AI Toolbox

Choosing the right AI software for your project

There are multiple AI tools for video, business, security, image, automation, sales, customer service, bots etc. and you should choose based on several criteria and using quality and accuracy measures. For choosing the right AI software for your project you must consider data preparation, model updates, adjustments, scalability, integration, customized support and transparent pricing. Them are tools like Jasper.ai, Dante AI, GPT Excel, HubSpot, Apollo.io, ClickUp, Doodle, Image-Gen, Indy, Bubble, Semrush, SIRI, Google Assistant, Amazon Alexa, Shutterstock, Adobe Premiere Pro, DALL-E, Firefly, Grammarly, Namelix, Hiver, Zapier etc.

AI tools

ChatGPT: Developed by OpenAI Inc. from San Francisco. ChatGPT now generates 82.14% of AI traffic on desktop computers and records more than 50 million visits per month according to Semrush Analytics. ChatGPT is characterized by advanced conversational interaction that enables the creation of coherent texts, maintaining conversations and providing access to up-to-date information. Worldwide visits amount to more than 600 million visitors per month. Visit Website >>>

Copilot: Microsoft Copilot is a generative artificial intelligence chatbot developed by Microsoft. Based on the GPT-4 series of large language models. In addition to the chat application for consultations, Copilot can also be integrated into office automation solutions to facilitate processes. Visit Website >>>

Gemini: (formerly Bard): Developed by Google, it is the second most popular chatbot. It is characterized by a focus on democratizing information and integration with search functions and support for text, images and audio. Visit Website >>>

MidJourney: Created and hosted by the San Francisco-based independent research lab Midjourney, Inc., MidJourney is an image generation tool that is very popular due to its artistic quality and allows users to generate images from natural language descriptions. Similar to DALL-E by OpenAI or Stable Diffusion by Stability AI. Visit Website >>>

Claude: Developed by Anthropic, one of the most widely used chatbots. If you are focused on IA security and ethics, Claude will provide you with effective conversational partners. Visit Website >>>

Perplexity AI: is a conversational search engine that uses large language models (LLMs) to answer queries. Perplexity AI Inc. is based in San Francisco, California. Backed by Amazon and leading AI chipmaker Nvidia, Perplexity launched a shopping hub in mid-November 2024 to generate leads. Visit Website >>>

Synthesia: This model from Synthesia Ltd in London has a large capacity to generate educational videos. You can use it to create real avatars and deliver them in 140 languages. Visit Website >>>

Adobe Firefly: is a software generated by Adobe to generate images, edit existing photos, apply artistic styles, create social media content, flyers, and more using text descriptions. Visit Website >>>

Character.ai: is a neural language model chatbot service that can generate human-like text responses and participate in contextual conversation. Visit Website >>>

QuillBot: Editing and rewriting tool that uses AI to improve style and grammar. QuillBot boost productivity—without sacrificing authenticity. Visit Website >>>

Runway: specializes in video generation and allows users to create audiovisual content using AI. Explore endless variations of everything you create. Change your location, tweak the lighting, recast a character. Runway bridge the gap between concept and execution. Visit Website >>>

Copy.ai: A tool for creating content, generate pipeline, execcute campaigns and close deals for blogs, social media, and e-commerce. Visit Website >>>

Grammarly: Grammarly: Grammarly, the AI-powered writing partner, offers real-time grammar corrections and style suggestions to help you find the right words - to write that tricky email, get your point across, and move your work forward. Visit Website >>>

Murf.ai: With diverse, lifelike AI voices and multi-language support, Murf simplifies the process of creating professional voiceovers for your creative projects. Bring your ideas to life instantly and enrich your content with realistic audio that engages your audience for podcasts or customer support. Visit Website >>>

DALE-E2, DALE-E3: Are computer programs developed by OpenAI that can create images from text descriptions based on machine learning. Visit Website >>>

Selection criteria

• Accuracy and quality of responses.
• Performance on specific tasks.
• Integration with existing systems.
• Ethical and bias considerations.
• Flexibility and customization
  
  

Evaluate accuracy and quality

• Relevance of generated content.
• Coherence and fluency.
• Factual accuracy.
• Security and risk mitigation.
• Robustness to variations
  
  

Unlock the Power of AI projects

Obtain data that represents the business, services, or products: To develop machine learning models for a specific use case, data managers must obtain data that is both representative of the business and relevant to a use case.

Continuous data collection: To ensure optimal performance of AI solutions, models must be constantly fed with trusted, relevant and up-to-date internal and external data. Real-time and batch processing data updates models and allows data engineers to correct models as needed. To illustrate, a model trained to recognize motorcycles may have difficulty recognizing trike motorcycles until it is fed with images of trike motorcycles.

Curate, label and certify: Use business object descriptions and naming conventions to ensure data is fully contextualized for specific use cases.

Transform and prepare data: Transforming and preparing data are critical steps to make it more usable and relevant for AI applications. To ensure transparency, traceability and sharing of data preparation and transformations, data scientists and engineers must work together. Teamwork helps to coordinate transformations, be it in the data flow or within the AI ​​model itself.

Test and train the model: Trust in AI solutions is paramount for their successful adoption and use. To build trust, companies need to thoroughly test and train their models. AI testing must include not only the data services and models, but also the business logic and governance aspects and ethics. Biases must be identified and any risks associated with the solution must be mitigated.

Ensuring speed and scale of AI deployment: Turning a minimal viable product with a limited release into a more fully-fledged product with a wider release is very difficult, but scaling is possible when all data scientists, data engineers, and other stakeholders align and orchestrate their activities. These are parameters that many low-cost models are likely to fail on in the future.

Continuously develop, integrate and deploy new intelligence capabilities: For AI to be successful in the long term, it is critical to foster a culture of continuous development, integration and deployment of new intelligence capabilities. Companies should have the agility to act dynamically to achieve desired results.

Monitor the performance of their AI solutions: Companies should constantly monitor the performance of their AI solutions and use real-time and batch processing techniques to adapt immediately to changing circumstances. In this way, AI-driven decision-making processes can be optimized and market disruptions can be anticipated in order to always stay one step ahead of the competition.

Continuously develop, integrate and deploy new intelligence capabilities: For AI to be successful in the long term, it is critical to foster a culture of continuous development, integration and deployment of new intelligence capabilities. Companies should have the agility to act dynamically to achieve desired results.