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  • ASIMOV Deloyment options_v8_1750x875_1x
    Deployment Options

    Deploy ASIMOV Where Your Code, Models and Controls Require It

    Choose an ASIMOV-managed environment, customer cloud, hybrid architecture or restricted on-premises deployment based on trust boundaries, model policy, infrastructure and operational ownership.

    Explore How ASIMOV Works Request a Fitment Workshop

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. 

Deployment Principles

Keep Source Code, Models, Artifacts and Evidence Within the Approved Boundary

Deployment architecture is matched to data sensitivity, residency, network constraints, model approval, scale, latency and operating responsibility.

ASIMOV-managed

Fast initiation in an isolated, secure project environment with controlled repository integration and agreed retention.

Customer Cloud

ASIMOV runs in the customer subscription or private tenant with enterprise IAM, networking, storage and monitoring.

Hybrid

Separate model inference, orchestration, graph, source storage and validation according to sensitivity and policy.

On-premises / Restricted

Run agents, models, graph and build tooling inside a customer-controlled or air-gapped ecosystem.

Reference Architectures

Four Deployment Patterns 

Client Cloud with ASIMOV
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Client Cloud
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Client On-Premises
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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

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

Security and Governance

Design the Trust Boundary Before the Migration Factory

Every deployment should explicitly define access, model endpoints, storage, logs, network routes, secrets, code review, incident handling and platform operations.

Data Masking and Access Control1

Identity & Access

Enterprise IAM, role-based permissions, repository controls, approval workflows and auditable actions.

monitor

Data & Artifact Control

Encryption, customer-controlled storage, retention and deletion policies, keys and source-code boundaries.

model-1

Model Governance

Approved endpoints or locally hosted models, prompt/output logging, updates, safety and performance controls.

SaaS Agentification & Embedded AI Features

Network & Integration

Private endpoints, restricted egress, CI/CD, build environments, test platforms and monitoring.

Governace security and access

Generated-code Governance

Security scanning, third-party component policy, code review, repository ownership and release gates.

SAP to S-4HANA upgrade and cloud migration@2x

Operating Responsibility

Clear ownership for infrastructure, platform support, upgrades, incidents, evidence and handover.

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

Design the Deployment Around the Application’s Trust Boundary and Delivery Model

A deployment workshop confirms source-code movement, model policy, network design, integration, infrastructure sizing and ownership.

Design a Deployment Model

Design the Deployment Around the Application’s Trust Boundary and Delivery Model

A deployment workshop confirms source-code movement, model policy, network design, integration, infrastructure sizing and ownership.

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.