AI Add-On Integration Docs
Budget: ₹1,500 – ₹12,500 INR
I run an established RP digital-signage and appointment-booking platform and now want to bolt on an AI agent. The agent must combine predictive analysis with voice recognition so users can simply speak to:
• schedule appointments
• retrieve real-time information
• receive basic customer support
Your task is to create thorough, developer-ready documentation that shows exactly how to weave this agent into the current stack. I’m looking for a pragmatic, no-fluff guide that covers architecture, data flow, API touchpoints, error handling, and security considerations so my in-house team can implement the integration without guesswork.
Acceptance criteria
1. High-level architecture diagram illustrating where the AI layer sits alongside our signage and booking modules.
2. Step-by-step integration walkthrough, including authentication, voice capture pipeline, predictive analysis endpoints, and callback events for the three voice-driven actions above.
3. Sample code snippets (REST/JSON preferred) for registering voice commands, invoking predictive models, and updating the appointment ledger.
4. Data schema or field mapping for any new tables or objects introduced.
5. Test scenarios with expected results to confirm scheduling accuracy, information retrieval speed, and support responses.
6. Clear explanation of any third-party libraries, SDKs, or model hosting services required.
Keep the language concise, assume a mid-level developer audience, and deliver the document in Markdown or PDF so it can drop straight into our knowledge base.
• schedule appointments
• retrieve real-time information
• receive basic customer support
Your task is to create thorough, developer-ready documentation that shows exactly how to weave this agent into the current stack. I’m looking for a pragmatic, no-fluff guide that covers architecture, data flow, API touchpoints, error handling, and security considerations so my in-house team can implement the integration without guesswork.
Acceptance criteria
1. High-level architecture diagram illustrating where the AI layer sits alongside our signage and booking modules.
2. Step-by-step integration walkthrough, including authentication, voice capture pipeline, predictive analysis endpoints, and callback events for the three voice-driven actions above.
3. Sample code snippets (REST/JSON preferred) for registering voice commands, invoking predictive models, and updating the appointment ledger.
4. Data schema or field mapping for any new tables or objects introduced.
5. Test scenarios with expected results to confirm scheduling accuracy, information retrieval speed, and support responses.
6. Clear explanation of any third-party libraries, SDKs, or model hosting services required.
Keep the language concise, assume a mid-level developer audience, and deliver the document in Markdown or PDF so it can drop straight into our knowledge base.