context.md

Via: Paytm Merchant Copilot - Project Context

1. Problem Statement

SMB merchants on the Paytm platform struggle with payment failures, delayed settlements, manual reconciliation, and invoicing/collections. Existing dashboards are passive, overwhelming, and require proactive monitoring. When things go wrong (e.g., a drop in UPI success rate or a pending settlement), merchants often lack the time or expertise to analyze the data and take immediate action.

2. The Solution: 'Via'

Via is an Actionable Intelligence Agent (an MCP Client) accessed directly through Telegram. It serves as a conversational, voice-first control layer over merchants' Paytm infrastructure. Instead of just being a Q&A chatbot, Via follows a core philosophy of: Insight → Reason → Action → Execution

For example, when asked "Why was yesterday's collection low?", Via doesn't just return a number. It gives the insight (collection dropped by 18%), the reason (UPI success rate dropped), and immediately offers an actionable execution (e.g., "Would you like me to send payment links for the failed orders?").

3. UX & Interface

• Platform: Telegram Bot
• Input Types: Text and Voice (.ogg files)
• Voice UX: SMB merchants (often speaking Hindi or Hinglish) can simply send voice notes like "refund last payment". The bot uses Telegram's voice capabilities smoothly to deliver a natural, low-friction experience.

4. Multi-Model AI Architecture

To balance cost, speed, and intelligence, Via utilizes a layered model stack:

1. Input Layer (Speech-to-Text): whisper-large-v3-turbo
   • Transcribes Telegram .ogg voice notes, excellent for Hinglish natively.
2. Intent & Routing (Fast & Cheap): llama-3.1-8b-instant
   • Rapidly classifies the merchant's intent, extracts entities, and routes queries.
3. Reasoning & Execution (The "Brain"): gpt-oss-20b or Gemini
   • Handles the complex financial logic, analyzes data patterns, generates insights, and formats the final responses.
4. Safety Verification (Optional/Critical Tasks): gpt-oss-safeguard-20b
   • Ensures sensitive operations like "initiate refund" are double-checked for authorization and intent.

5. Backend Architecture & Tool Integration

• Stack: Python, FastAPI, python-telegram-bot.
• MCP Mock Tools (Simulating Paytm APIs):
  • Orders: fetch_order_list
  • Payment Links: create_link, fetch_link, fetch_transaction
  • Refunds: initiate_refund, check_refund_status, fetch_refund_list
  • Settlements: get_settlement_summary, get_settlement_detail

Note: The backend avoids heavy frameworks like React in favor of a clean, instantly demo-able chat interface that highlights the actual value: the MCP tool-calling and the multi-model intelligence.

6. Conversational Memory & Context Awareness

To ensure Via is not a stateless bot, it maintains a rolling conversational memory per user (using Telegram's chat_id):

• Short-Term Memory (Session State): A fast, in-memory buffer (e.g., Python dict or Redis) stores the last 20 interactions or can store the summary till this point in chat of all the conversations. This payload is passed to the LLM's messages array so the AI always remembers the tone of the user, recent tool results, and previously asked questions.

• Tool-Call Continuity: If a user says "Refund the last one we talked about," the AI can inspect the most recent initiate_refund or fetch_transaction MCP tool execution to determine precisely which order the user meant.Just ensure that its not stale data... like time wise... can use last 5 mins

• Context Injection: When an order or settlement requires action, the backend injects system prompts framing the exact state: "The user just saw a drop in UPI success; guide them to routing updates."

• Sensitive stuff redirect to relevent section of paytm app, so that user continues from there...

7. Voice UX & Hinglish Support