Production Reliability Governance for the AI Era
Ship at AI speed.
Stay Reliable in Production.
Tomosu continuously scores every application and closes its reliability gaps across development, pre-merge and runtime, using the Production Reliability Index (PRI) and a multi-tier agentic system.
Prevent incidents Resolve L1/L2 automatically Learn from every escalation
No credit card. Read-only by default. Evaluating for a team? Book a call →
One unified reliability loop for Engineering, SRE & Support.
Proof
One deployment, forty points of PRI.
PRI improvement from a customer deployment
One trendable number, moved by closing the gaps the governance layer found.
73%reduction in support escalations
50%cut in post-deployment failures
$2.4Mannual support cost savings
Industry First The first AI‑powered platform that embeds governance intelligence across your entire development‑to‑production lifecycle. IDE → CI/CD → Production
Platform ROI Engine
Tomosu Business Value Simulator
$
Total Spend Without Tomosu
Combined engineering payroll and standard support desk overhead.
$0
Total Savings With Tomosu
ROI: 0%
Engineering capacity recovered from governance overhead + support deflection savings, net of platform cost.
$0
Projected Spend (Target Efficiency)
Optimized runtime budget including platform licensing fees.
$0
Projected SWE Headcount Equivalency
Headcount capacity required to deliver identical roadmaps under Tomosu governance.
0 SWEs
Functional Savings Attribution
Recovers ~10% of eng capacity lost to governance rework & review churn — equivalent to 0 FTEs redirected to features.
Deflects ~25% of support spend via automated routing and proactive governance signals before tickets escalate.
*Modeled estimates based on standard benchmarks — not a contractual guarantee.
Tomosu AI — Free Edition
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A new category
AI Governance Layer. Not another linter, APM, or code assistant.
AI assistants are the gas pedal. Observability is the rear-view mirror. Tomosu AI is the braking system, policy plane, and risk ledger: the layer every enterprise is about to require.
We call this category AI code governance. It is not what AI code review tools do. Review tools comment on a diff; an AI code governance layer decides what reaches production, scores the risk, and keeps the evidence. More on the missing layer and why AI-generated code needs different pre-screening.
Not an AI code assistant
Not a static analyzer
Not an APM or logging tool
Not a PR review chatbot
Not a CI pipeline runner
Not a ticketing or ITSM platform
Why Tomosu wins
Point solutions don’t speak a common governance language. Tomosu is that language.
🔒
Governance built for AI, not retrofitted
Policy, identity, approval, and audit designed from day one for AI-generated change, not forced onto workflows built for humans.
🔄
Closed loop between code and production
Live incidents become guardrails in the IDE. The governance layer gets smarter every week, without your team writing new rules.
📊
One risk ledger every stakeholder reads
PRI is the single trendable number the CTO operates, the CFO budgets against, and the board tracks. No more translation layers.
👁
Sits above your existing stack
Works with the Git, observability, and ticketing tools you already run. Read-only by default. Enforcement stays under your control.
🚨
Escalations the right tier can actually act on
Incidents arrive with root-cause context, a likely fix, and an evidence trail. L1 solves what only L3 could before.
🏛
Audit-ready by default
Every governance decision is logged with evidence, ready for SOC 2, ISO, or internal AI-use policy reviews without a fire drill.
Pilot outcomes
The numbers your board, your CFO, and your on-call team all care about.
Conservative targets for mid-size SaaS with 50–300 engineers, 24/7 production workloads, and meaningful AI-assisted PR volume.
Estimated up to
73%
reduction in support escalations
Estimated up to
68%
decrease in engineering tickets
Estimated up to
40+ hrs
developer time reclaimed weekly
Estimated up to
50%
cut in post-deployment failures
Estimated up to
$2.4M
annual support cost savings
Estimated up to
3× faster
innovation delivery cycles
Estimated upper bounds. Actual outcomes depend on baseline metrics, data coverage, and adoption of recommended actions.
Go deeper
How the governance layer works, and what it’s worth.
The Platform
One governance layer. Four capabilities.
The agent architecture — VisionOps, RapidSense, RootView, Knowledge, Ticket and the Unified Runner — and the eight Indexes every AI-generated change rolls up into.
Read →How it works
From first keystroke to live production.
The four-step walkthrough across development, pre-merge, deployment and runtime, plus the Live Governance Impact Simulator.
Read →Built for the full C-suite
One governance layer every stakeholder reads.
What the CTO, CIO, CFO and CCO each get from the same risk ledger, and the number each of them operates on.
Read →The Burning Platform
Why governance, and why now.
Six months of market data, and the incident record behind it: Amazon, Replit and Firetiger.
Read →Platform ROI Engine
Model what governance is worth to you.
The Business Value Simulator: engineering rework, support spend, and the payback on a Tomosu deployment.
Read →Before you ask
Every question, answered in full.
All 22 questions from engineering leaders — merge gates, scoring, data access, and what happens when Tomosu is wrong.
Read →Before you ask
Common questions from engineering leaders.
Is the merge gate blocking or advisory?
Your choice. Most teams start advisory, see the risk ledger take shape over 30 days, and promote specific policies to blocking once they’ve built trust in the signal.
Does Tomosu replace Copilot, Cursor, or our observability stack?
No. Tomosu sits above them. Your engineers keep their AI assistants. Your SRE team keeps Datadog, New Relic, or Dynatrace. Tomosu is the governance layer that unifies signal across all of them into one risk ledger.
We have senior engineers. Can’t we just build this ourselves?
The LLM call is the easy part. What takes 12–18 months: a 25-rule scoring matrix with empirically calibrated critical caps, SHA-256 file hashing to skip unchanged code, token-count batching to avoid context window overflows, error fingerprinting to prevent hundreds of duplicate tickets from one root cause, semantic deduplication via vector embeddings, and per-user spending limits. The weights in the scoring matrix only become credible after you’ve correlated them against real production incidents—data you don’t have until after your first outage.
What happens when Tomosu is wrong? How do we trust a score enough to gate merges on it?
You should not trust it on day one, and the product does not ask you to. The gate starts advisory: Tomosu publishes verdicts alongside your existing process while you compare them against what your reviewers would have decided. You enforce when the evidence earns it, not because a vendor switched it on.
Three things make that comparison honest. Every verdict carries the reasoning and the evidence behind it, so it can be argued with rather than obeyed. Scores are calibrated to your organization instead of a generic industry baseline. And the projected confidence score is hard-capped at 85, so the system is structurally prevented from overselling its own fixes. Humans keep the override, always.
The governance layer the enterprise is about to require.
For engineering leaders ready to turn AI-accelerated velocity into an auditable, board-defensible risk ledger, before the next Amazon-scale incident becomes yours.