AptlyStar

AptlyStar is a no-code platform for building, training and launching AI agents. Businesses use it to create agents for customer support, HR, sales, course training and employee onboarding without writing code. AptlyStar is industry-agnostic, with sectors such as finance and insurance, healthcare, retail and eCommerce, education, real estate, travel and hospitality, and human resources, and it serves startups, enterprises, government bodies and nonprofits.

Building an agent in AptlyStar takes three steps: choose an AI model, upload training data or connect a knowledge base, then create and deploy the agent. Agents can draw on files such as PDF, Word, Excel, CSV and JSON, public websites and sitemaps, cloud storage including Google Drive, Dropbox and AWS S3, databases such as PostgreSQL and MongoDB, and tools such as SharePoint, Notion and Confluence. Retrieval-augmented generation grounds answers in that private data.

AptlyStar offers support for 49+ LLMs, among them GPT-4, Claude 3, Mistral, Groq, Google Gemini, Azure OpenAI and Llama models, and lets teams test models side by side on speed, accuracy and cost. Other capabilities include scheduled agent retraining as sources change, a library of pre-built agent templates, image understanding, real-time web search in chat, brand customisation of agents, and real-time logs and analytics on usage, accuracy and errors.

AptlyStar agents can be embedded on websites, connected to Discord, Telegram and WhatsApp, or reached through an API, and workspaces support teams with role-based access. Prompts are anonymised and field-level encrypted before they reach LLM providers, and private inference modes keep data inside the customer's cloud. Plans are Starter at $20.00/month, Scale at $60.00/month and Growth at $200.00/month, with caps on Q/A volume, team members and data size, plus a free trial.

Use cases

• Support agents that answer routine questions such as order status, password resets and appointment scheduling
• HR, sales and employee training agents built on company documents
• Marketing teams searching past campaign material in natural language
• Answering brand tone and messaging questions from brand guidelines
• Summarising campaign performance metrics on request
• Comparing language models to choose one for cost and performance