Stop the world from lying to your AI agent.
Most AI security tooling checks for malicious instructions, not whether the source itself is authentic, coordinated, or trustworthy. Fydel Signal scores source trust before your agent acts on open-web content in high-stakes workflows.
The problem is real and growing
AI agents are moving from pilot to production. Security and governance teams are allocating budget. But most funded AI security vendors focus on guardrails, red teaming, and runtime defenses — source integrity for open-web retrieval remains underbuilt.
One way this plays out
Content is planted
An attacker publishes manipulated content on the open web — fake articles, coordinated posts, or cloned domains.
Agent fetches it
Your AI agent retrieves the content as part of a research, monitoring, or diligence task.
Signals are hidden
The content carries false claims, coordinated amplification, or hidden instructions the agent cannot distinguish from legitimate sources.
Damage is done
The agent acts on poisoned input — producing bad analysis, triggering wrong actions, or surfacing false conclusions.
This is just one pattern. Coordinated campaigns, source impersonation, and data poisoning all exploit the same gap.
Why now
Three converging forces make source trust an urgent problem.
Fydel Signal in one API call.
Before your agent acts on any web content, get a source trust assessment. A single POST request returns a score, verdict, why, actionable signals, and a recommended policy action.
POST /v1/signal/assess HTTP/1.1
Host: api.fydel.ai
Authorization: Bearer fydel_sk_...
Content-Type: application/json
{
"url": "https://example.com/breaking-news",
"content": "CEO announces surprise merger..."
}{
"score": 0.23,
"verdict": "low_trust",
"recommended_action": "quarantine",
"why": "New domain. Coordinated amplification.",
"signals": [
"coordinated_amplification",
"domain_age_7d",
"source_impersonation"
],
"summary": "Source exhibits coordinated
amplification across recently created
domains with naming patterns mimicking
established outlets."
}Built for high-trust agent workflows
The first teams that feel this pain are the ones shipping agents into workflows where bad sources create real operational risk.
Research and market-monitoring agents
Teams using agents to summarize news, market chatter, public filings, and third-party analysis.
Why it matters: One coordinated fake can distort the brief your team acts on.
Compliance and vendor-risk workflows
Internal agents that retrieve external sources to support diligence, procurement, and monitoring decisions.
Why it matters: Bad source quality turns into bad decisions, audit gaps, and extra human review.
Trust, safety, and intelligence systems
Workflows that need to distinguish authentic signals from manipulation campaigns across open-web content.
Why it matters: The hard part is not reading more content. It is trusting the right content.
Built from the adversary's playbook
Trust & Safety built this playbook. Now agents need it.
Most teams can wire an agent to the web. Very few can build the adversarial dataset, signal models, and policy logic needed to decide whether retrieved content should be trusted. Fydel Signal is the first product from Fydel built for that decision boundary.
- Adversarial datasets — trained on the manipulation patterns behind coordinated campaigns at Big Tech, not just clean-web benchmarks
- Signal models — built to score source trust the way integrity systems score account and content authenticity at scale
- Policy logic — encodes the decision frameworks used to act on ambiguous signals in high-stakes, high-volume pipelines
Design partner fit
Best for teams already shipping, or about to ship, agents that browse or retrieve third-party content in high-trust workflows.
Where existing tools stop short
Prompt-injection scanners check for malicious instructions. Credibility databases rate known publishers. No current products combine all four of these for arbitrary open-web content in the agent hot path.
Source reputation
Domain age, registration patterns, publishing history, and impersonation signals for any URL your agent fetches.
Coordination detection
Identify when content is being amplified across duplicated or synchronized sources as part of a manipulation campaign.
Low-latency scoring
Built for the agent hot path. Sub-200ms assessments so trust scoring doesn’t slow down your workflow.
Agent-native policy API
Returns a recommended action — proceed, downgrade, quarantine, or skip — so your agent can enforce trust policies programmatically.
How it works
Add trust-gated content processing to your agent without rebuilding trust-and-safety infrastructure in-house.
Step 1
Agent fetches content
Your AI agent crawls a webpage, reads an article, or receives user-submitted content from the web.
Step 2
Calls Fydel Signal
Before processing, your agent sends the URL and content to the Fydel Signal API for source trust assessment.
Step 3
Gets a trust score
The API returns a trust score, verdict, source-authenticity and coordination signals, and a recommended action.
Step 4
Acts on policy
Your agent follows the recommended action: proceed, downgrade, quarantine, or skip the source entirely.
Request design partner access
We're onboarding design partners for Fydel Signal: teams building agents that retrieve third-party content. Share your work email and we'll reach out if the workflow is a fit for the beta.
Best for teams shipping or piloting agents in high-trust workflows.
Prefer a direct conversation? Email the founder.