alt.qa Field Reports
The expensive AI gaps nobody is watching.
Every post here is a real, quantified, high-consequence failure mode, and the scan, load test, or evaluation that catches it before it reaches your P&L.
New here? Start with Invisible to AI, Keyboard navigation, Overlays and lawsuits or 16 ways agents get stuck.
All Published Reports
Flagship, deeply-researched reports, live now.
HIPAA BAA Violations in Cloud LLM Prompt Caching: Preventing ePHI Leaks in Clinical AI
Cloud LLM prompt caching achieves 50-90% cost discounts by retaining KV cache tensors in GPU memory. When clinical notes are cached across sessions, organizations commit statutory HIPAA violations under 45 CFR § 164.312.
The gap: Up to $2,067,813/yr in civil monetary penalties
Time to First Token Is Your Real SLA
Your dashboard says 200ms. Your users wait 8 seconds. TTFT, not request duration, is the metric that decides whether they stay.
The gap: 15-25% conversation abandonment above 2s TTFT
The $50,000 Token Bill Nobody Load-Tested For
A $5k/month LLM feature becomes a $60k invoice in 90 days. The gap is that nobody tested cost as a function of load.
The gap: $5k → $60k/mo cost runaways in 90 days
When the Stream Drops at Token 500: Streaming Failures Under Load
Streaming hides failure. Under load, connections die mid-response and your users get half an answer, with a 200 status code.
The gap: Silent partial responses tank trust & retention
The Retry Storm That Took Down Your AI Feature
One slow provider + naive retry logic = a self-inflicted DDoS that triples your bill and extends the outage you were trying to survive.
The gap: Retry amplification turns blips into outages
Inter-Token Latency: The Stutter Your Users Feel But You Never Measure
A good TTFT with bad inter-token latency reads like a laggy typewriter. ITL is the UX metric hiding between your other metrics.
The gap: Perceived-slowness churn invisible to APM
429 Cascade: How Provider Rate Limits Become Your Outage
Provider rate limits don't fail gracefully through your stack. One 429 becomes a queue, the queue becomes a timeout, the timeout becomes a P1.
The gap: Rate-limit incidents = full-feature downtime
Capacity Planning for AI: Stop Counting Requests, Start Counting Tokens
RPS is a lie for LLMs. A single request can be 10 tokens or 10,000. Capacity and cost track tokens/sec, and almost no one plans for it.
The gap: Mis-sized infra = overspend or brownouts
Agent Loops That Burn Money While You Sleep
An agent that worked in the demo hits a tool-call loop in production and spends $1/second until someone notices at 9am.
The gap: Stuck agent loops = overnight 5-figure bills
Context Window Exhaustion: The Failure That Only Shows Up Under Load
Long conversations + concurrency fill the context window faster than you modeled. Quality degrades, then requests fail with cryptic errors.
The gap: Silent quality decay → hard failures at peak
Launch-Day Burst Traffic Is Where AI Features Die
You tested at average load. Launch day is 10x for 20 minutes. AI endpoints degrade soft, then fail hard, exactly when the world is watching.
The gap: Launch failures waste the whole GTM spend
p99 Latency Is Your Brand: Why Averages Lie About AI UX
Your average user is happy. Your p99 user is tweeting. For AI features the tail is the story, and it's the tail that load testing must target.
The gap: Tail-latency users churn and complain loudest
Your Prompt Cache Hit Rate Collapses Exactly When You Need It
Prompt caching saves 50% in the demo. Under varied production load the hit rate craters and your cost projection was fiction.
The gap: Cache assumptions break the cost model
Circuit Breakers for AI: How to Fail Without Failing the User
When the model API degrades, the right move is a fast, cheap, degraded answer, not a queue of doomed retries. Test the breaker before you need it.
The gap: No breaker = full outage instead of graceful degrade
Multi-Provider Failover: Test It Before the Outage Tests You
You added a fallback model "for resilience." You never load-tested the failover path. It's slower, pricier, and formats output differently.
