Artificial Intelligence Assessment and Strategy, Implementations, Maintenance and Support Services

The Vendor is required to provide artificial intelligence assessment and strategy, implementations, maintenance and support services for include:
- Assessment and strategy services
•    Contractors may conduct interviews with the business and it stakeholders and hold multiple workshops where ideas can be discussed and evolved.
•    Business value mapping: identifying core pain points and mapping them to AI solutions.
•    Contractors may conduct an evaluation of existing infrastructure and provide recommendations for supporting future AI development, support, and growth.
•    Infrastructure and stack review: determining if existing cloud or on-prem systems can handle the compute and latency requirements of large language models (LLMS).
•    Ai maturity and readiness assessment: comprehensive audit of current data infrastructure and technical capabilities to determine organizational readiness for AI adoption.
•    Data readiness: assessing the quality, volume, accessibility, and lineage of data required for model training. 
•    Data architecture and quality evaluation: analysis of data lineage, availability, and cleansing requirements to ensure high-fidelity datasets suitable for model training and fine-tuning.
•    Technology: inventorying current AI and ML platforms, computer infrastructure, and supporting tools (e.g., feature stores, vector databases).
•    People and culture: determining staff knowledge, skills, and abilities in AI engineering, and responsible AI (rai). 
•    Assessments performed must leverage industry-recognized strategy and capability maturity domain frameworks, focusing on the typical environment elements: people (culture, organization, roles and responsibilities), technology (tools, platforms, deliverables), and process.
•    Compliance mapping: ensuring the roadmap accounts for regulations or industry-specific privacy laws (HIPAA).
•    Strategic AI roadmap development: creation of a multi-year execution plan prioritizing high-impact use cases, defining ROI targets, and establishing a resource procurement strategy (build vs. buy).
•    Success metric definition: establishing KPIs such as return on AI, cost-per-interaction, or employee hours saved.
•    Ai governance: recommend governance processes, including identifying roles and responsibilities, to ensure business and it alignment. 
•    Any recommendations for the agency’s AI governance must align with state policies it-17 use of AI in state solutions and it-19 data governance.
•    Conduct AI training and skillset development program to foster data and AI capable culture.
- Managed services
•    Ongoing managed services and enhancements for the upkeep of the solution. 
•    This is vital to continue to evolve with technology.
•    Continuous model monitoring and alerting: implementing real-time monitoring tools to track model metrics (e.g., accuracy, latency, and throughput), data drift, and model drift, with automated alerting for performance degradation.
•    Real-time performance dashboards: interactive dashboards for monitoring model health, drift detection, and business value metrics.
•    Cost monitoring: tracking token usage and "compute burn" to prevent unexpected cloud bills.
•    Model retraining and update management: establishing protocols for triggered or scheduled model retraining based on performance decline or data drift and managing the versioning and rollback capabilities.
•    Reinforcement learning from human feedback: using "thumbs up/down" data from users to fine-tune the model's alignment with human preferences.
•    Security and compliance: ensuring the AI solution complies with all security protocols, access controls, and regulatory requirements (e.g., data privacy, lineage tracking).
•    Optimization: continuous review and optimization of the solution for efficiency, speed, cost, and resource utilization (e.g., optimizing serving infrastructure or inference code).
•    Versioning: managing versions of the model, the data, and the code (prompt) simultaneously
•    Automated testing: running a set of prompts with known good answers against every new model update to ensure no regression in quality.
•    Rollback procedures: immediately revert to a previous stable model version if the new one begins hallucinating or exhibiting toxic behavior.
•    Infrastructure cost optimization: ongoing analysis of utilization to optimize cloud spend and resource allocation.
- AI technologies and application domains
•    Inclusive of all methods and models designed to simulate human intelligence.
•    Including supervised, unsupervised, and reinforcement learning algorithms.
•    Utilizing neural networks for complex tasks like image, text, and speech processing.
•    Models capable of generating novel content, including text (LLMS), images, code, or synthetic data.
•    Generative AI that is given autonomy, tools, and goals to act on its own.
•    The engineering discipline for reliable and efficient deployment, monitoring, and management of ml models in production.
•    Methodologies focused on ensuring AI models are fair, transparent, explainable, and accountable.
•    Implementing robotic process automation integrated with AI for complex decision-making.
•    Uses massive datasets to generate original, human-like responses in real-time rather than pulling from a script.
•    Developing solutions for text classification, sentiment analysis, language translation, and entity recognition.
•    Applications for image and video analysis, object detection, facial recognition, and spatial modeling.
•    Deploying lightweight AI models directly onto devices (sensors, drones, embedded systems).
•    Applying deep learning and machine learning to satellite imagery, maps, and spatial datasets for insights.