For teams building AI in high-stakes domains, Scorecard combines LLM evals, human feedback, and product signals to help agents learn and improve automatically, so that you can evaluate, optimize, and ship confidently.
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Promoted
Hey Product Hunt, Darius here, CEO of Scorecard 👋
I almost shipped an AI agent that would've killed people
I built an EMR agent for doctors. During beta testing, it nailed complex cases 95% of the time. The other 5% it confused pediatric and adult dosing and suggested discontinued medications. And the problem wasn't just my agent. My friend's customer support bot started recommended competitors, another founder's legal AI was inventing case law. We were all playing whack-a-mole with agent failures, except we couldn't see the moles until customers found them.
At Waymo, we solved this differently
I helped ship the Waymo Driver, the first real-world AI agent. The difference? Every weird edge case becomes a test. Car gets confused by a construction zone? We built a platform to simulate 100s of variations before the next deployment. We still played whack-a-mole, but we could see ALL the moles first.
That's why we built Scorecard - the agent eval platform for everyone
Now your whole team can improve your agent without the chaos. Here's what Scorecard unlocks:
🧪 Your PM runs experiments without begging engineering for help
🔍 Your subject matter expert validates outputs without Python
🛠️ Your engineer traces which function call went sideways
📊 Everyone sees the same dashboard of what's working
After running millions of evals, the signal is clear: teams using Scorecard ship 3-5x faster 📈 because you can't improve what you don't measure. Checkout how leading F500 companies like Thomson Reuters are shipping faster using Scorecard 🚀
🎁 [Exclusive PH Offer!] Get hands-on help setting up evals today
Product Hunters building AI agents today drop your worst agent horror story below. First 20 teams get me personally helping set up your evals (fair warning: I will get too excited about your product). Stop shipping on vibes and start shipping with confidence.
@dare Impressed by the holistic approach to agent evaluation,combining LLM scoring, human review, and real product telemetry. This addresses a real need for shipping reliable AI agents. Does Scorecard offer native integrations for tracking evals on open-source agent frameworks like LangChain or Haystack?
@khushal_bapna Brilliant question!
Yes, we have official 1-liner integrations with LangChain, Haystack, LlamaIndex, CrewAI, and many more. We're featured in both the Vercel AI SDK and OpenAI Agents SDK docs as a recommended observability provider.
We're huge fans of open standards and are working with leaders like Amazon, Google, Elastic among others to create a OpenTelemetry standard for AI agents and help builders move faster!
@khushal_bapna @dare great product. I will definitely try this and share in my circle. I sent you a dm on twitter. is there any better way to contact you for more info?
@khushal_bapna @kshetez_vinayak Thanks for the kind words! Will take a look at Twitter DMs
@dare Love how you connected the Waymo approach to AI validation. That mindset of “seeing all the moles before users do” really sticks. Congrats on the launch, Darius!
@hrgdevbuilds Thank you! Working at Waymo convinced us that shipping real-world agents requires a fundamentally different approach than traditional software.
Are you building an AI agent? Would love to hear what you're working on!
@dare Congrats on the launch! Love the simple workflow and side by side comparison. Curious if you import eval datasets like llm benchmarks and support local models via Ollama, etc.
@navam_io Thank you! Yes to both, you can import datasets via CSV, JSON, or our API/SDK.
For local models (Ollama, llama.cpp), you run them like normal and log results to Scorecard via our SDK. Would love to hear your setup, what models are you running and how?
@dare That's cool. We are an AI product studio building white label products at navam.io so our stack is pretty much most frontier models and providers, agentic frameworks, etc. We have not looked at eval frameworks yet to add to our stack, so don't mind starting with yours :-)
Well that’s exactly what serious AI teams need. Combining human feedback with product metrics kinda closes the loop perfectly. Does it support continuous evaluation in production environments too?
@roozbehfirouz Absolutely!
Monitoring is core to what we do, and the best evals come from production failures. Our Monitors automatically sample live agent traffic and score it in real-time: You can:
- Set sampling rates (1-100%) to control eval volume
- Filter by keywords to track specific topics like "refunds" or "PII" separately
- Catch regressions before customers do and convert failing traces into evals
Darius congrats buddy good launch. Does scorecard offer integrations with Data ETL prods like snowflake?
@hpgpt Thank you!
We don't have a direct Snowflake connector yet, but you can export via our API/SDK or do bulk export into Snowflake. What's your use case? Are you looking to join eval data with product analytics or build a custom dashboards?
Congrats on the launch! Scorecard looks super useful especially for keeping performance data transparent and easy to understand.
Quick question though: how do you make sure the scoring system stays fair and can’t be easily gamed?
I think it’d be great if users could see a breakdown of how each metric affects their overall score that extra bit of clarity could make it even more valuable.
@chrishicken Great question and agreed on transparency!
Every metric in Scorecard shows the reasoning for why an eval passed or failed. As a builder, you define what "good" looks like for your use case, combining signals (LLM-based metrics, rules, human feedback, product metrics) so you're not optimizing for a single gameable metric.
Plus, if your agent learns to ace your test dataset but fails in the real world, production monitoring catches it. We score live user traffic automatically, so you're always testing against reality.
Incredible story, love how you turned a near-disaster into a framework for reliability. Does Scorecard simulate edge cases automatically, or do teams define them manually?
Congrats on the launch! Love the simple workflow and side by side comparison. Curious if you import eval datasets like llm_benchmarks and support local models via Ollama/llama.cpp?
@navam_io Thank you! Yes to both, you can import datasets via CSV, JSON, or our API/SDK.
For local models (Ollama, llama.cpp), you run them like normal and log results to Scorecard via our SDK. Would love to hear your setup, what models are you running and how?
Love the details you added and features for the agents! Congrats to launch!
Beautiful website meanwhile!