The gap: Untested failover = worse outage than no failover
Cold Starts Are Killing Your Self-Hosted LLM SLA
Scale-to-zero saves money until a scale-up event adds 40 seconds of model load to the first user's request. Test the cold path.
The gap: Cold-start spikes blow the latency SLA
Goodput, Not Throughput: The Only Load Number That Matters
Throughput counts requests you served. Goodput counts requests you served well enough to keep. For AI, they diverge fast under load.
The gap: Optimizing throughput while losing users
Token-Aware Rate Limiting: The Guardrail Request Limits Can't Provide
Limiting requests/minute does nothing when one request is 50k tokens. Limit the thing you're billed for, then load-test the limiter.
The gap: Request-based limits let cost explode
Your RAG Pipeline Has Four Latency Bombs and Load Finds All of Them
Embed → retrieve → rerank → generate. Each stage has its own failure curve. Load-test the pipeline, not just the model.
The gap: Stacked stage latency blows the budget
Vector DB Load Testing: Recall Quietly Drops as QPS Climbs
Your ANN index is fast and accurate at 10 QPS. At 500 QPS recall degrades and retrieval quality silently rots, feeding the model garbage.
The gap: Silent recall loss degrades every answer
Voice AI Has a 300ms Budget. Here's How to Spend It Under Load
STT + LLM + TTS in under 300ms or the conversation feels broken. Each hop steals from the budget, and load testing shows who's stealing most.
The gap: Latency over budget = unusable voice product
Load Testing the Realtime API: WebSockets Break Differently
Realtime/voice endpoints hold long-lived connections. Connection limits, not request rates, are what fall over, and most tools can't even fire the test.
The gap: Connection ceilings cap concurrent users
The Embedding Endpoint Is Your Hidden Bottleneck
Every search, every RAG query, every dedup job hits embeddings. It's the most-called, least-tested endpoint in your AI stack.
The gap: Embedding throttling stalls the whole app
The Metric Your CFO Wants: Cost Per Conversation Under Load
Cost per request is a vanity metric. Cost per resolved conversation, measured under realistic load, is the number that decides if the unit economics work.
The gap: Broken unit economics scale your losses
Soak Testing AI: The Leaks That Only Show Up After Hour Six
Memory creep, connection-pool exhaustion, cache rot. AI serving stacks fail slowly. An 8-hour soak finds what a 5-minute test never will.
The gap: Slow leaks = 3am pages days after launch
Put a Load Gate in CI: Block the Deploy That Slows Your AI
Latency regressions ship silently. A CI gate that asserts maxTTFT and minTPS turns "it feels slower" into a red build before users notice.
The gap: Latency regressions ship to prod undetected
Why the Fastest LLM Load Test Runs in Your Browser
No account, no Python, no CORS proxy. A browser extension with host permissions reuses your session and fires cross-origin bursts no web page can.
The gap: Setup friction means the test never gets run
Tool Calls Double Your Latency. Did You Load-Test Them?
Every function call is a round trip the model waits on. Under load, tool latency stacks on model latency and the budget evaporates.
The gap: Tool round-trips blow the latency budget
Multimodal Load Testing: Big Payloads Break Small Assumptions
Image and audio inputs are 100x the bytes of a text prompt. Upload time, preprocessing, and token blowup all change the load curve.
The gap: Payload size breaks the cost & latency model
GPU Saturation: The Cliff Your Self-Hosted LLM Falls Off
Self-hosted inference is linear, then it isn't. Past the batch ceiling, queue depth explodes and latency goes vertical. Find the cliff first.
The gap: Hitting the GPU cliff in prod = hard outage
Your LLM Timeouts Are Wrong (Both Directions)
Too short and you kill good requests mid-stream. Too long and you pile up doomed ones. The right timeout comes from the latency distribution, not a guess.
The gap: Wrong timeouts waste capacity & cut users off
The Confused Deputy in Federated MCP: Preventing Cross-Server Privilege Escalation
When an agent connects to multiple MCP servers, all tools share ambient credentials. An indirect prompt injection in a read-only tool can coerce administrative mutations on internal databases, violating SOC 2 CC6.1.
