Your dashboards are green. Your AI is lying to you.
Traditional software fails loud. AI fails quiet — drift, hallucination, stale data, unseen threats — while every dashboard stays green. Vivanti builds the observability layer that makes silent failure loud, in real time, on ClickHouse.
AI fails silently. We make it audible.
A Vivanti whitepaper, in collaboration with ClickHouse
The four silent failures
Conventional monitoring was built to catch things going down. AI’s most expensive failures happen while everything is still up.
Stale data
Fresh-looking answers built on inputs that expired days ago. Nothing alarms.
Drift
Quiet performance decay as the world shifts under the model. By the time metrics move, trust has eroded.
Hallucination
Fluent, confident and false — the hardest failure to spot, precisely because it looks right.
Security blind spot
You can’t stop the intrusion or the data leak you never saw.
99%of organizations reported AI-related financial losses, averaging about $4.4 million each. EY Responsible AI Pulse Survey, 2025.
Observability is the smoke detector for AI.
Conventional monitoring watches for systems going down. But your AI’s worst failures happen while everything is still up. A smoke detector doesn’t wait for the fire — it watches for the smoke. AI-era observability continuously verifies your data, your AI and your security, and turns silent failure into an alarm you can actually hear.
Data, AI and security — on one substrate
Data observability
Freshness, quality, lineage and access — because AI inherits the condition of its data.
- Freshness SLOs
- Lineage
- Governance
AI observability
Drift, hallucination, evaluation and cost: the full request-and-response record of every model and agent.
- Drift bounds
- LLM traces
- Token cost
Security observability
Threats and misuse, detected over the same telemetry — the records that reveal drift also reveal intrusion.
- Access anomalies
- Prompt injection
- OCSF
Same incident. Two views.
Inject a silent failure and watch what conventional monitoring reports — then what AI-era observability reports.
Conventional monitoring
Is the system up?
- Service uptime100%
- Error rate (5xx)0.02%
- p95 latency388 ms
- Pipeline jobs succeeded24 / 24
All conventional signals inside tolerance. This panel looks identical whether the system is right or wrong.
Vivanti observability on ClickHouse
Is the system right?
- Data freshness, all 38 sourcesWorst source 4 min behind SLO windowOK
- Input distribution driftPSI 0.04 vs 30-day baselineOK
- Grounded-answer rateCitations resolving to live sources98%
- Anomalous accessNo deviation from role baselines0 OPEN
All three layers verified against declared objectives in the last 60 seconds. Green here means checked, not merely up.
Illustrative demonstration. Signal names and thresholds are configured per estate during a Vivanti engagement.
Why this runs on ClickHouse
Watching everything only works if watching everything is affordable — and AI-scale telemetry bankrupts legacy tools. OpenAI reportedly pays around $170M a year for Datadog. Most enterprises respond by watching less: sampling, dropping detail, shortening retention. That’s exactly how silent failures slip through.
ClickHouse flips the economics: real-time, columnar, petabyte-scale, sub-second queries, and a reported 10–100× cost saving. It anchors the whole stack.
- ClickStack
- Open observability for logs, metrics and traces, built on OpenTelemetry.
- ClickStack AI
- Observability built for AI systems and agents.
- Langfuse
- The leading open-source LLM-observability platform, acquired January 2026.
Cost of watching everything
What retention actually costs
- Legacy per-GB tooling
- $350k/yr
- ClickHouse-based stack
- $14k/yr
$336k saved per year — the difference between watching a sample and watching everything.
Indicative model only, at $2.40/GB ingested for legacy per-GB pricing and a 25× efficiency assumption within ClickHouse’s published 10–100× range. Not a quote. Real figures depend on schema, cardinality and tiering.
Seeing is believing
Anthropic
Describes ClickHouse as “instrumental” to developing and shipping Claude 4, running AI-era observability with lightning-fast queries.
Source: ClickHouse
Character.AI
Cut observability query latency from one to two minutes down to near-instant, across thousands of GPUs.
Source: ClickHouse
Sony
Ingests tens of millions of streaming events for real-time quality monitoring.
Source: ClickHouse
Vivanti
Data preparation and activation at scale for Electronic Arts, Pepperstone, Pacific National, and a US government document-intelligence program.
Source: Vivanti
If your AI drifted tomorrow, would you know?
Six honest questions about one AI system you run in production. Nothing is sent anywhere.
- 01
You can state, from a live signal, how fresh every data source feeding this system is right now.
- 02
You would be alerted if the model’s input distribution shifted materially, before an output looked wrong.
- 03
Every model and agent request and response is captured and queryable, with the retrieved context attached.
- 04
You have a written definition of “healthy” for this system — freshness windows, drift bounds, quality thresholds.
- 05
You can answer, within minutes, who or what accessed a given record and which sources produced a given output.
- 06
You retain enough telemetry, for long enough, to reconstruct a decision made three months ago.
Visibility score
?
Answer the six above
Be honest rather than generous — the point of the exercise is to find the gap, not to pass.
Observability for the AI Era: Detecting Silent Failure in Data and AI Systems
A Vivanti whitepaper, in collaboration with ClickHouse. Written for the data, AI, risk and security executives running AI systems in production.
- 01The silent-failure problem: stale data, drift, hallucination and the security blind spot
- 02Why AI changes observability — from monitoring downtime to observing correctness
- 03Data, AI and security observability, defined as one discipline
- 04A seven-step blueprint, from defining “healthy” to governed operations
- 05The ClickHouse reference stack, and the economics of watching everything
- 06A five-stage roadmap, and the barriers that stall programs
Built for regulated, high-stakes industries
Banking, financial services & insurance
Media, entertainment & sports
Supply chain & logistics
Health & life sciences
Government & education
Bring one AI system you’re not sure you can trust.
Give us 30 minutes. We’ll map what you can and can’t currently see across your data, AI and security — and where silent failure could be hiding right now. No slideware, no obligation.
Book a 30-minute sessionPrefer to write first? jules.trono@vivanti.com