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The fastest way to turn your taste and expertise into automation. Arbiters are language models that make a decision, explain why, and return a (reliable) score for how sure they are -- measured from inside the model. Confident decisions are accepted, unsure decisions go to a human, then their fixes update the instructions automatically. Over time, arbiters align to your judgment and decision rules. Use them to score, classify, route, extract, and moderate -- the way your team would by hand.
At scale, automating human judgment with LLMs fails in three ways:
1. Models can’t reliably flag their own edge cases 2. Manual instruction fixes are unsystematic and risk regression 3. To measure success, you must review either everything or nothing
Modaic is the alignment engine for decision automation — it turns expert judgment into reliable, auditable LLM decisions. We introduce Arbiters: language models that return a decision, its reasoning, and a (reliable) score telling you how sure the model is in its decision. Reviewer corrections compile back into better instructions automatically.
What you can build: - LLM-as-a-judge that scores agent outputs, RAG answers, or generated content the way your reviewers would - Classification and routing for support tickets, leads, content, and messages aligned to your team’s taxonomy - Tagging and data labeling for training sets, eval datasets, and trace metadata — aligned to your reviewers’ standard - Structured extraction from contracts, invoices, medical records, and transcripts at scale - Content moderation aligned to your policy, not a generic safety filter - Quality scoring and ranking for code completions, search results, and AI-generated content - Fraud detection, risk scoring, and KYC for transactions, claims, onboarding flows, aligned to your team’s policy
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About Modaic on Product Hunt
“Your expert judgment and taste, running at scale.”
Modaic was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #110 on the daily leaderboard. The fastest way to turn your taste and expertise into automation. Arbiters are language models that make a decision, explain why, and return a (reliable) score for how sure they are -- measured from inside the model. Confident decisions are accepted, unsure decisions go to a human, then their fixes update the instructions automatically. Over time, arbiters align to your judgment and decision rules. Use them to score, classify, route, extract, and moderate -- the way your team would by hand.
Modaic was featured in Developer Tools (515.4k followers), Artificial Intelligence (473.1k followers) and Tech (627.5k followers) on Product Hunt. Together, these topics include over 345.9k products, making this a competitive space to launch in.
Who hunted Modaic?
Modaic was hunted by Farouk Adeleke. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Modaic stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Farouk Adeleke
Modaic is live! Accessible via waitlist
At scale, automating human judgment with LLMs fails in three ways:
1. Models can’t reliably flag their own edge cases
2. Manual instruction fixes are unsystematic and risk regression
3. To measure success, you must review either everything or nothing
Modaic is the alignment engine for decision automation — it turns expert judgment into reliable, auditable LLM decisions. We introduce Arbiters: language models that return a decision, its reasoning, and a (reliable) score telling you how sure the model is in its decision. Reviewer corrections compile back into better instructions automatically.
What you can build:
- LLM-as-a-judge that scores agent outputs, RAG answers, or generated content the way your reviewers would
- Classification and routing for support tickets, leads, content, and messages aligned to your team’s taxonomy
- Tagging and data labeling for training sets, eval datasets, and trace metadata — aligned to your reviewers’ standard
- Structured extraction from contracts, invoices, medical records, and transcripts at scale
- Content moderation aligned to your policy, not a generic safety filter
- Quality scoring and ranking for code completions, search results, and AI-generated content
- Fraud detection, risk scoring, and KYC for transactions, claims, onboarding flows, aligned to your team’s policy
And more!