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Built by a principal engineer, for engineers who work with technical documents. Runs on Anthropic models. Every verdict cites its sources.
Blog · Marcin Kolny · · Analyzed 14 Jun 2026· 5 hypotheses · 3 problems · 31 entities
Automated quality monitoring can be embedded directly into live video delivery pipelines, where detectors flag perceptual defects as they occur, and results are split across fast alert channels and durable storage to meet both real-time and audit needs. Keeping processing stages inside a single container with shared memory cuts inter-stage latency to near zero, and this trade-off between efficiency and scalability is well understood...
At a glance
Hypotheses
Deploying automated perceptual quality analysis on every customer stream enables real-time defect detection and remediation... assurance from reactive testing to proactive continuous monitoring... Continuous in-stream perceptual defect detection for block corruption and audio-video sync is a well-deployed but the claim that detection enables real-time remediation is unsubstantiated by any cited source...
Prior art established · confidence high · 5 citations · addresses 0 problems
Every technology decision starts with reading: the vendor's whitepaper, the “we migrated and cut costs 40%” blog post, the benchmark that somehow always favors its author. You can't take any of it at face value.
So you check it claim by claim, tab by tab, hours per document. Or you don't, and hope the claim you skipped isn't the one that fails in production.
A summary compresses the document; it doesn't challenge it. If the source is wrong, the summary is wrong faster.
Assay reads the full document and isolates every substantive claim the author stated or implied. It doesn't summarize. It builds a claim inventory. Typically 9 - 12 claims per document.
Each claim gets its own research run against public sources: documentation, independent benchmarks, issue trackers, papers, postmortems. Not a single search, an agent per claim!
You get a paper: every claim, a verdict (Supported / Contested / Contradicted / Unverifiable), the evidence, and links to every source, so you can check the checker.
Typical run time: 9–10 minutes for a 5-page document.
Long enough to
get coffee, short enough to run before the meeting.
Blog · Marcin Kolny · · Analyzed 14 Jun 2026· 5 hypotheses · 3 problems · 31 entities
Automated quality monitoring can be embedded directly into live video delivery pipelines, where detectors flag perceptual defects as they occur, and results are split across fast alert channels and durable storage to meet both real-time and audit needs. Keeping processing stages inside a single container with shared memory cuts inter-stage latency to near zero, and this trade-off between efficiency and scalability is well understood...
At a glance
Hypotheses
Deploying automated perceptual quality analysis on every customer stream enables real-time defect detection and remediation... assurance from reactive testing to proactive continuous monitoring... Continuous in-stream perceptual defect detection for block corruption and audio-video sync is a well-deployed but the claim that detection enables real-time remediation is unsubstantiated by any cited source...
Prior art established · confidence high · 5 citations · addresses 0 problems
Analysis overview as "one-pager"
Scaling up the Prime Video audio/video monitoring service and reducing costs by 90% · Hypothesis 1 of 4
The claim is that embedding automated perceptual quality detectors (block corruption, audio/video sync, video freezes) directly into the streaming delivery pipeline enables continuous, proactive defect remediation rather than reactive post-delivery testing. This mechanism is extensively documented in existing literature and commercial deployments...
Deploying automated perceptual quality analysis on every customer stream enables real-time defect detection and remediation...
Rather than discovering quality issues through post-delivery testing or customer reports, streaming services can embed quality analysis into the delivery pipeline itself. By processing each stream through defect detectors that look for specific perceptual issues—such as block corruption, audio/video sync problems...
Assumptions · 3
Evidence analysis
Deploying automated perceptual quality analysis on every customer stream enables real-time defect detection ... that look for specific perceptual issues—such as block corruption, audio/video sync problems, and video freezes
Evidence The Amazon Science Prime Video blog explicitly states that detectors for block corruption, audio artifacts, and audio-video synchronization errors are deployed as quality assurance tools on live streams, directly naming the same defect categories the claim enumerates.
Analysis The evidence describes the identical mechanism under an operational implementation rather than a theoretical framing; the claim is not novel but is a restatement of an already-deployed industrial practice confirmed by a primary industry source.
Citations · 5
Inside a hypothesis
Real output, truncated for the page. See the full analysis below or run your own document.
Verdicts are evidence-backed judgments, not truth oracles. That's why every verdict links its sources so you can disagree with it.
When no independent source exists, Assay says so instead of guessing. In practice, knowing which claims rest on the author's word alone is half the value.
Assay checks what the document says against what the internet knows. Whether the technology fits your context is still your call. The analysis just makes it an informed one.
If your job is deciding, and deciding means reading things you don't fully trust, Assay is for you.
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A note from the founder
I'm a principal engineer at a large e-commerce company. A meaningful part of my job is writing decision documents and every one of them starts with reading material I can't take at face value.
I used to spend half of a day fact-checking a single vendor whitepaper before I'd cite it in my work. So I built a pipeline that does the tedious part: pull out every claim, research each one, show me the evidence. I've used it for my own decisions for over a year. Assay is that pipeline, cleaned up so you can use it too.
It's a small product built by one person. It charges from the first document because each analysis runs frontier models hard but you'll always see a full example analysis and the exact price before you commit a cent. If it saves you one bad architecture bet, it's paid for itself for years.
— Dmitry
Run the whitepaper through Assay before your next decision meeting.
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