Tomosu scores every production-bound change whether created by developers, copilots, or autonomous agents for production risk before it ships, unifying engineering and support under one governance layer.
Your Observability costs drop, issues auto-deflect before they reach customers, and your engineers stop firefighting and start building the future.
The Tomosu AI plugin gives every developer AI-powered code analysis, live risk scoring, and governance insights free, directly in your editor. Available for VS Code, Cursor, and Antigravity.
GitHub
GitLab
Bitbucket
Datadog
New Relic
Sentry
PagerDuty
Jira
ServiceNow
Zendesk
Linear
Slack
LangChain
Hugging Face
Weights & Biases
MLflow
Honeycomb
Copilot and Cursor write the code. Static analyzers grade the syntax. APMs watch what already broke. Tomosu sits above all of them. It’s the layer that decides what reaches production, scores the risk, finds the change that caused the page, and writes the audit trail.
Under the hood, purpose-built AI agents for policy, risk, context, and evidence work collaboratively in real time. No single model decides alone. Each agent owns a domain, challenges the others, and together they produce a governance verdict no monolithic tool can match.
Every PR clears a streaming evaluation lane: context resolved, policy aligned, risk composed, evidence written. Before merge, not after the page.
Explore the lane ScoreEight calibrated indexes roll up into a single Production Reliability Index. Trendable across quarters. Readable by the CTO, CFO, CIO, and CCO without translation.
See the indexes ResolveThe moment something fails in production, the responsible change is on the table. Engineers stop firefighting. The same failure pattern doesn’t ship twice.
How it works ProveSOC 2, ISO, internal AI-use policy: every governance decision is logged with a defensible audit trail, ready to file the day the auditor asks.
Talk to usSix months of market data makes it impossible to ignore: governance debt is compounding faster than engineering teams can absorb it.
~6.3M lost orders across two outages. 335 Tier-1 systems placed on a 90-day “code safety reset.” VP-level accountability now mandated. ↗
1,200+ executives and 1,190+ companies lost data. The agent ran unauthorized commands during an active code freeze, then fabricated records. ↗
A race condition between AI-driven CI jobs rolled out a non-existent container ID. Textbook governance gap: answer without authority. ↗
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.
Tomosu plugs in read-only across the tools your teams already use: Git, observability, and ticketing. Live in days, measurable in weeks. No rip-and-replace.
At every step, specialized Tomosu agents collaborate: one resolves context, another enforces policy, a third composes risk, and a fourth writes the evidence trail. They operate as a coordinated system, not isolated checks, so governance scales with the speed your AI tools ship code.
Tomosu evaluates code against your organization’s standards as it’s written, so risk is surfaced and corrected long before it has a chance to reach your users.
Every change reaching your main branch arrives with a clear, evidence-backed verdict. The backlog moves at the pace AI generates, not at the pace humans can keep up.
A real-time read on where AI-generated risk is compounding across your codebase, in language the CTO operates and the CFO, CIO, CCO, and board can act on.
When something breaks, the change responsible is identified instantly and the same failure pattern is prevented from re-surfacing. Engineers stop firefighting and return to shipping the roadmap.
Context resolved. Policy aligned. Risk composed. Evidence written. Four checks, one auditable trail. Running the moment a change is opened, not after the incident.
Static analyzers give you pass/fail. APMs give you mean-time-to-detect. Tomosu gives you eight trendable, executive-readable signals, calibrated to your stack.
A single trendable master score that rolls up the seven sub-indices, calibrated per organization to reflect your architecture, maturity, and risk tolerance. The one number the board tracks.
How likely this code is to break, based on structural signals and real production behavior.
The gap between what dev expected and what production delivered. “Test like you fly.”
Severity-weighted adherence to your organization’s coding, security, and observability standards.
Live production health: error rate, latency, and resource anomalies aggregated per service.
Churn and hotspot density. Penalizes files that keep re-triggering the same issues.
Frequency and safety of releases. Rewards small, confident batches.
The interrupt tax on engineering: repeat ticket rate, tier-level MTTR, senior on-call load.
One weighted score. One trendline. Calibrated to your org, not a generic template.
The CTO operates it. The CFO budgets against it. The CIO reports to the board with it. The CCO files it as compliance evidence.
Ship AI-generated code without trading away stability. Cut PR review time, catch bad patterns pre-merge, and reduce repeat incidents.
Only 12% of enterprises have a centralized governance platform for agentic AI. Tomosu is yours, with a defensible audit trail.
PRI is a trendable financial signal. Quantify how much AI-accelerated risk is compounding each quarter, and justify AI spend to the board.
Every governance decision has an evidence trail. Escalations arrive with context, not raw logs. Repeat tickets get deflected upstream.
Policy, identity, approval, and audit designed from day one for AI-generated change, not forced onto workflows built for humans.
Live incidents become guardrails in the IDE. The governance layer gets smarter every week, without your team writing new rules.
PRI is the single trendable number the CTO operates, the CFO budgets against, and the board tracks. No more translation layers.
Works with the Git, observability, and ticketing tools you already run. Read-only by default. Enforcement stays under your control.
Incidents arrive with root-cause context, a likely fix, and an evidence trail. L1 solves what only L3 could before.
Every governance decision is logged with evidence, ready for SOC 2, ISO, or internal AI-use policy reviews without a fire drill.
Baseline in week one. Visible risk ledger by day 30. Full closed loop by day 60. Board-ready trendline by day 90.
Connect read-only to Git, observability, and ticketing. Establish baseline metrics and a service-level risk heatmap.
IDE plugin and advisory merge gate running on first repos. Service risk heatmap live on the Visionboard.
Escalation routing active with context-attached packets. L1/L2 resolving what only L3 could before.
Production incidents feeding back as new guardrails. Repeat clusters dropping. Board-ready PRI trendline.
Conservative targets for mid-size SaaS with 50–300 engineers, 24/7 production workloads, and meaningful AI-assisted PR volume.
For engineering leaders ready to turn AI-accelerated velocity into an auditable, board-defensible risk ledger, before the next Amazon-scale incident becomes yours.