The gap: Data loss, unauthorized mutations & SOC 2 failure
The ADA Lawsuit Tax: When an Unscanned Page Becomes a $75,000 Demand
Nearly 4,000 website accessibility lawsuits were filed in 2025. The gaps that trigger them are exactly the ones an automated scan catches in minutes.
The gap: $45k-$200k per ADA web accessibility claim
Invisible to AI: Why ChatGPT Can't Cite Your Site
AI referral traffic grew 527% in a year and converts 2-4x better. If LLMs can't parse and cite your pages, you're absent from the new front page.
The gap: Missing the channel that converts 2-4x better
Core Web Vitals Are Cash: The Milliseconds Killing Your Conversion Rate
Every 100ms of LCP delay costs ~1% of conversions. On a $10M store, a half-second is half a million dollars, and 53% of sites fail the threshold.
The gap: ~$500k/yr per 500ms on a $10M store
Broken Links Are Broken Revenue (and Broken Trust)
A 404 in a checkout flow or a top blog post leaks revenue and authority every day it lives. Most teams find out from a customer, not a scan.
The gap: Dead links leak revenue & link equity daily
llms.txt: The File That Tells AI How to Read You (You Don't Have One)
A new convention lets you hand AI crawlers a clean map of your content. Without it, models guess, and guess wrong about what you do.
The gap: AI mis-describes your brand to buyers
The European Accessibility Act Is Live. Is Your Site a Liability?
As of June 2025 the EAA applies to digital products across the EU. Non-compliance risks fines and market removal, and it's scannable today.
The gap: EAA fines + EU market exclusion
Accessibility Overlays Are a Lawsuit Magnet, Not a Shield
That one-line "accessibility widget" is named in a growing share of ADA suits. Real remediation starts with a real scan of the underlying DOM.
The gap: Overlays attract, not deflect, litigation
INP Is the Core Web Vital Quietly Failing Your Site
Interaction to Next Paint replaced FID in 2024 and it's stricter. Heavy JavaScript makes every tap feel laggy, and Google is watching.
The gap: Poor INP = lower rankings & conversions
Your JavaScript Framework Is Eating Your SEO and Your AI Visibility
Client-rendered pages return a shell to crawlers. Google sometimes waits; most AI crawlers don't. A scan shows what bots actually see.
The gap: Empty pages to bots = lost organic & AI traffic
Your robots.txt Is Accidentally Blocking the AI Traffic You Want
Half of teams either block GPTBot by reflex or leak everything by accident. The right answer is deliberate, and a scan tells you which mistake you're making.
The gap: Blocking AI crawlers erases AEO upside
Structured Data Decay: Your Schema Broke and Nobody Noticed
A template change silently invalidated your Product and FAQ schema. Rich results vanished, AI citations dropped, and analytics never flagged it.
The gap: Lost rich results & AI citations
The API Key in Your Frontend: A Scan Finds It Before an Attacker Does
Hardcoded keys, tokens, and secrets ship to the browser more often than anyone admits. One scan of your bundle can save a breach disclosure.
The gap: Leaked credentials → breach & disclosure cost
Missing Security Headers: The Free Hardening You Skipped
No CSP, no HSTS, no X-Frame-Options. Each missing header is an open door auditors and attackers both check first. A scan closes them in an afternoon.
The gap: Failed security review stalls enterprise deals
Open Redirects: The Phishing Vector Hiding in Your Login Flow
An unvalidated redirect param turns your trusted domain into a phishing launchpad. It's low-severity until it's in a credential-theft campaign.
The gap: Brand abused in phishing; trust & takedown cost
Soft 404s Are Bleeding Your Crawl Budget
Pages that say "not found" but return 200 confuse crawlers, waste budget, and dilute indexation. They're invisible without a scan that reads intent.
