SupportMind AI Agent Brings Persistent Memory to Customer Support Interactions

·3 views

A developer built SupportMind, an AI-powered customer support agent, as part of the HackHyderabad 3.0 hackathon. The tool is designed to retain context from previous customer interactions, allowing it to recall past issues and successful solutions without requiring customers to repeat themselves. SupportMind maintains individual memory profiles for each customer and a shared playbook of proven fixes to ensure consistent responses. The project was built using Python, Streamlit, and Groq, with an LLM-based memory layer powered by Hindsight AI. The developer has open-sourced the project on GitHub and is seeking community feedback.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

AI Desktop Tool Helps Surface Unanswered Questions from Meeting Recordings

OGAD (Off Grid AI Desktop) is a locally run AI tool that allows users to transcribe saved meeting recordings and identify questions that were left unanswered or deferred during the discussion. The tool processes audio files in formats such as MP3, WAV, and M4A using downloaded local models for transcription and text analysis, keeping all data off the cloud. Users can then categorize unresolved questions by status — such as deferred, partly answered, or no answer found — to guide more targeted follow-ups. The workflow is part of OGAD's free core app and is available on Mac, Windows, and Linux in beta. The approach is particularly useful for small teams who need to close gaps before a project or delivery deadline.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

How separating queues and workers fixed silent failures in a headless AI server setup

A developer running always-on AI agents discovered their system appeared healthy while webhook jobs silently piled up unprocessed, with Docker reporting containers as live despite workers being effectively frozen. The root cause was mixing webhook intake, cron scheduling, and heavy LLM execution inside a single process — a setup that works locally but breaks under production load. The fix involved splitting ingress from execution using a Redis queue, routing all LLM calls through a single OpenAI-compatible endpoint, and replacing local storage with PostgreSQL and S3-compatible solutions. A critical additional requirement was making all jobs idempotent, since queue systems like Celery can redeliver tasks if a worker crashes before acknowledging completion, risking duplicate side effects such as double charges or repeated emails. Stripe's webhook system, which retries undelivered events for up to three days, was cited as a real-world example of why systems must track whether a given event has already been processed.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Developer Builds Issue-Conditioned Memory Recall for AI Customer Support Agent

A developer has shared a technical approach to making AI-powered customer support agents retain and apply memory across separate interactions. The system uses a tool called Hindsight to retrieve past support history — such as prior resolutions and known preferences — by anchoring recall to the customer's current issue rather than pulling broad account summaries. A practical example illustrates how a customer's earlier router power-adapter fix could inform a later, unrelated connectivity complaint, allowing the agent to skip redundant troubleshooting steps. The developer outlines three independent quality checks — scope, relevance, and correct use of recalled history — arguing that collapsing these into a single pass risks masking distinct failure modes. The post concludes that retrieval strategy is as much a product decision as a modelling one, with logging and evaluation recommended for production deployments.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Six AI-Powered Data Modeling Tools Reviewed: Which Ones Actually Deliver in 2026

A hands-on evaluation of six widely discussed AI-powered data modeling platforms was conducted in 2026 to separate genuinely AI-native tools from those simply marketing themselves as such. The review assessed each platform across five criteria: depth of AI integration, semantic layer support, cloud warehouse compatibility, collaboration and governance features, and pricing transparency. SqlDBM emerged as a standout, offering an AI Copilot embedded throughout the modeling workflow and an MCP Server — unveiled at the 2025 Databricks Data + AI Summit — that exposes governed models directly to LLMs and downstream AI agents. The platform reportedly serves over 400,000 users globally, with enterprise clients including DocuSign, Pfizer, and Hulu, and PwC credited it with reducing modeling time by 25 percent. Unlike standalone semantic tools such as dbt or legacy diagrammers like erwin, SqlDBM was noted for bridging both physical schema design and semantic layer management within a single workspace.

0 comments Read more at DEV Community