Applied AI
Build and modernize business applications with governed AI.
Apply generative and agentic AI across enterprise data, workflows, integrations and software—using the models and tools that fit the business, security and operating context.
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Applied AI for ERP and everyday business work
See how AI can assist ERP implementation, connect business workflows and improve applications, with specialist validation and clear ownership.
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Where applied AI fits
Start with a business application or workflow—not a preferred tool.
Aurosten supports business-critical applications across ERP, CRM, finance, operations, workflow, integration, legacy and custom platforms. AI is selected only where it adds a supportable capability.
ERP, CRM and operational data exists, but its definitions, permissions and quality are not ready for dependable AI use.
Teams need reliable knowledge search, document processing, summarization or decision support inside daily work.
A multi-step workflow could be handled by an agent, but tool access, approvals, exceptions and audit evidence need design.
An existing business application needs an intelligent capability without weakening its security, controls or supportability.
A new internal application, portal or workflow system needs AI as part of the architecture rather than as an isolated demonstration.
A large or inherited codebase needs faster orientation, documentation, testing and controlled modernization.
Two forms of AI—different operating risks
Generate useful output. Or execute bounded work.
Generative AI produces content from approved context. Agentic AI coordinates actions across approved tools and systems. Both require data boundaries, evaluation and named human authority; agents additionally require action limits and recovery controls.
Generative AI
Search, summarize, extract, classify and draft using governed enterprise data, documents and operational context—with grounding and measurable acceptance criteria.
Agentic AI
Plan and perform multi-step work through approved APIs and tools—with permissions, checkpoints, state, audit, escalation and safe shutdown designed in from the start.
Applied AI services
From opportunity assessment to production operation.
Each engagement connects AI to real data, software, operating processes and acceptance evidence instead of stopping at an isolated demonstration.
AI Strategy & Opportunity Assessment
Identify useful, feasible AI opportunities before committing to a model, platform or implementation pattern.
- Business process, application, data and control assessment
- Use-case prioritization by value, risk, cost and readiness
- Build-versus-buy, model, hosting and integration decisions
Enterprise Data & Knowledge Readiness
Prepare ERP, CRM, document and operational data so AI works from governed definitions instead of disconnected extracts.
- Source, semantic, quality, lineage and permission assessment
- Controlled retrieval across transactions, master data, documents and knowledge
- Evaluation datasets, reconciliation rules and access boundaries
Generative AI Solutions
Build grounded assistants and document workflows whose output can be traced, evaluated and reviewed.
- Enterprise knowledge search, summarization and drafting
- Document extraction, classification and validation
- Grounding, citations, evaluation and human review queues
Agentic AI & Workflow Automation
Design agents that perform bounded multi-step work across approved applications, APIs and tools.
- Tool and data permissions, action limits and approval checkpoints
- ERP, CRM, document, ticket, finance and integration workflows
- State, retry, audit, escalation, observability and recovery
AI-Enabled Business Applications
Add intelligent capabilities to existing systems or build new internal applications around a defined operational need.
- ERP, CRM, finance, operations and workflow application enhancements
- Internal portals, role-based tools and decision-support applications
- Secure APIs, user experience, business rules and production integration
AI-Assisted Software & Legacy Modernization
Use suitable AI engineering tools to accelerate software work while experienced engineers retain architecture, review and acceptance responsibility.
- Repository orientation, dependency mapping and technical documentation
- Test generation, feature delivery, refactoring and controlled upgrades
- Codex, GitHub Copilot, Claude Code or another fit-for-purpose tool
AI Operations, Governance & Assurance
Operate AI capabilities with visible ownership, measurable behavior and controlled access to business systems.
- Model and agent evaluation, security and permission boundaries
- Quality, cost, latency, action and exception monitoring
- Human authorization, incident response, rollback and change governance
Technology selection
Platform-neutral by design.
Models and tools are selected for the application, data boundary, security, deployment environment, maintainability and cost. Product names identify options—not partnerships or a mandatory stack.
Models & AI platforms
OpenAI, Azure OpenAI, Microsoft Foundry, Anthropic Claude, Google Gemini, Amazon Bedrock and appropriate open or locally hosted models
Agents & orchestration
OpenAI Agents, Copilot Studio, Semantic Kernel, AutoGen, LangGraph, MCP and custom orchestration
Applications & automation
ERP, CRM, SQL, documents, Power Platform, Boomi, n8n, enterprise APIs and custom business applications
Engineering accelerators
Codex, GitHub Copilot, Claude Code, Gemini Code Assist and other tools selected for the codebase and delivery controls
AI-assisted engineering
Use the right accelerator—not one fixed vendor.
Codex, GitHub Copilot, Claude Code, Gemini Code Assist or another suitable tool may help engineers understand repositories, document dependencies, build tests and deliver controlled changes.
Architecture, access, business-rule interpretation, security, review and production approval remain accountable engineering decisions regardless of the tool selected.
Control model
Every AI capability needs an operating boundary.
Choose for fit
Select models, tools, hosting and architecture from the business need, data boundary, risk and operating environment.
Define authority
Specify what AI may read, generate, propose and execute—and what always requires a person.
Evaluate behavior
Test representative, adverse and exception cases before expanding scope, users or permissions.
Operate visibly
Log actions, monitor quality and cost, own exceptions, and retain recovery and shutdown paths.
Next step
Discuss an applied AI opportunity
Describe the application, data, workflow or codebase—and the outcome that matters.