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AI Observability

Trace every turn. Test each change.

AI Observability keeps every call on record, shows which stage or tool failed, and compares each new version with the one before it, before a person decides.

Book a DemoSee how it runs
  • Every call on record
  • Failures by cause
  • Versions compared
Story01 / 10

The harness under the runtime.

The loop itself, then three scenes from the operations owner’s side: the failure with no cause, the record that names it, and the change decided on evidence.

  1. The loop, end to end

    Every call recorded, scored against the rubric, then compared.

  2. A failure, no cause

    Something failed, and nothing says which stage or which tool.

  3. Scored and compared

    Kept with its transcript, scored after the call, then compared.

  4. Decided on evidence

    Every version change carries the comparison it was decided on.

A runtime disc with a slate top ringed by six pucks, a track running out to a record spool on the left, under a low arch at the front, past a switch on the right, and back to the runtime.
  1. The runtimeEvery call runs through the runtime and leaves a record.
  2. The recordEvery call keeps its transcript, its events and its tool results.
  3. The comparisonEach new version runs the same scenarios as the one before it.
  4. The decisionThe comparison is shown, and a person decides what goes live.

Ask us for a walkthrough of the loop

Illustrative scene, not a product capture

Capability 1 of 4: The loop, end to end

Four layers02 / 10

Sixteen signals, every call, not a sample.

Audio, model, tool and policy are watched on every call.

  • 01

    Audio

    Line quality, and whether the voice is who it claims.

    • Signal to noise

    • Recognition confidence

    • Codec health

  • 02

    Model

    What the model said, how sure it was, and whether it drifted.

    • Hallucination detection

    • a confidence score from 0 to 100

      LLM layer

    • Semantic grounding

  • 03

    Tools

    Whether the tools answered, and how the integration held.

    • tool success rate on a rolling 5 minutes

      tool layer

    • Slowest tool responses

    • Error chain trace

  • 04

    Policy

    What policy allowed, what was masked, and how the brand read.

    • Guardrail checks

    • Personal data detection

    • Immutable audit log

detection in under 1 second across all four layers

hallucination, intent drift, grounding, slowest tool responses, integration health, PII, brand adherence

AI Observability

Four layers of one call, watched together

100% of calls monitored and scored, not a sample

Capabilities03 / 10

See the call, score the run, compare the versions.

Five product demonstrations, from the service view to the release decision, playing as the product plays on sample data.

The observability overview for one agent: a service map from telephony through speech, model and tools to the systems of record, with its indicators, alerts, logs and active traces.
  1. Service and healthRecognition, model, speech, transport and tools on a single view.
  2. One call as a traceWhich stage or tool the delay or the failure came from, span by span.
  3. Scored on a rubricRecorded calls scored against the rubric, each failure opened.
  4. Synthetic callersImpatient, repeat and social-engineering callers, run as scenarios.
  5. The release decisionScenario results beside the release decision, version by version.

Ask us what the service view reports

Product demonstration with sample data

Capability 1 of 5: Service and health

How it runs04 / 10

One recorded call, one tested change.

The path after the call: records are scored against a rubric, failures are grouped by cause, and a draft version is compared with the live one before a person decides.

One recorded call, one tested change.The path after the call: records are scored against a rubric, failures are grouped by cause, and a draft version is compared with the live one before a person decides. The steps, in order: Conversation, Recorded trace, Scoring, Metrics per workflow, Failure analysis, New draft version, Release decision.Conversationa customer callRecordedtraceturns and toolsScoringafter the callMetrics perworkflowgrouped by taskFailureanalysisgrouped by causeNew draftversionthe changeReleasedecisiona person decidesOne record per callTurns, events and toolsAgainst a rubricThe same categoriesRolled up per taskNot per callEach failure openedWith the call’s recordChange in one placeA version, not a patchDecided on evidenceThe comparison in view

Evidence out05 / 10

Evidence goes where your teams already look.

The record leaves the runtime in the formats your tools read.

Customer collector

Standard traces over OTLP.To the collector you run.

Log platforms

A dedicated output carries evidence.It enters the customer’s log platform.

SplunkElasticsearchKibana

Security operations

A dedicated output carries evidence.Events map to MITRE ATT&CK.

SplunkQRadarSentinel

Native view

Included with the runtime.Covers all four layers.

On-call

Alerts follow customer-owned rules.They reach the on-call service.

PagerDutyOpsgenie

Warehouses

A dedicated output carries evidence.It lands in the customer’s warehouse.

SnowflakeBigQueryRedshift
AI Observability

The record leaving the runtime for your own tools

Where it runs06 / 10

Records stay where you deploy.

Recordings, transcripts and traces are stored with the runtime, on your premises, in your private cloud or in Voicing Cloud; the data residency terms are settled in the architecture review.

  • On your premises

    Call records, recordings and traces stay on the racks the runtime runs on, inside your own network.

  • In your private cloud

    Your own tenancy in your own region, reviewed with your security team before the first call runs.

  • In Voicing Cloud

    Hosted by Voicing AI in a region your residency rules allow, with the same records kept beside it.

Regulator-ready07 / 10

A record a regulator can read.

Each regulation gets the handling that it actually asks for.

A protected call record is stored and signed before its evidence pack is exportedCall recordMaskedTokensStored, signedEvidence
The call record

Masking at source

Health data masked where it is captured.

Card tokenisation

Card numbers become tokens before storage.

Protected before storage

Cryptographic signing

Signed at write, so any change is detectable.

Lineage and erasure

Tracks each record to its source. Erasure reaches the source.

Retention policy

Each regulation keeps its own clock.

Regulator export

One action assembles the auditor’s pack.

  • HIPAAhealthcare deployments, current
  • PCI DSSpayment handling, current
  • SOC 2 Type 2On request, current
AI Observability

The evidence pack a regulator opens

What changes08 / 10

Issues caught before the customer notices.

Watching every call changes what a contact centre catches.

A scheduling coordinator on a headset at a hospital reception desk, the waiting area behind him out of focus.
AI Observability

A hospital scheduling desk on a live call

  • 94% of issues caught before customer escalation

    Detection runs on the call, not on a sample read afterwards.

    measured after deploying Voicing AI Observability

  • 67% reduction in mean time to resolution

    The trace names the hop that failed, so the fix starts there.

    measured after deploying Voicing AI Observability

  • 3.2x fewer compliance audit findings

    The audit log is always on, so evidence is never reconstructed.

    measured after deploying Voicing AI Observability

  • 41% reduction in LLM inference cost

    Redundant tool calls are found and taken out of the path.

    measured after deploying Voicing AI Observability

Proof09 / 10

AI Observability

Ask us to walk through one call record and one evaluation run on your own scenarios.

Talk to an engineer

Closing10 / 10

Bring one call type. Leave with an architecture.

A white suite case opening on six coloured discs, the product surfaces, rising between its lid and its base.
AI Observability

Every turn traced to its cause

A working session with an engineer who has deployed inside a bank’s perimeter. We map your telephony, data boundary and handoff rules, and tell you what we would not automate.

Book a Demo