The gap: Wasted crawl budget = slower indexation
AI Forgets You in 90 Days: Content Freshness as an AEO Signal
AI answer engines have a strong recency bias, citations to 3-month-old pages drop sharply. A scan surfaces what's gone stale before you vanish.
The gap: Stale pages drop out of AI answers
Redirect Chains Are Stealing Your Speed and Your Link Equity
Every hop in a redirect chain adds latency and leaks ranking signal. Years of migrations leave chains a scan can collapse in one pass.
The gap: Latency + lost link equity on every hop
Color Contrast: The #1 WCAG Failure Hiding in Your Brand Palette
Low-contrast text is the most-cited accessibility violation and the easiest litigation target. Your designer's favorite gray may be a legal risk.
The gap: Most common cited WCAG violation in suits
Alt Text Is Doing Double Duty: Accessibility and AI Discovery
Missing alt text fails WCAG and blinds AI image understanding. One scan, two wins, compliance and discoverability in the same pass.
The gap: WCAG failure + lost multimodal AI discovery
Third-Party Scripts Are the Performance Tax You Forgot You're Paying
Chat widgets, analytics, A/B tools, each adds blocking JavaScript and a privacy liability. A scan quantifies the tax and names the offenders.
The gap: Script bloat tanks CWV & conversions
Web Fonts Are Causing the Layout Shift That Costs You Sales
FOIT/FOUT and unsized fonts spike CLS, making buttons jump as users tap. It's a measurable conversion leak and a one-line fix a scan pinpoints.
The gap: CLS-driven misclicks & conversion loss
Canonical Chaos: How Duplicate URLs Split Your Ranking Power
Faceted nav, tracking params, and http/https variants spawn duplicate URLs that cannibalize each other. A scan maps the canonical mess.
The gap: Split ranking signals suppress every page
Your Cookie Banner Is Lying: Consent Gaps That Invite GDPR Fines
Trackers that fire before consent are a documented enforcement target. A scan compares what your banner promises against what the page actually loads.
The gap: GDPR enforcement on pre-consent tracking
Mobile Performance Is Where Your Revenue Actually Lives
Most traffic is mobile; most performance budgets are set on desktop. The gap is a conversion sinkhole a mobile-emulated scan exposes.
The gap: Mobile slowness leaks majority-traffic revenue
If You Can't Tab Through It, You Can Be Sued For It
Keyboard operability is table-stakes WCAG and a frequent litigation finding. A scan plus a keyboard pass catches the traps no mouse user sees.
The gap: Keyboard traps are common suit findings
TTFB: When Your Server, Not Your Frontend, Is the Slow Part
You optimized images and shipped a CDN, but LCP is still bad. The culprit is time to first byte, and a scan separates server from client delay.
The gap: Server latency caps every other optimization
Share of Voice in AI Answers: The Metric Your CMO Doesn't Have Yet
Buyers ask ChatGPT and Perplexity for recommendations. If competitors get named and you don't, you're losing pipeline you can't see in analytics.
The gap: Invisible pipeline loss in AI recommendations
Orphan Pages: The Content You Paid For That Google Can't Find
Pages with no internal links are invisible to crawlers and users alike. A scan of your link graph finds the stranded assets and the fix.
The gap: Paid-for content never gets indexed
Mixed Content and SSL Gaps: The Padlock That Isn't
One http:// asset on an https:// page breaks the padlock, triggers browser warnings, and spooks buyers at checkout. A scan finds every offender.
The gap: Browser warnings scare off buyers at checkout
EU AI Act Article 15 Compliance: Quantitative Audit Protocols for Accuracy, Robustness, and Cybersecurity
Article 15 of Regulation (EU) 2024/1689 imposes legally binding accuracy, cyber resilience, and fail-safe obligations on Annex III high-risk AI deployers. Here is the quantitative audit protocol required before August 2026.
The gap: Up to €15M or 3% of global turnover in penalties
The Hallucination That Cost a Million: Why Spot-Checks Aren't Enough
Air Canada was held liable for its chatbot's invented policy. When your AI speaks for you, every confident wrong answer is a legal and financial exposure.
