Agentic AI for enterprise workflows
Autonomous, goal-driven AI execution
Design, build, and operationalize agentic AI workflows that move from insight to action across the enterprise.
- Apply Agentic AI to complex, cross-domain business workflows
- Transform rule-based and human-driven processes into agent-orchestrated workflows
- Enable autonomous decision-making with human-in-the-loop control
- Orchestrate GenAI, ML models, analytics, and enterprise systems end-to-end
- Embed safety, governance, observability, and compliance by design
Pre-built agentic accelerators:
- Retrieval-Augmented Generation (RAG) accelerator
- Enterprise Knowledge Base accelerator
- LLM safety, governance, and guardrails accelerator
- Integrated GenBI accelerator for conversational analytics
- Document Intelligence accelerator
- MLflow integration accelerator for model lifecycle management
Analytics strategy for GenAI and ML
Assessing enterprise analytics for Generative AI and Machine Learning
Build the foundation to evolve from traditional analytics to AI-driven, learning-based decision systems.
- Assess the current state of enterprise analytics, data platforms, and pipelines
- Modernize rule-based systems to enable learning-driven decision-making
- Define roadmaps for transitioning to AI/ML-powered analytics
- Enable ML lifecycle, validation, and governance best practices
- Recommend upgrades to modern, cloud-ready or hybrid technology stacks
Tools and enablers:
- GenAI Innovation Labs: Build a production-ready GenAI solutions in under 6 weeks
- Business alignment on high-value use cases
- Exploratory data analysis and feature engineering
- Performance metrics and model validation
- Solution: Schema matching and intelligent data integration
Generative AI
Large Language Models (LLMs) for automation and innovation
Apply enterprise-grade GenAI to automate workflows, improve productivity, and unlock innovation.
- Automate enterprise processes across customer experience, contact centers, reporting, and IT workflows using LLMs
- Scale GenAI applications using LLMOps
- Solve domain-specific business problems using enterprise LLM frameworks
- Fine-tune and adapt models using Reinforcement Learning with Human Feedback (RLHF)
- Design robust prompt strategies for accurate, contextual, and auditable outputs
- Ensure safety, quality, and response consistency using enterprise guardrails
Tools and enablers:
- GenAI Innovation Labs: Production-ready GenAI solutions in under 6 weeks
- LeapLogic™: Automated cloud modernization and migration accelerator
- Choice of enterprise LLMs, embeddings, and vector databases
- Best practices for secure and responsible GenAI adoption
Analytics & Business Intelligence
Foundational, human-led insights
Deliver descriptive and diagnostic analytics through dashboards, reporting, and visualization to support human-led decision-making.
- Discover business value through advanced data exploration
- Identify and prioritize data-driven use cases
- Generate and visualize insights through intuitive dashboards and analytics
Tools and enablers:
- Python, Power BI, Tableau, Amazon QuickSight
- Near-real-time insights for proactive decisions
- Business alignment on feature stores and analytical models
Model creation and scaling
Scalable model development with GenAI and ML
Accelerate model development and scaling using modern ML engineering practices.
- Identify data patterns and features for effective model selection
- Build and refine models through iterative experimentation
- Implement pipelines for training, validation, and deployment
- Apply best practices for model performance optimization
Tools and enablers:
- Feature engineering and management for structured and unstructured data
- Business alignment on data sourcing and data quality improvement
- Collaborative model development with version control and lineage
- Solution: Data & AI Labs
MLOps
AI and ML lifecycle management at enterprise scale
Operationalize ML and GenAI with reliable deployment, monitoring, and governance.
- Enable best practices for production deployment and model management
- Detect data and model drift using statistical and performance-based indicators
- Maintain model accuracy through automated, event-driven retraining
- Support cloud and hybrid inference scenarios
- Enable collaboration with shared metadata, metrics, lineage, and audit trails
Tools and enablers:
- Engineering blueprints and deployment patterns
- CI/CD for AI and ML
- A/B testing and controlled experimentation frameworks
Agentic Analyst
AI-powered decision intelligence for business teams
Move from insights to action with AI-driven Decision Intelligence embedded directly into enterprise workflows.
- Conversational access to enterprise data, KPIs, and metrics
- Context-aware reasoning across analytics and operational signals
- Insight generation, anomaly detection, and action recommendations
- Human-in-the-loop decision support with full traceability
- Secure, role-based governance and auditability
Impetus services:
- Agentic Analyst blueprints
- Integration with enterprise BI platforms and semantic layers
- Governance, access control, and observability powered by Impetus Prism™
