AI you can trust.trust.
The operating system enterprises use to trust, understand, and operate AI at scale — safely and efficiently.
The gap
Your logs say what happened. Not why.
Spans, tokens, latency — never what the answer relied on, whether it holds up, or what it cost.
All accounted for — yet none of it tells you why.
Enterprise AI
AI is in production.
Proof isn't.
Every ~10% price cut is absorbed by 12–18% more consumption. Optimize token yield.
of generative-AI projects are abandoned after proof of concept — cost, data quality, and unclear value.
Source · Gartner
of agentic-AI projects will be canceled by 2027 — escalating costs and unclear business value.
Source · Gartner
more tokens consumed year over year — even as prices halved. Cheaper tokens, heavier bills.
Source · Bain & Co. · via Fortune
This is the gap Evigauge closes. Provenance, not promises.
Why Evigauge
From black box to full provenance.
Four stages. One platform.
Most teams are stuck between “we use AI” and “we can prove our AI.” Evigauge moves you through every stage.
Rebuild how every answer was made.
The query decomposition, the decision graph, the sources cited and dropped — reconstructed from your app's own execution, not a span you have to guess from.
One OS
What you get on day one.
One SDK, four capabilities — full visibility, provenance, cost control, and compliance, reconstructed from your app's own execution.
See what every answer was built from.
What every answer cited, dropped, and never retrieved — verified, not guessed.
What you get: A complete sourcing audit for any answer.
Every decision traced, versioned, replayable.
Every decision traced and versioned — replay any answer, end to end.
What you get: Replay any answer, exactly as it happened.
Not a dashboard. The fix.
The cheaper model and the exact dollars — with the span to change attached.
What you get: The line to change, not the number to stare at.
Set the trust boundary. It's enforced and logged.
Your trust boundary — enforced, logged, and audit-grade.
What you get: Audit-grade evidence, control on the record.
See it running · the real interface
See the product. Actually running.
Four core views — fleet, decision graph, provenance, and cost — cycling through what Evigauge surfaces on your own traffic.
- retriever is 3.1× its learned baseline for this operationflag raised
- tool-agent recovered — the earlier flag resolved itselfauto-resolved
Now showing
Four live views, one platform.
Every agent and tool call across the estate, mapped live — per-operation baselines and auto-resolving flags, zero added latency.
How it works
Live in under an hour.
No engineering required.
Drop in the SDK and the panels reconstruct themselves — provenance, reasoning, and cost, from your app's own execution.
Connect
Drop in the SDK — it instruments your LLM calls, tools, and retrieval. No code changes, live in under an hour.
Discover
See every call across the company, reconstructed by source, reasoning, provider, and cost.
Act
Real-time findings. Catch drift and cost spikes, prove provenance, and act with the exact fix.
One platform.
Every leader's answer.
One platform every leader trusts — the CFO sees the spend, the CISO sees the proof, the team sees the fix.
Get the answers your leadership team is asking for.
Book a demoEnd-to-end deployment
We turn legacy systems into self-sustaining AI.
No rip-and-replace — a governed business ontology around your trusted core, so AI inherits provenance, governance, and trust at every layer.
Self-sustaining
Autonomous Workflows
End-to-end workflows that run, verify, and improve themselves — a self-sustaining, provenance-led AI machine.
What you get: Whole processes run — and improve — on their own, so you get more done without adding headcount.
Fleet monitoringShip CheckGroundingWhere AI acts
Agentic Layer
AI agents that draft, decide, and execute inside those systems — acting alongside your people, never around your controls.
What you get: Agents take the repetitive work off your people, so they focus on the decisions that matter.
ReasoningFleet monitoringPrompt-injection testingSystem of action
Operational Layer
The CRM, ERP, and line-of-business systems your teams already run every day — kept in place, modernised around, not ripped out.
What you get: Your teams keep the tools they already know — the same work just gets faster and cheaper.
PerformanceCost & speedShip CheckSystem of context
Ontology Layer
Your business modelled as a living ontology — entities, relationships, and rules as an executable contract every model and agent must honour.
What you get: The AI actually understands your business, so it gets answers right the first time.
GroundingProvenanceSystem of record
Unified Data Layer
Decades of siloed, legacy data reconciled into one always-current foundation the whole enterprise can trust — every record traceable to its source.
What you get: Everyone finally works from one trusted set of numbers — no more conflicting spreadsheets.
ProvenanceGovernance & securityFoundation
Infrastructure
Deployed in your cloud, private VPC, or fully air-gapped — with enterprise security and governance switched on from day zero.
What you get: You go live without a security battle — everything runs inside your own walls, and nothing leaves.
Governance & securityFleet monitoring
For developers
Every claim lands on a number, a name, or a span.
Per-trace tooling to debug performance and cost — the exact span, the exact dollars. No vibes, no monthly averages.
Know which layer is actually slow.
p50 / p95 / p99 broken out by retrieval, tool call, generation, and guardrail — then drill from the slow phase to the offending span.
“generation is the slow part, not the database”
Here's the line to change.
The correlation engine names the trace where a premium model did simple work, the cheaper model that fits, and the exact dollars.
→ saving priced, span linked
See the whole fleet, not one agent.
Live topology, per-agent tokens, latency baselines learned per operation, and flags that resolve themselves when an agent recovers.
off-band read · zero added latency
A PR gate for prompts.
Catches a broken cache, a bloated prompt, or rising failed responses — and can fail the build. Same diff, same verdict, every time.
runs in CI · deterministic
Plugins
Drop it into the tools you already code in.
Ready-made plugins for the AI coding tools your team already runs — same provenance, cost, and trust, right inside the workflow.
Claude Code
Kilo Code
Google CLI
Kimi Code
Security & deployment
What leaves
what stays
The boundary · 00
Prove every answer stayed inside your walls — attributed, aligned, and audit-ready, with zero data egress.
| Time | Scope | Shape | Event | Where | Status |
|---|---|---|---|---|---|
| 14:02:04 | CI | shipcheck.gate | PR #482 | Passed | |
| 14:02:39 | KB | provenance.verify | source-matrix | Grounded | |
| 14:03:11 | WEB | web.crosscheck | open-web | Blocked · no egress | |
| 14:03:48 | PII | pii.redact | ingest | Redacted | |
| 14:04:20 | ALIGN | query.align | u/j.chen | Within KB | |
| 14:04:55 | RBAC | role.scope | owner/dev | Enforced | |
| 14:05:26 | KB | drift.check | kb-baseline | Within 1σ | |
| 14:05:59 | LOG | audit.write | ledger | Sealed |
Nothing leaves your walls. Every answer attributed, aligned, and on the record.
Read the security detail