Your AI isn't hallucinating. It's uninformed. Feed it.
Updated
Live brand.mentions.summary sentiment pull, Liquid Death. Grounding means the model quotes this, not its guess.
The short answer
When AI drafts confident nonsense, you want every generation grounded in real audience and competitive data, so scale stops meaning slop. Adveron is the grounding layer: current audience language, live category conversation, and tracked competitive activity, served to the model at generation time instead of recalled from its training data.
Current audience language, live category conversation, and tracked competitive activity served to the model as it writes.
Drafts built from real data instead of training-data recall, so volume stops costing accuracy.
What the model cites traces to real posts and ads, so review checks sources instead of guessing.
The slop problem is mostly an input problem: a model asked to write for an audience it has never observed will produce the average of everything, fluently. The remedy is retrieval, not prompting, put what this audience actually says, asks, and responds to in front of the model at generation time, and the output starts from evidence instead of from plausible average.
In practice that means the generation step makes tool calls before it writes: pull the audience's own phrases for the pain, pull what the category is currently saying so the draft avoids the worn angles, pull the competitive record so no claim contradicts the observable world. Every draft arrives with its sources attached, which is also what gets machine-made work through human review.
Illustrative request and response
{"brand": "liquid-death","sentiment_distribution": {"neutral": 397,"positive": 347,"negative": 102},"window": "30d"}
Early access. 5,000 free credits, no card.
Get early access/v1/audiences/{audience_id}/insightsWhat this audience actually cares about, at generation time./v1/audiences/{audience_id}/postsTheir own phrases, quotable straight into the draft./v1/categories/{category_id}/trendsThe live conversation the draft should meet, not miss.MCP serverThe same grounding as tools any MCP-compatible model calls itself.Neither carries current data. Fine-tuning bakes in a snapshot that ages; prompts describe the audience secondhand. Grounding retrieves what the audience said this week, at the moment of generation, which is the part no static method can supply.
Grounded drafts carry their sources, so a reviewer checks claims against linked evidence instead of arguing with a vibe. The approval conversation gets shorter because the question becomes 'is this receipt right,' not 'do we believe this.'
Adveron is opening to a first wave of teams. Ask for a key and we will open your workspace with one credential for REST and MCP. Usage-priced credits, no seat licenses.
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