The gap: Direct liability for AI misstatements
Silent Model Updates Are Breaking Your AI (And You Won't Know for 14 Days)
91% of production LLMs drift within 90 days; teams detect it 14-18 days late. A regression eval turns silent drift into a failed build.
The gap: 14-18 day detection lag on silent regressions
RAG's Silent Failure: Confidently Wrong, Caught Too Late
68% of RAG users hit a silent failure, a confident wrong answer caught only after customer impact. The fix is groundedness eval, not vibes.
The gap: 68% hit a customer-impacting silent failure
LLM-as-Judge: Can You Trust the Judge Grading Your AI?
Using a model to grade a model scales evaluation, until the judge is biased, inconsistent, or gameable. Here's how to validate the validator.
The gap: Untrusted judges greenlight bad releases
Your Eval Coverage Is a Rounding Error
Ten hand-picked prompts is not a test suite. The behaviors that break in production are the ones no one wrote a case for.
The gap: Untested behaviors fail first in production
Prompt Drift: Your Prompt Didn't Change, But Its Behavior Did
Prompts are code, but they're versioned in Slack threads and config files no one tests. Drift creeps in until output quality quietly collapses.
The gap: Untracked prompt edits cause silent quality loss
Golden Datasets: The Regression Suite Your AI Has Been Missing
Traditional code has unit tests. AI needs golden datasets, curated input/output pairs that turn "seems fine" into a measurable pass/fail.
The gap: No regression baseline = ship-and-pray
Continuous Evaluation: Put a Quality Gate Between Your AI and Production
You'd never deploy code without CI. Yet prompt and model changes ship ungated. A continuous eval gate blocks the regression before users meet it.
The gap: Ungated AI changes ship regressions
Test Your AI for Bias Before a Regulator (or Reporter) Does
Disparate-impact failures in hiring, lending, and pricing AI are now enforcement and headline risk. Bias eval makes the invisible measurable.
The gap: Discrimination findings → fines & reputational hit
The EU AI Act Wants Evidence. Your Evals Are That Evidence.
High-risk AI systems must show testing, accuracy, and robustness evidence. Ad-hoc QA won't pass a conformity assessment, a documented eval pipeline will.
The gap: Up to 7% of global turnover in penalties
Red-Teaming Your LLM: Find the Jailbreak Before Your Users Post It
A single screenshot of your bot saying something awful is a brand crisis. Systematic adversarial eval finds the failure before it goes viral.
The gap: Viral jailbreak = brand & trust damage
Prompt Injection Is a Data Breach Waiting to Happen
When your agent reads untrusted content, that content can hijack it. Injection eval treats every external input as a potential attacker.
The gap: Agent exfiltration & unauthorized actions
Your LLM Is About to Leak PII. Are You Testing for It?
Models repeat back, infer, and occasionally fabricate personal data. PII-leakage eval catches the disclosure before it becomes a notification obligation.
The gap: Breach-notification cost & regulatory exposure
Did the Agent Call the Right Tool? The Eval Everyone Skips
Agents fail not by saying the wrong words but by taking the wrong action. Tool-selection and argument eval is the difference between helpful and harmful.
The gap: Wrong tool calls = real-world side effects
Multi-Agent Systems Fail in the Handoffs, Not the Agents
Each agent passes the unit test; the system still fails. Error compounds across handoffs, and only system-level eval catches it.
The gap: Compounding errors break multi-agent flows
When Your RAG Cites a Source That Doesn't Say That
Past three retrieval hops, the odds of a wrong citation jump from 12% to 31%. Citation-accuracy eval is non-negotiable for anything fact-bearing.
The gap: Misattributed facts in regulated answers
Groundedness: The One Metric That Actually Stops Hallucination
Relevance isn't enough, an answer can be relevant and still made up. Groundedness measures whether every claim traces to retrieved evidence.
