Moda
Product & Competitive Intelligence
Monitors AI agents in production with real-time failure detection and conversation replay.
Company Overview
Provides a reliability and monitoring layer for AI agents and LLM-powered applications, offering real-time behavioral failure detection, security monitoring, conversation replay, and root-cause analysis via a lightweight SDK integration.
Competitive Advantage & Moat
Product Roadmap & Public Announcements
Moda's product offers automatic conversation tracking, real-time behavioral failure detection (agents that claim they "did it" without actually doing it, tool calls that error or time out), custom signal writing with threshold-based alerts, conversation replay and editing, security monitoring (prompt injection, jailbreak, RAG poisoning, NSFW), and SDK support for OpenAI, Anthropic, and AWS Bedrock. Built for long, messy conversations with skills, MCPs, and tools that previous tools weren't designed for.
Signals & Private Analysis
The emphasis on "no-config" behavioral detection and conversation replay hints at heavy investment in unsupervised ML and proprietary evaluation pipelines. Likely building toward self-hosted/on-prem deployment for enterprise buyers, cost/token analytics dashboards, multi-modal agent support, and deeper integrations with orchestration frameworks like LangChain and LlamaIndex.
Product Roadmap Priorities
Automatically detects AI agent behavioral failures—such as unverifiable promises, repeated answers, and hallucinations—in real time with zero manual configuration.
It's like having a quality inspector watching every single conversation your AI agent has and instantly raising a flag the moment something goes wrong.
It's like having a spell-checker that doesn't just catch typos but also notices when your AI starts confidently making things up, and then hands you a red pen to fix it on the spot.
Continuously monitors all AI agent interactions for security threats including prompt injection, jailbreak attempts, RAG poisoning, and NSFW content generation.
It works like an always-on security guard for your AI, catching anyone trying to trick it into saying or doing something it shouldn't.
It's like having a bouncer at the door of your AI who's seen every con in the book and never takes a bathroom break.
Clusters agent failures to identify systemic root causes, then enables product teams to replay, edit, and batch-test conversation fixes before deploying improvements with regression prevention.
It lets you rewind any AI conversation that went wrong, fix the problem, test the fix across hundreds of similar cases, and ship it—all without writing a single new test from scratch.
It's like being able to rewind a bad first date, figure out exactly where things went sideways, rehearse a better version, and then make sure you never repeat that awkward moment again across all your future dates.
Company Overview
Key Team Members
- Mohammad, Co-Founder
- Pranav, Co-Founder
Founder Force Multiplier
Moda combines always-on, zero-config behavioral failure detection with conversation-level replay and editing, a unique pairing that lets teams not only find agent failures but immediately fix and regression-test them in a single workflow, something no competitor offers out of the box.
Funding History
- 2025-2026 | Mohammad and Pranav co-found Moda.
- 2026 | Accepted into Y Combinator Winter 2026 batch.
- 2026 | Product live with SDK support for OpenAI, Anthropic, and AWS Bedrock.
Competitors
- LLM Observability: LangSmith (LangChain), Arize AI (Phoenix), Helicone, Braintrust.
- General ML Monitoring: Weights & Biases, Datadog LLM Monitoring, New Relic AI Monitoring.
- AI Security: Lakera (Guard), Robust Intelligence, Prompt Security, Rebuff.
- Evaluation Platforms: Patronus AI, Confident AI (DeepEval), Ragas.
