Skip to main content

Assisted Intelligence Business

How can businesses use AI to improve their business model?

Use Cases​

Business use cases for GenAI applications:

  • RAG-based chat apps ("chat with proprietary data")
  • Sentiment analysis
  • Summarization
  • Content generation
  • Entity extraction
  • Text classification
  • Image classification
  • Search
  • Translation

Checklist​

Evaluate potential to implement AI into your business model:

  • Strategic Alignment
    • What are the strategic goals of the business? Does AI align with the overarching goals and objectives of the business?
    • What competitive advantage could AI provide? Can AI offer a distinct advantage over competitors, whether through increased efficiency, improved customer service, or innovative products?
  • Business Process and Efficiency
    • Which repetitive tasks consume too much human time? Could these tasks be automated using AI, freeing employees for more value-added work?
    • Where are the bottlenecks in existing processes? Can AI algorithms quicken decision-making or streamline workflow?
  • Customer Interaction and Engagement
    • How can customer experience be improved? Can AI-driven chatbots, recommendation systems, or personalized marketing enhance customer satisfaction and engagement?
    • What data do we have on customer behavior? Can this data be used to train machine learning models to predict future customer actions or preferences?
  • Data Analysis and Decision Making
    • What kinds of data is the business currently collecting, and how is it being used? Can AI provide new insights or create predictive models based on this data?
    • How are business decisions currently being made? Can AI assist in data-driven decision-making or expose new avenues for exploration?
  • Innovation and Product Development
    • Are there new products or services that could be created through AI? Can AI be integrated into the product development lifecycle to spur innovation?
    • How can AI enhance existing products or services? Can AI features add value to current offerings, making them more appealing or effective?
  • Risk Management
    • How can AI improve security measures? Can machine learning algorithms better detect fraudulent activities or security vulnerabilities?
    • What is the business's tolerance for risk in implementing AI? What are the ethical and practical considerations, and how will AI adoption be managed to minimize risks?
  • Cost and ROI
    • What are the costs involved in implementing AI solutions, including training staff? How does this weigh against the projected return on investment (ROI)?
    • What is the timeline for implementation? Is there a need for quick wins, or is the business prepared for a longer, more involved implementation?

Gemini APIs​

By leveraging Gemini's unique capabilities and competitive pricing, startups can build innovative AI-powered solutions across various industries. The key is to align Gemini's strengths with your specific use case and continuously optimize as both your startup and the AI technology evolve.

Overview​

Google offers several Gemini models tailored for different use cases:

  • Gemini Ultra: The most powerful model, currently only available to consumers via Gemini Advanced.
  • Gemini Pro: Best for general-purpose applications across a wide range of tasks. Features a 2 million token context window.
  • Gemini Flash: Optimized for speed and affordability, ideal for scaling up.
  • Gemini Nano: Designed for on-device tasks (e.g. Chrome, Android), with zero cost to developers.

Gemini models can process text, images, audio, and video inputs, producing text or image outputs.

Pricing Comparison​

  • Advanced Models:
    • Gemini 1.5 Pro: $0.0035/1K input tokens, $0.0105/1K output tokens (up to 128K context)
    • GPT-4: $0.03/1K input tokens, $0.06/1K output tokens
    • Claude 3 Opus: $0.015/1K input tokens, $0.075/1K output tokens
  • Efficient Models:
    • Gemini 1.5 Flash: $0.00035/1K input tokens, $0.00105/1K output tokens (up to 128K context)
    • GPT-3.5 Turbo: $0.0005/1K input tokens, $0.0015/1K output tokens
    • Claude Instant: $0.00163/1K input+output tokens

Gemini Flash offers competitive pricing, especially with context caching providing up to 75% cost reduction for repeated prompts or instructions.

Competition​

Gemini vs Competitors:

  1. Text Performance: Gemini Flash performs well on MMLU benchmarks, competitive with GPT-4 and surpassing many alternatives.
  2. Multi-Modal Performance: Gemini excels in processing multiple input types, particularly video analysis.
  3. Code Performance: Gemini Flash currently underperforms compared to competitors like GPT-4 in coding tasks.
  4. Cost: Gemini is competitively priced, with unique features like context caching offering significant cost savings.
  5. Architecture: Gemini uses a mixture-of-experts (MoE) architecture optimized for TPUs, differing from the dense transformer (GPT-4), constitutional AI (Claude), and optimized transformer (LLaMA) approaches of competitors.

Adoption​

Enterprises across various industries are leveraging Gemini:

  1. Content Creation:
    • Walmart: Improving product listing pages at scale
    • Victoria's Secret: AI-powered product recommendations
  2. Coding:
    • Goldman Sachs: 40% increase in developer efficiency
    • Wayfair: Accelerating coding processes
  3. Support Agents:
    • Six Flags: Answering 30% of guest questions
    • Uber: Enhancing human customer support efficiency
  4. Content Summarization:
    • Patreon: Summarizing unread messages

Building Guide​

Building on Gemini: A Step-by-Step Guide

  1. Choose the Right Model:
    • Start with Gemini Pro for versatility during prototyping
    • Consider Gemini Flash for cost-efficient scaling
  2. Leverage Multi-Modal Capabilities:
    • Explore video and image analysis features unique to Gemini
  3. Optimize for Cost:
    • Implement context caching for repeated prompts to reduce costs by up to 75%
  4. Benchmark Against Use Cases:
    • Test Gemini against specific application needs, especially for coding tasks
  5. Explore Enterprise Integrations:
    • Consider how Gemini can enhance existing workflows in content creation, customer support, or development

Startups​

Key Considerations for Startups

  1. Differentiation: Focus on Gemini's strengths in multi-modal processing and competitive pricing.
  2. Scalability: Utilize Gemini Flash for cost-effective scaling as your startup grows.
  3. Integration: Explore how Gemini can augment existing tools and processes in your target industry.
  4. Performance Monitoring: Regularly benchmark Gemini against competitors, as the AI landscape evolves rapidly.
  5. Cost Management: Implement context caching and monitor usage to optimize expenses.