Model labs and inference providers
Find stolen credentials, unauthorized wrappers, relay networks, and customers reselling access through your endpoints.
Trust infrastructure for AI
Can you trust the way your AI is being used?
InferTrail is the metadata-only intelligence layer that helps AI companies understand when usage is legitimate growth—and when trust in the system may be breaking down.
Metadata only · Explainable signals · No production-path dependency
AI cannot become dependable infrastructure if its operators cannot tell what is happening inside it.
InferTrail gives platform, trust, security, fraud, and FinOps teams a shared view of AI usage—without requiring access to prompts, responses, or customer identities.
AI gateways, routers, and marketplaces
Separate legitimate downstream customers from hidden proxy traffic, credential sharing, and coordinated account abuse.
AI coding and agent platforms
Detect automated extraction, compromised sessions, headless harnesses, and abnormal tool or model consumption.
AI SaaS products
Identify free-tier farming, account sharing, stolen sessions, and unexpected inference usage before the bill compounds.
Security and fraud teams
Investigate affected principals and behavioral clusters with evidence—not isolated IP alerts or opaque scores.
Platform and FinOps teams
Connect suspicious behavior to model usage, token volume, concurrency, and estimated cost exposure.
Understand usage without turning every anomaly into an accusation.
InferTrail combines independent signals and evaluates them against each principal’s own context, so teams can investigate change with evidence and human judgment.
01
Principal baselines
Learn normal model choice, spend, timing, geography, network, client, concurrency, and session behavior for each user, key, workspace, or service account.
02
Behavioral clustering
Split one principal’s traffic into distinct usage populations so an intruder does not disappear inside legitimate aggregate activity.
03
Network and relay fingerprints
Link stable behavior across changing datacenter, proxy, VPN, residential, and mobile networks without relying on one IP address.
04
Coordination signals
Find account farms and shared automation through synchronized creation, infrastructure overlap, repeated usage shapes, and entitlement exhaustion.
05
Spend and concurrency shifts
Detect model-tier escalation, runaway loops, retry amplification, token-volume bursts, and simultaneous clusters competing for the same access.
06
Explainable findings
Combine supporting evidence into a finding that states what changed, which cluster is affected, the estimated exposure, and what could be benign.
When trust breaks down, the signals are already in your traffic.
Runaway spend, automation, free-tier abuse, credential replay, and model extraction are different ways AI usage can become difficult to understand and control.
Stolen credentials
A new behavioral cluster starts consuming inference through an established API key, session, account, or service identity.
Hidden token resale
One customer’s access begins serving many unrelated downstream users through wrappers, routers, or grey-market services.
Unauthorized automation
Scripts, headless clients, coding harnesses, and automated extraction consume access outside the intended product experience.
Proxy and relay networks
Stable usage behavior persists while infrastructure rotates across datacenter, VPN, residential, and mobile networks.
Free-tier account farms
Coordinated accounts share infrastructure, usage shapes, timing, and entitlement-exhaustion behavior.
Runaway inference spend
Agent loops, retry amplification, abnormal concurrency, model-tier shifts, and token bursts drive unexpected cost.
From raw telemetry to confident investigation.
InferTrail does not reduce an entire account to one score. It separates distinct behavioral clusters and shows investigators what changed, why it matters, and what could be benign.
01 / CONNECT
Send the usage metadata you already collect
InferTrail accepts asynchronous events from inference gateways, authentication, accounts, networks, and billing systems.
02 / LEARN
Understand what normal usage looks like
Compare model choice, spend, timing, network, client, concurrency, and session behavior for every account or credential.
03 / SEPARATE
Expose unfamiliar usage inside real accounts
Separate legitimate customer activity from suspicious concurrent, automated, relayed, or coordinated traffic.
04 / INVESTIGATE
See who is affected and what it costs
Review the affected access, supporting evidence, confidence, likely explanation, and estimated spend exposure.
Detect abuse without creating a new data risk.
InferTrail analyzes operational metadata while keeping sensitive AI content and production request handling outside the system.
01
Your AI content stays out of scope
InferTrail does not collect prompts, responses, tool arguments, request bodies, or generated content.
02
Your credentials remain secret
API keys, authorization headers, cookies, passwords, and session secrets are never collected.
03
Analysis stays off the request path
Events arrive asynchronously, so detection never delays or modifies an inference request.
04
Findings remain verifiable
Every result includes the behavioral change, supporting evidence, confidence, cost exposure, and plausible benign explanations.
Build AI systems you can trust.
Bring the usage metadata you already collect and start understanding where legitimate growth ends and operational risk begins.