commonplane
Production truth. One brain. Every team.
Tomás Senart · Seed · March 2026
The Origin

I built it for SRE.
Four other teams pulled it in.
One product worked for all of them.

Support traced bugs to root cause, identified affected customers, and opened fix PRs — without engineering. Product investigated feature behavior in production. Sales and accounts had it calculate burn rates and build customer dashboards from live usage data.

Observed pull across teams
SRE
→
Support
→
Product
→
Sales
→
Accounts
One product. No role-specific features.
The Insight

Production problems are multiplayer.
Today's tools are single-player.

Engineering-first systems
Engineering-led incident workflow
Built for engineering as the control plane for production questions and actions.
Read code + telemetry
Reason about incidents
Act in engineering workflows
▼
Engineering
Other teams may participate, but the buyer, workflow, and trust model still stay anchored in engineering.
vs
commonplane · production-first
Production-native operating surface
Built from the start for permissioned action on the same production system.
Read across systems
Reason over production truth
Act as its own principal
Shared directly by
SRE
Support
Product
Sales
Accounts
Production Truth
Compounding Memory
Grounded in live production data. Every investigation makes it smarter about your company.
Engineering-first systems may involve other teams, but the buyer, workflow, and trust model stay anchored in engineering. Commonplane is built for every team that depends on production — from day one.
Why Now + Why Us

The first wave proved engineering agents.
The next wave is built around production truth.

12 months ago

Models and tool use were too unreliable for serious production workflows.

Today

Engineering-first agents are real. They validate the workflow, but stay anchored in engineering-led incident response.

12 months from now

The winners turn production truth into a shared operating layer for the rest of the company.

What exists today
Engineering-first agents
incident.io · Datadog Bits AI · Rootly · SRE.ai · Resolve.ai
Buyer, workflow, and trust model all anchored in engineering.
Search / knowledge-first platforms
Glean
Unify company knowledge, but not grounded in live production data.
Code-first agents
Cursor · Devin
Built around code authoring, not live production truth.
Why Commonplane wins
Commonplane · Production AI
Starts from live production truth and compounds company memory.
One system reads and acts across SRE, support, product, sales, and accounts.
Reads across systems
Acts as its own principal
Compounds company memory
Same depth. Built for the whole company.
Headcount budget, not tool budget
Sold like talent, not software. Compounds value across teams.
In Practice

Production truth,
turned into action.

Workflows like these run on live Axiom production every day.

› Incident What caused our last P1 incident, which customers were affected, and which accounts are most likely to escalate?
› Support A customer hit a login timeout. Investigate the root cause and open the fix.
› Product Which features are growing fastest by customer segment? Build the dashboard.
Now imagine it's your company.
The End State

One production-native brain
for every software company.

Start with incidents.
Expand to every team that touches production.
Capital Efficiency

The system debugs itself and opens the fix.

It watches its own telemetry, finds failures, and opens the fix. I review. This happens multiple times a day.

Failure
→
Telemetry
→
Root cause
→
Open PR
→
Human review
→
Improved
Why Me

I built the data layer at Axiom.
Then I built Commonplane on top of it.

Founder
Tomás Senart
Principal Engineer at Axiom. Created Vegeta. Built Commonplane inside Axiom and proved it across five teams on live production systems.
Advisor
Seif Lotfy
Co-founder & CTO at Axiom.
Advisor
Neil Jagdish Patel
Co-founder & CEO at Axiom.
Why this founder can win
Already built the infrastructure, proved the product, and has an immediate path to early customers.
The wedge was forged inside Axiom, and the first external demand is already visible around the same production-debugging workflow.
Infrastructure
Built core event data infrastructure at Axiom and scaled it to petabyte workloads.
Proof
Built and used Commonplane inside Axiom before fundraising, on real production systems across multiple teams.
Distribution
Axiom gives us a warm path to technically aligned design partners and early interest around narrow production-debugging workflows.
Setup
Clean spinout, clean IP, and Axiom as the proving ground reduce product and go-to-market risk.
The Round

Raising a $2.5M seed.
To turn internal proof into external proof.

What's already true
  • Support, product, sales, and accounts pulled in organically inside Axiom from an SRE origin
  • The system investigates its own failures and opens PRs with human review
  • Production-grade security architecture with agent-as-principal controls
  • Explicit external interest from a major foundation-model company in a narrow production-debugging deployment
What this round funds
  1. Founder-led conversion of early design-partner interest into first paid pilots
  2. Turn the first paid pilots into measured case studies
  3. Ship enterprise trust and compliance (SOC 2, SSO/SCIM)
What this round should prove
  1. External willingness to pay for narrow production-debugging workflows
  2. Referenceable customers with measured ROI
  3. Cross-team expansion inside external accounts
  4. A repeatable sales motion beyond founder heroics
We win against headcount budget first, then compound through cross-team expansion.
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