Emerging Technology Solutions
Capability scope for Emerging Technology Solutions: Business fit, maturity, dependency, and readiness assessment
Explore Emerging Technology SolutionsComplete service directory
AARHIT Service Directory
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AARHIT Systems
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Browse by capability pillar
Capability pillar
Advance Technology
Explore unfamiliar technologies, modernize connected services, engineer intelligent products, and place cloud, edge, and digital twin capabilities where they can be operated responsibly.
Open pillar guideDigital Transformation
Decision question for Digital Transformation: Which journeys and operational constraints should be addressed first
Explore Digital TransformationIntelligent Product Engineering
Expected output for Intelligent Product Engineering: Product requirements, user flows, and acceptance criteria
Explore Intelligent Product EngineeringCloud-Based AI Solutions
Evaluation signal for Cloud-Based AI Solutions: Cost per accepted workload or completed transaction
Explore Cloud-Based AI SolutionsInternet of Things and Edge AI
Evaluation signal for Internet of Things and Edge AI: End-to-end latency under device operating constraints
Explore Internet of Things and Edge AIDigital Twin Development
Expected output for Digital Twin Development: Functional twin prototype with governed data connections
Explore Digital Twin DevelopmentCapability pillar
Automation
Redesign and coordinate work through rules, workflows, bots, models, agents, integrations, controls, and accountable human review.
Open pillar guideIntelligent Process Automation
Evaluation signal for Intelligent Process Automation: Error, control-adherence, and recovery performance
Explore Intelligent Process AutomationRobotic Process Automation
Capability scope for Robotic Process Automation: Application interaction, data validation, and queue orchestration
Explore Robotic Process AutomationAI Workflow Automation
Operating consideration for AI Workflow Automation: Model terms, data handling, version changes, and provider dependency
Explore AI Workflow AutomationAgentic AI and Autonomous Agents
Decision question for Agentic AI and Autonomous Agents: Which multi-step tasks justify agent-based execution
Explore Agentic AI and Autonomous AgentsBusiness Process Automation
Capability scope for Business Process Automation: System integration, notification, task, and records orchestration
Explore Business Process AutomationIndustrial Automation
Capability scope for Industrial Automation: Control logic, simulation, test-rig, and sequence engineering
Explore Industrial AutomationIntelligent Document Processing
Safeguard focus for Intelligent Document Processing: Field-level confidence thresholds and human verification
Explore Intelligent Document ProcessingDevOps and Infrastructure Automation
Expected output for DevOps and Infrastructure Automation: Reusable infrastructure modules and environment baseline
Explore DevOps and Infrastructure AutomationCapability pillar
Research
Turn uncertainty into evidence through applied inquiry, controlled experiments, feasibility studies, prototypes, model evaluation, and reproducible technical learning.
Open pillar guideApplied Artificial Intelligence Research
Capability scope for Applied Artificial Intelligence Research: Research question and hypothesis formulation
Explore Applied Artificial Intelligence ResearchMachine Learning Research
Expected output for Machine Learning Research: Research report with error analysis and constraints
Explore Machine Learning ResearchAI Proof-of-Concept Development
Expected output for AI Proof-of-Concept Development: Architecture note with dependencies and limitations
Explore AI Proof-of-Concept DevelopmentTechnology Feasibility Studies
Decision question for Technology Feasibility Studies: Which prerequisites must be resolved before investment
Explore Technology Feasibility StudiesAI Model Evaluation and Optimization
Decision question for AI Model Evaluation and Optimization: Which model and configuration best fit the operating requirement
Explore AI Model Evaluation and OptimizationResearch Prototype and MVP Development
Operating consideration for Research Prototype and MVP Development: Representativeness of pilot users and workflows
Explore Research Prototype and MVP DevelopmentCapability pillar
Hybrid Intelligence
Combine human expertise with language, vision, prediction, knowledge representation, multimodal evidence, and transparent decision support.
Open pillar guideHuman-AI Collaboration Systems
Evaluation signal for Human-AI Collaboration Systems: Effectiveness of override and escalation paths
Explore Human-AI Collaboration SystemsDecision Intelligence Solutions
Safeguard focus for Decision Intelligence Solutions: Preserve input, model, recommendation, approval, and override lineage
Explore Decision Intelligence SolutionsGenerative AI and Large Language Models
Expected output for Generative AI and Large Language Models: Representative evaluation suite and results
Explore Generative AI and Large Language ModelsConversational AI and Virtual Assistants
Safeguard focus for Conversational AI and Virtual Assistants: Provide visible handoff paths and preserve transfer context
Explore Conversational AI and Virtual AssistantsComputer Vision Solutions
Capability scope for Computer Vision Solutions: Cloud, edge, and device inference engineering
Explore Computer Vision SolutionsNatural Language Processing
Capability scope for Natural Language Processing: Summarization, normalization, and multilingual processing
Explore Natural Language ProcessingPredictive Analytics
Capability scope for Predictive Analytics: Feature engineering and external signal assessment
Explore Predictive AnalyticsKnowledge-Based and Expert Systems
Expected output for Knowledge-Based and Expert Systems: Structured ontology or domain knowledge model
Explore Knowledge-Based and Expert SystemsMultimodal AI Solutions
Expected output for Multimodal AI Solutions: Evaluation report with failure and fallback guidance
Explore Multimodal AI SolutionsCapability pillar
Technology and Engineering
Build the applications, data foundations, interfaces, infrastructure, quality controls, operating pipelines, and governance required for dependable AI.
Open pillar guideCustom AI Software Development
Decision question for Custom AI Software Development: Where the system boundary and human approval points should sit
Explore Custom AI Software DevelopmentAI Application Development
Decision question for AI Application Development: Which proposed actions require review or confirmation
Explore AI Application DevelopmentAI API and Systems Integration
Evaluation signal for AI API and Systems Integration: Duplicate or incomplete transaction count
Explore AI API and Systems IntegrationData Engineering and Analytics
Operating consideration for Data Engineering and Analytics: Ownership of data definitions and quality exceptions
Explore Data Engineering and AnalyticsMachine Learning Operations
Decision question for Machine Learning Operations: Which production changes should trigger investigation or reevaluation
Explore Machine Learning OperationsCloud AI Infrastructure
Expected output for Cloud AI Infrastructure: Monitoring, recovery, and cost-management runbook
Explore Cloud AI InfrastructureAI Security, Governance and Ethics
Evaluation signal for AI Security, Governance and Ethics: Unresolved high-risk finding count
Explore AI Security, Governance and EthicsAI Strategy and Technology Consulting
Decision question for AI Strategy and Technology Consulting: Which AI opportunities warrant investigation or investment
Explore AI Strategy and Technology ConsultingAutomation Testing and Quality Engineering
Evaluation signal for Automation Testing and Quality Engineering: Flaky test rate
Explore Automation Testing and Quality EngineeringDiscuss the service combination your initiative needs.
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