The gap: Ungrounded claims = liability in every answer
Your AI Returns JSON, Until the One Time It Doesn't
A single malformed object breaks the downstream pipeline. Structured-output eval asserts schema conformance on every change, not just in the demo.
The gap: Malformed output crashes downstream systems
Over-Refusal: When Your "Safe" AI Refuses to Do Its Job
Safety tuning that refuses legitimate requests quietly destroys product value and user trust. Refusal-rate eval catches the overcorrection.
The gap: False refusals kill product value & retention
The Summary That Added a Fact: Faithfulness Eval for Summarization
Summarizers don't just drop details, they invent them. For legal, medical, and financial summaries, an added fact is a liability.
The gap: Fabricated facts in high-stakes summaries
A/B Testing Models in Production Without Shipping a Regression
Swapping models live is a gamble unless you measure quality, not just latency and cost. Here's how to compare models on the metrics that matter.
The gap: Blind model swaps regress quality silently
The Cheapest Model That Passes: Eval-Driven Model Selection
You're probably overpaying for a frontier model on a task a cheaper one handles. Eval turns model selection from brand loyalty into a measured decision.
The gap: Overpaying for capability you don't use
You Can't Eval Your Way to Quality Without Production Observability
Offline evals miss the inputs real users send. Sampling and scoring live traffic closes the loop between "passed the test" and "works in the wild."
The gap: Offline-only eval misses real-world failures
Shadow Deployment: Test the New Model on Real Traffic, Risk-Free
Run the candidate model in parallel on live inputs, score it against the incumbent, and promote only on evidence. The safest way to upgrade.
The gap: Blind cutovers risk user-facing regressions
Fine-Tuning Fixed One Thing and Broke Three: Catching the Regression
Fine-tuning to improve one behavior often degrades others you weren't watching. A full eval suite catches the trade you didn't mean to make.
The gap: Fine-tuning silently regresses other tasks
Calibration: Teaching Your AI to Say "I Don't Know"
A model that's confidently wrong is worse than one that abstains. Calibration eval measures whether stated confidence matches real accuracy.
The gap: Overconfident wrong answers drive bad decisions
Your Bot Aces One Turn and Fails the Conversation
Single-prompt evals miss the real failure surface: context loss, contradiction, and drift across a multi-turn dialogue. Eval the whole conversation.
The gap: Multi-turn breakdowns frustrate & churn users
Healthcare AI Eval: Where "Mostly Right" Is a Safety Event
Clinical AI can't be graded like a chatbot. Domain-specific eval with clinician-aligned rubrics is the line between decision support and patient harm.
The gap: Patient-safety events & malpractice exposure
Financial-Services AI Eval: Model Risk Management for LLMs
Regulators already govern model risk. LLMs in lending, advice, and disclosure need eval evidence that maps to SR 11-7-style expectations.
The gap: Model-risk findings halt AI deployments
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Hit · Load & Latency
31 reports
Live
HIPAA BAA Violations in Cloud LLM Prompt Caching: Preventing ePHI Leaks in Clinical AI
Up to $2,067,813/yr in civil monetary penalties
Live
When the Stream Drops at Token 500: Streaming Failures Under Load
Silent partial responses tank trust & retention
Live
Inter-Token Latency: The Stutter Your Users Feel But You Never Measure
Perceived-slowness churn invisible to APM
Live
429 Cascade: How Provider Rate Limits Become Your Outage
Rate-limit incidents = full-feature downtime
Live
Capacity Planning for AI: Stop Counting Requests, Start Counting Tokens
Mis-sized infra = overspend or brownouts
Live
Context Window Exhaustion: The Failure That Only Shows Up Under Load
