Bernard Huang
Founder of VeracityAPI. Co-founded Clearscope and bootstrapped it to 7-figure ARR over 10 years working with editorial and content teams at Nvidia, HubSpot, Adobe, IBM, and Condé Nast. Now building VeracityAPI — content trust infrastructure for autonomous agent workflows.
What I write about
- The design choices behind VeracityAPI's routing-first response shape
- Operational patterns I see in agent content pipelines
- Why the AI-detection category is splitting into two product categories
- How specificity, not authorship, is the signal that predicts whether content earns trust
- Benchmark methodology and why competitor numbers are gated until the 2026 program freezes
Background
I spent ten years at Clearscope watching content teams make publish/revise/reject decisions on tens of thousands of drafts. The pattern that emerged was that specificity — named places, named products, real examples — was the reliable predictor of whether content earned reader trust and search ranking. VeracityAPI is the product I would have wanted for the agent-driven version of that same decision.
Recent posts
SynthID and VeracityAPI: the layered verification stack content teams actually need
Hard provenance signals like SynthID and workflow-risk APIs like VeracityAPI answer different questions in the same pipeline. Why most teams need both layers, where each one carries weight, and what to do when the strong signal isn't available.
What 10 years at Clearscope taught me about AI slop
The lesson that shaped VeracityAPI's design: specificity, not authorship, is the signal that predicts whether content earns trust.
The most expensive bug in agent content workflows
The unbounded revise loop. Why it happens, why your agent will burn through hundreds of dollars on a single draft if you let it, and the four-line fix.
Why the AI-detection category is splitting
The category that started as 'is this written by AI?' is becoming two categories. Where each one is heading, who they're built for, and why a single product can't do both jobs well.
Why we don't publish competitor benchmark numbers (yet)
The most common question I get is 'how does VeracityAPI compare to GPTZero on accuracy?' The honest answer is that I don't have a number I'm willing to publish — and here's the system we're building to produce one.
Benchmarking AI detectors on the routing decision production teams actually need
Why VeracityAPI will report binary flagging metrics alongside workflow-routing F1, with caveats and reproducibility gates.
VeracityAPI is not an AI detector. It is a routing linter for AI outputs.
The product wedge: action plus evidence for agent workflows, not forensic authorship proof.
Where to find me
GitHub: @psyduckler · LinkedIn: bernardjhuang · X: @bernardjhuang · Email: hello@veracityapi.com