BanglaGuard

BanglaGuard is a Bangladesh-aware safety agent for Bangla and Banglish chatbots. The problem: When a Bangladeshi user asks a chatbot for help after a bKash scam, online blackmail or abuse, Meta's Muse Spark Open is safe and helpful, but 18.6% of its replies contained phone numbers that aren't verified Bangladeshi contacts, including Indian helplines like 1930, 1091 and 181. A standard safety guard doesn't catch this, because a wrong phone number isn't "unsafe content", and it turns helpful answers into bare refusals (only 11% helpful). What I built during the hackathon: - A tool-calling agent (classify_intent, lookup_verified_resources, verify_numbers, crisis_route) that routes each message as normal, help-seeking, harmful or crisis. - The model never writes phone numbers. Contacts come only from a hand-verified list of Bangladeshi resources (bKash 16247, 109, 1098, Police Cyber Support for Women, consumer rights 16121, 999), each with its source. A final filter removes anything else. - A crisis route that skips the model and gives calm steps and 999. - Replies in the user's own script (Bangla or Banglish). - An OpenAI-compatible server (change one base_url), a Gradio demo ("DemoPay", a fictional mobile-banking support chat) and a report card. Results on 1,703 requests (BanglaSafe, MultiJail, 172 legitimate Bengali requests incl. formal journalism, 20 help-seeking cases): - Unverified numbers: 18.6% (raw) -> 4.2% (standard guard) -> 0.1% (BanglaGuard) - Helpful redirect: 98% (raw), 11% (standard guard), 86% (BanglaGuard) - Unsafe: 2.1% / 1.3% / 1.7% (BanglaGuard matches the guard; intervals overlap) - Over-refusal of legitimate requests: 0% for all Limitations: one response model, an LLM judge without human validation yet, and helplines need periodic re-verification.