Product · how it works

See how every answer was actually made.

Install the SDK, add your keys, and the dashboard reconstructs itself — provenance, reasoning, and cost, from your app's own execution. No prompt rewrites.

From black box to full provenance

Evigauge reconstructs how every AI answer was made — the query decomposition, the reasoning path, the sources cited and dropped, whether the claims were grounded, and where the money went. It reads your app's own execution, so there is nothing to rewrite.

Evigauge workspace overview dashboard: $826.03 of AI spend over the last 30 days, 39.8M tokens, $0.17 average cost per trace, a daily spend trend line, and a cost-anomaly banner flagging cost per trace running 28.8% above the workspace baseline. — The workspace you land in: spend, tokens, and cost per trace across every model and agent, with anomalies raised against your own baseline.

One SDK, one API key per workspace.
Auto-instruments LLM calls, tool calls, retrieval, and decisions.
Standards-native — stock OpenTelemetry exporters work out of the box.

Connect in minutes

Drop in the tracing SDK and add your keys. Evigauge instruments the calls already happening in your app — no prompt rewrites, no re-instrumentation. The panels fill themselves as traffic flows.

providers → instrumented workspace

Reconstruct every answer

For any answer, Evigauge rebuilds the whole picture — not a span waterfall you have to guess from, but the actual decomposition, sourcing, and reasoning behind the result.

Evigauge decomposition view for trace tr_2328: the reconstructed query plan for the question "What drove Reliance's FY24 revenue, and is the margin expansion sustainable?", broken into four sub-tasks, above a plan graph running from query classification through parallel retrieval and cross-verification to two possible outcomes. — Decomposition — the plan the run actually followed, rebuilt from its own execution: sub-tasks, fan-out, and the branches each outcome hung on.

Evigauge decision graph for trace tr_2328: seven reasoning nodes and six relationships mapping observations and considerations through a cross-verification tool call to a final conclusion, with the trace's $0.12 cost, 5.88 second latency, and 187.1K tokens shown above. — Decision graph — every branch, tool call, and dependency behind one answer, node by node, with what the agent was doing stated in plain language.

Source matrix

What was cited, what was dropped, and what the model should have seen but never did.

Decision graph

The reasoning map — branch points, tool-call decisions, and what each step depended on.

Reliability grade

Per-claim grounding: did the source actually support the sentence, or did the model just say it?

Real-link verification

A cited URL that doesn't exist, or doesn't back the claim, gets flagged. Defeats hallucinated citations.

Evigauge provenance view for trace tr_2328: two sources cited, one dropped as outdated, one canonical source recommended but never retrieved, 67% citation density, and per-source authority, relevance, and recency scores for reliance.com and screener.in. — Provenance — what the answer cited, what it dropped, and the authoritative source it never saw, each scored for authority, relevance, and recency.

Evigauge reliability view: four claims extracted from one AI answer and graded supported, contradicted, or unsupported, each with its cited source, confidence and risk scores, and external corroboration or refutation quoted underneath. — Reliability — every claim in the answer checked against the source that was supposed to back it, then corroborated against the open record.

Govern what ships

Set a trust boundary per workspace and keep the work that can't touch the open web inside a closed knowledge base. Every crossing is logged, every agent supervised, and every prompt change gated before it reaches production.

Per-workspace trust boundary with an open-web toggle.
An access audit trail over the tool itself — who saw what, and when.

Fleet monitor

Live multi-agent topology, per-agent tokens, and latency baselines learned per operation.

Ship Check

A PR-time gate for prompts. Flags a broken cache, a bloated prompt, or rising failures — and can fail the build.

boundary crossings, recorded

Evigauge Fleet Monitor: a live topology of a research-assistant agent fleet — Planner, Researcher, Retriever, Tools, Writer, and Critic nodes joined by hand-off edges labelled with call volume — beside a live-flags panel reporting a slow Researcher agent and an HTTP 500 on the Critic-to-Writer hand-off. — Fleet monitor — every agent and tool call in the estate, mapped live, with latency baselines learned per operation and flags that resolve themselves.

Optimize cost and speed

Same models, same quality, lower cost. The correlation engine names the trace where a premium model did simple work, the cheaper model that fits, and the exact dollars — with the offending span attached.

Savings advisor

The cheaper model and the exact dollars, with the span to change — not a number to stare at.

Audit logging

An append-only record of every run, review, and access event — exportable, and sealed so it cannot be edited after the fact.

Prompt optimization

The prompts carrying dead context or a broken cache prefix, with the token saving each rewrite is worth.

Phase-sliced latency

p50 / p95 / p99 by retrieval, tool call, generation, and guardrail — then drill to the offending span.

Evigauge Optimization view: an estimated $179.10 in monthly savings across three flagged pipeline stages, led by routing a synthesis stage from a premium model to a cheaper one for an estimated $118.40 a month, each candidate showing the share of stage spend and the traces it affects. — Optimization — the stage running a premium model on simple work, the cheaper model that fits, and the dollars, with the traces attached.

See it on your own traffic.

45 minutes, your real traces — provenance, reasoning, and cost, reconstructed.

Book a demo

The screens on this page are captures of the live product, shown on sample data. The line diagrams are illustrations rather than captures.