Silent quality decay → hard failures at peak
Live
p99 Latency Is Your Brand: Why Averages Lie About AI UX
Tail-latency users churn and complain loudest
Live
Your Prompt Cache Hit Rate Collapses Exactly When You Need It
Cache assumptions break the cost model
Live
Circuit Breakers for AI: How to Fail Without Failing the User
No breaker = full outage instead of graceful degrade
Live
Multi-Provider Failover: Test It Before the Outage Tests You
Untested failover = worse outage than no failover
Live
Goodput, Not Throughput: The Only Load Number That Matters
Optimizing throughput while losing users
Live
Token-Aware Rate Limiting: The Guardrail Request Limits Can't Provide
Request-based limits let cost explode
Live
Your RAG Pipeline Has Four Latency Bombs and Load Finds All of Them
Stacked stage latency blows the budget
Live
Vector DB Load Testing: Recall Quietly Drops as QPS Climbs
Silent recall loss degrades every answer
Live
Voice AI Has a 300ms Budget. Here's How to Spend It Under Load
Latency over budget = unusable voice product
Live
Load Testing the Realtime API: WebSockets Break Differently
Connection ceilings cap concurrent users
Live
The Metric Your CFO Wants: Cost Per Conversation Under Load
Broken unit economics scale your losses
Live
Soak Testing AI: The Leaks That Only Show Up After Hour Six
Slow leaks = 3am pages days after launch
Live
Put a Load Gate in CI: Block the Deploy That Slows Your AI
Latency regressions ship to prod undetected
Live
Why the Fastest LLM Load Test Runs in Your Browser
Setup friction means the test never gets run
Live
Tool Calls Double Your Latency. Did You Load-Test Them?
Tool round-trips blow the latency budget
Live
Multimodal Load Testing: Big Payloads Break Small Assumptions
Payload size breaks the cost & latency model
Live
GPU Saturation: The Cliff Your Self-Hosted LLM Falls Off
Hitting the GPU cliff in prod = hard outage
Scan · Site Quality
30 reports
Live
The Confused Deputy in Federated MCP: Preventing Cross-Server Privilege Escalation
Data loss, unauthorized mutations & SOC 2 failure
Live
The ADA Lawsuit Tax: When an Unscanned Page Becomes a $75,000 Demand
$45k-$200k per ADA web accessibility claim
Live
Invisible to AI: Why ChatGPT Can't Cite Your Site
Missing the channel that converts 2-4x better
Live
Core Web Vitals Are Cash: The Milliseconds Killing Your Conversion Rate
~$500k/yr per 500ms on a $10M store
Live
llms.txt: The File That Tells AI How to Read You (You Don't Have One)
AI mis-describes your brand to buyers
Live
The European Accessibility Act Is Live. Is Your Site a Liability?
EAA fines + EU market exclusion
Live
Accessibility Overlays Are a Lawsuit Magnet, Not a Shield
Overlays attract, not deflect, litigation
Live
Your JavaScript Framework Is Eating Your SEO and Your AI Visibility
Empty pages to bots = lost organic & AI traffic
Live
Your robots.txt Is Accidentally Blocking the AI Traffic You Want
Blocking AI crawlers erases AEO upside
Live
The API Key in Your Frontend: A Scan Finds It Before an Attacker Does
Leaked credentials → breach & disclosure cost
Live
Missing Security Headers: The Free Hardening You Skipped
Failed security review stalls enterprise deals
Live
Open Redirects: The Phishing Vector Hiding in Your Login Flow
Brand abused in phishing; trust & takedown cost
Live
AI Forgets You in 90 Days: Content Freshness as an AEO Signal
Stale pages drop out of AI answers
Live
Redirect Chains Are Stealing Your Speed and Your Link Equity
Latency + lost link equity on every hop
Live
Color Contrast: The #1 WCAG Failure Hiding in Your Brand Palette
Most common cited WCAG violation in suits
Live
Alt Text Is Doing Double Duty: Accessibility and AI Discovery
WCAG failure + lost multimodal AI discovery
Live
Third-Party Scripts Are the Performance Tax You Forgot You're Paying
Script bloat tanks CWV & conversions
Live
