicit.

Customer responsibilities

  • Provide approved source, environments and supporting artifacts

  • Define target architecture and non-functional constraints

  • Nominate application and business SMEs

  • Approve critical rules, deltas, exceptions and production release

  • Provide representative data and acceptance environments

ASIMOV team responsibilities

  • Deploy and configure the modernization platform

  • Build the system intelligence and specifications

  • Operate agentic transformation and validation

  • Manage issues, exceptions, traceability and evidence

  • Deliver target outputs and support knowledge transfer

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.

Start a Fitment Workshop

Bring One Application, Its Target Direction and the Constraints That Matter

The workshop identifies the right entry point: documentation, migration readiness, proof wave, full modernization, rationalization or Maintain.

The first deliverable is a clear fitment decision and evidence plan, specific to the source application and target direction.

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.

What We Enable

Enterprise AI Strategy & Advisory

Enterprise AI Strategy & Advisory

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

Read more →
AI Copilot & Knowledge Assistants

AI Copilot & Knowledge Assistants

Help people find, synthesize, compare, and apply enterprise knowledge with role-aware, source-grounded assistance.

Read more →
Customer and Employee Self-service

Customer and Employee Self-service

Make routine service easier for users and lighter for service teams through grounded answers, approved actions, and context-aware human handoff. 

Read more →
AI Enablement and Workforce Adoption

AI Enablement and Workforce Adoption

Redesign how roles work with AI, build practical skills and confidence, and measure whether adoption is creating real productivity value.

Read more →
Enterprise Intelligence & Knowledge Layer

Enterprise Intelligence & Knowledge Layer

Create reusable enterprise context so people and the AI assistants they use work from connected, governed organizational knowledge. 

Read 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.

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

Identify where trusted AI can remove the most cognitive load and improve how your people work.

Request for Services

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.