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LLM Observability

LLM observability that produces evidence, not just dashboards.

See every request over OpenTelemetry — then get one signed, externally anchored compliance number a day, with drift detection. A “98% pass rate” rendered from your own database is a dashboard. Ours is bytes we signed, and the auditor reproduces it in a browser.

daily_attestation · 2026-06-26

ANCHORED

97.4%

signed daily pass rate

events

18,432

merkle_root

7c1a…d09f

psi_drift

0.06 · stable

anchor

RFC 3161 + Rekor

Everyone in this category produces logs. We produce evidence.

The difference is determinism. An LLM-as-judge on a shared GPU pool gives different verdicts at 95% utilization than at 5% — the score moves and nobody knows why. Our daily number runs on a batch-invariant kernel, so it only moves when your AI’s behavior moves.

Three streams, one correlation ID

Operational telemetry. Security taxonomy. Durable audit.

ops

90 days

Health, latency, errors, performance class. RED metrics per endpoint, exported over OpenTelemetry — no SDK wrapping, no proprietary agent.

security

13 months

A closed, versioned taxonomy — auth, admin, network, data, crypto, compliance — the exact surface a SOC 2 auditor enumerates.

audit

7 years

Durable, hash-chained rows: every policy decision, every governance action, every export — attested evidence, not a log line.

The wedge

Observe. Prove. Intervene. One kernel does all three.

01

Observe

Every request is traced end-to-end over OpenTelemetry, with logs, traces, and metrics stitched by one correlation ID. Plug it into Datadog, Grafana, Splunk, or Sentinel.

02

Prove

Each event returns a signed verdict; every day rolls into one Merkle-rooted, KMS-signed, externally anchored compliance number your auditor reproduces in a browser.

03

Intervene

When drift fires, the same deterministic kernel that observed the event goes inline to block, correct, or mask — observability that can act, not just alert.

The proof underneath every verdict is deterministic replay; the streaming layer is Continuous Assurance; the inline action is AI guardrails.

FAQ

LLM observability, answered

What is LLM observability?

LLM observability is the practice of instrumenting AI applications so you can see what your models and agents did — latency, errors, prompts, outputs, tool calls, drift — and explain it later. Trinitite ships full OpenTelemetry tracing across three streams (ops, security, audit) and, crucially, turns the audit stream into signed, replayable evidence rather than a dashboard you have to be trusted on.

How is this different from Arthur, Fiddler, or a tracing dashboard?

Most LLM observability tools render a dashboard from your own database rows — useful, but not evidence an auditor will accept. Trinitite produces a per-event signed verdict and one signed, externally anchored compliance number per day, on a determinism-fixed kernel, so the number moves when your AI’s behavior moves — not when the GPU pool gets busy. And the same kernel can move inline to intervene.

How does drift detection work?

A nightly roller computes the Population Stability Index (PSI) between today’s pass/fail distribution and a trailing 30-day baseline. At PSI ≥ 0.25 it fires a signed drift webhook — with the structured comparison in the payload — the day a provider swaps a model, a system prompt changes, or the traffic mix shifts. No code change; you copy events from your gateway over one HMAC-signed connector.

Do I have to route production traffic through Trinitite to get observability?

No. Continuous Assurance is a stream copy — one HMAC-signed POST per interaction from your existing gateway, with no inline interception, so production safety is unchanged. When you’re ready to act on what you see, the same kernel becomes the inline runtime enforcement layer.

Stream one connector. Get a signed number tomorrow.

Free first-month pilot: one HMAC-signed connector, up to 100,000 events, daily anchored attestations and PSI drift detection from day one. No proxy, no agent, no production redirect.