Web Fonts Are Causing the Layout Shift That Costs You Sales
CLS-driven misclicks & conversion loss
Live
Canonical Chaos: How Duplicate URLs Split Your Ranking Power
Split ranking signals suppress every page
Live
Your Cookie Banner Is Lying: Consent Gaps That Invite GDPR Fines
GDPR enforcement on pre-consent tracking
Live
Mobile Performance Is Where Your Revenue Actually Lives
Mobile slowness leaks majority-traffic revenue
Live
TTFB: When Your Server, Not Your Frontend, Is the Slow Part
Server latency caps every other optimization
Live
Share of Voice in AI Answers: The Metric Your CMO Doesn't Have Yet
Invisible pipeline loss in AI recommendations
Live
Orphan Pages: The Content You Paid For That Google Can't Find
Paid-for content never gets indexed
Live
Mixed Content and SSL Gaps: The Padlock That Isn't
Browser warnings scare off buyers at checkout
Eval · Output Quality
30 reports
Live
EU AI Act Article 15 Compliance: Quantitative Audit Protocols for Accuracy, Robustness, and Cybersecurity
Up to €15M or 3% of global turnover in penalties
Live
The Hallucination That Cost a Million: Why Spot-Checks Aren't Enough
Direct liability for AI misstatements
Live
Silent Model Updates Are Breaking Your AI (And You Won't Know for 14 Days)
14-18 day detection lag on silent regressions
Live
RAG's Silent Failure: Confidently Wrong, Caught Too Late
68% hit a customer-impacting silent failure
Live
LLM-as-Judge: Can You Trust the Judge Grading Your AI?
Untrusted judges greenlight bad releases
Live
Prompt Drift: Your Prompt Didn't Change, But Its Behavior Did
Untracked prompt edits cause silent quality loss
Live
Golden Datasets: The Regression Suite Your AI Has Been Missing
No regression baseline = ship-and-pray
Live
Continuous Evaluation: Put a Quality Gate Between Your AI and Production
Ungated AI changes ship regressions
Live
Test Your AI for Bias Before a Regulator (or Reporter) Does
Discrimination findings → fines & reputational hit
Live
The EU AI Act Wants Evidence. Your Evals Are That Evidence.
Up to 7% of global turnover in penalties
Live
Red-Teaming Your LLM: Find the Jailbreak Before Your Users Post It
Viral jailbreak = brand & trust damage
Live
Your LLM Is About to Leak PII. Are You Testing for It?
Breach-notification cost & regulatory exposure
Live
Did the Agent Call the Right Tool? The Eval Everyone Skips
Wrong tool calls = real-world side effects
Live
Multi-Agent Systems Fail in the Handoffs, Not the Agents
Compounding errors break multi-agent flows
Live
Groundedness: The One Metric That Actually Stops Hallucination
Ungrounded claims = liability in every answer
Live
Your AI Returns JSON, Until the One Time It Doesn't
Malformed output crashes downstream systems
Live
Over-Refusal: When Your "Safe" AI Refuses to Do Its Job
False refusals kill product value & retention
Live
The Summary That Added a Fact: Faithfulness Eval for Summarization
Fabricated facts in high-stakes summaries
Live
A/B Testing Models in Production Without Shipping a Regression
Blind model swaps regress quality silently
Live
The Cheapest Model That Passes: Eval-Driven Model Selection
Overpaying for capability you don't use
Live
You Can't Eval Your Way to Quality Without Production Observability
Offline-only eval misses real-world failures
Live
Shadow Deployment: Test the New Model on Real Traffic, Risk-Free
Blind cutovers risk user-facing regressions
Live
Fine-Tuning Fixed One Thing and Broke Three: Catching the Regression
Fine-tuning silently regresses other tasks
Live
Calibration: Teaching Your AI to Say "I Don't Know"
Overconfident wrong answers drive bad decisions
Live
Your Bot Aces One Turn and Fails the Conversation
Multi-turn breakdowns frustrate & churn users
Live
Healthcare AI Eval: Where "Mostly Right" Is a Safety Event
Patient-safety events & malpractice exposure
Live
Financial-Services AI Eval: Model Risk Management for LLMs
Model-risk findings halt AI deployments