What's included

• AI Product Vision & Strategy Document

  Outlines long-term goals, target market, AI-specific value proposition, ethical guidelines, and data strategy for your AI product.

• AI Use Case Prioritization Framework & Report

  Delivers an analysis of potential AI applications, ranked by feasibility, impact, and strategic alignment to guide your investment.

• Data Acquisition & Annotation Plan

  Provides a clear strategy for sourcing, collecting, labeling, and managing the high-quality data crucial for AI model training and validation.

• AI Product Roadmap

  Details specific phases for data preparation, model development, experimentation, validation, deployment, MLOps considerations, and iteration for your AI product.

• AI Model Requirements Document (MRD) / Specifications

  Defines detailed success metrics (e.g., accuracy, F1), input/output data specifications, explainability (XAI) features, and Human-in-the-Loop (HITL) workflows.

• Ethical AI Review & Mitigation Plan

  Presents an assessment of potential ethical risks (e.g., bias, fairness, transparency) in your AI product and proposes actionable mitigation strategies.

• AI Go-to-Market Strategy

  Outlines plans for launching your AI product, including user education, managing expectations around AI capabilities, and market positioning.

• Sprint Plans with AI-Specific User Stories

  Provides clearly defined goals and AI-focused user stories for development sprints (e.g., "As a user, I want the AI to accurately categorize X with Y% precision...").

• A/B Test Plans for AI Features & Model Variations

  Delivers detailed designs for experiments to compare different AI models or AI-driven user experiences to optimize performance.

• Model Validation Reports

  Offers summaries of AI model performance against test datasets and predefined success criteria, ensuring readiness and effectiveness.

• Data Quality Assessment Reports

  Provides an analysis of training and production data for completeness, accuracy, and potential biases that could affect AI model performance.

• Feature Engineering Prioritization

  (In collaboration with data science) Delivers a prioritized list of features to be engineered to enhance AI model accuracy and effectiveness.

• Human-in-the-Loop (HITL) Process Documentation

  Outlines clear workflows for human review, intervention, and feedback in AI-driven decision-making processes.

• AI Model Performance Monitoring Plan & Dashboards

  Establishes KPIs and systems for tracking AI model accuracy, drift, latency, and business impact in a live production environment.

• Bias Detection & Fairness Reports

  Delivers regular assessments of AI model outputs to ensure fairness and mitigate bias across different user segments.

• Model Retraining & Update Plans

  Provides a strategy and schedule for retraining AI models based on performance degradation, new data, or evolving requirements.

• User Feedback Analysis on AI Features

  Offers specific insights derived from user interactions with, and trust in, your AI-powered functionalities to guide improvements.

• Stakeholder Updates on AI Product Performance & Roadmap

  Delivers clear progress reports and presentations tailored to explain AI concepts, performance, and roadmap updates to diverse audiences.

• AI Product Compliance Documentation

  Provides necessary records and documentation demonstrating your AI product's adherence to data privacy and relevant AI regulations or standards.