Sold by: Autonomic AI
Professional service that evaluates AI/ML model candidates against accuracy, latency, energy, and cost criteria to deliver a mathematically proven optimal model function selection.
Overview
Autonomic AI Model Selection (AssuredModel)
Choosing the right AI or machine learning model for production is a high-stakes decision that affects predictive performance, operational cost, latency, reliability, and energy consumption. AssuredModel is a professional service that helps organizations identify the optimal AI/ML model for a defined business purpose, dataset, operating environment, and set of evaluation criteria - using a mathematically proven selection framework rather than ad hoc experimentation.
The Challenge
Organizations evaluating AI/ML models often face a fragmented landscape of candidates with no systematic way to compare them across the dimensions that matter most. Internal teams may lack the time, tooling, or methodology to rigorously assess trade-offs between accuracy, latency, infrastructure cost, energy consumption, and other operational constraints. The result is model choices driven by familiarity or convenience rather than evidence - leading to suboptimal performance, unnecessary cost, or both.
How It Works
AssuredModel applies a structured evaluation methodology to construct and reduce the applicable candidate model space, then evaluate remaining candidates against your defined objectives. The engagement follows a phased approach:
Requirements and Criteria Definition - Collaborate with your team to define the business objectives, evaluation criteria, and operational constraints that will govern model selection. Candidate Model Assessment - Construct the admissible model space based on your requirements, identifying all viable deterministic, stochastic, and hybrid AI/ML model candidates. Controlled Sandbox Evaluation - Conduct comparative performance and efficiency measurements in a controlled environment, testing candidates against your criteria. Model-Space Reduction - Systematically narrow the candidate space using the mathematically proven framework, providing an auditable basis for each elimination decision. Final Model Selection Report - Deliver a comprehensive report describing the selected model, supporting measurements, trade-off analysis, and the rationale behind the recommendation.
What You Can Evaluate
The service supports evaluation across multiple dimensions, including:
- Predictive performance and accuracy
- Inference latency and throughput
- Energy consumption and sustainability impact
- Infrastructure cost on AWS compute
- Reliability and fault tolerance
- Other operational constraints specific to your workload
AssuredModel can evaluate deterministic, stochastic, and hybrid AI/ML models and is designed for use cases where model choice must balance multiple technical and business requirements simultaneously.
Engagement Outcomes
Depending on your needs, the results of an engagement can be used to:
- Select a model for a new production workload where no prior model exists
- Replace an existing model with a better-performing or more cost-efficient alternative
- Validate a current model choice against the broader candidate space with auditable evidence
AWS Integration
This professional service relates to AWS compute and AI/ML workloads, including Amazon EC2 and Amazon Lightsail where applicable to your evaluation or deployment. Customer models and workloads may also use other AWS services based on your architecture and requirements. Initial engagements can be delivered as an evaluation and Model Selection report without requiring deployment of customer workloads to AWS. Where requested, Autonomic AI can use the selection results to support subsequent deployment or integration of the selected model into an AWS-hosted environment.
Energy-Aware Model Selection
In an era of growing concern about AI's environmental footprint, AssuredModel includes energy consumption as a first-class evaluation criterion. Organizations facing sustainability targets or cost pressures from compute-intensive workloads can use this capability to identify models that deliver required performance at lower energy cost.
Why a Mathematically Proven Approach Matters
The AssuredModel framework provides an auditable, repeatable basis for model selection decisions backed by mathematical proof. Rather than relying on informal benchmarks or vendor claims, your organization receives a documented evaluation grounded in controlled measurements and transparent criteria - supporting governance, procurement review, and stakeholder confidence in the chosen model.
Get Started
To begin an engagement, contact Autonomic AI through AWS Marketplace to discuss your model selection requirements and scope a tailored evaluation.
Highlights
- Energy-Aware Model Selection - Evaluate energy consumption as a first-class selection criterion alongside accuracy, latency, cost, and reliability. Rather than selecting models based on a single metric, AssuredModel enables organizations to balance multiple technical and business requirements, identifying models that meet both performance and sustainability objectives for production workloads.
- Mathematically Grounded Optimization - Select an optimal model within a defined candidate space using customer-specified objectives and constraints. This approach provides a rigorous, auditable alternative to ad-hoc model selection based on trial and error, delivering a documented rationale that technical stakeholders and auditors can review and verify.
- Model and Architecture Independent - Evaluate deterministic, stochastic, and hybrid AI/ML models across different architectures and deployment environments. Organizations can compare alternatives against the specific requirements of a production workload, whether selecting a new model, replacing an existing one, or validating a current choice against available options.
Details
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Delivery method
Deployed on AWS
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Support
Vendor support
Technical support is provided for product access, AWS Marketplace entitlement issues, and AssuredModel product implementation. Customers may contact Autonomic AI at jharby@functormodel.ai . Support requests are reviewed during normal U.S. Pacific business hours.
Engagement Phases
AssuredModel engagements follow a structured five-phase process: Requirements and Criteria Definition, Candidate Space Identification, Model-Space Reduction, Optimization and Selection, and Final Model Selection Report delivery. Buyers should plan to collaborate during the Requirements and Criteria Definition phase to establish business objectives, evaluation criteria, and constraints.
Engagement Outcomes
Engagements address three primary scenarios: selecting a model for a new production workload, replacing an existing model with a better-performing alternative, or validating that a current model remains optimal against available options. Each engagement concludes with a Final Model Selection Report that documents the mathematical rationale for the recommendation.
Enterprise Support
Enterprise and separately contracted support arrangements may include additional response commitments and integration assistance as specified in the applicable agreement. For questions about engagement scoping, timelines, or support options, contact Autonomic AI through AWS Marketplace or at the email address above.