Faster is easy. Better is harder.
A system can be fully output-optimised while simultaneously deteriorating on outcomes. No prior framework detects this. KamiraFlow is the one that does.
DORA was designed for human engineering teams. It measures deployment frequency, lead time, failure rate, and recovery speed. All output metrics. All designed before AI agents became part of how software is built and maintained.
In engineering systems where AI participates, high output can coexist with deteriorating outcomes. Your agents are completing tasks. The question is whether those tasks were done correctly — and whether you have the measurement infrastructure to know the difference.
KamiraFlow connects to your GitHub repository. It computes your DORA baseline. Then it shows you what DORA cannot: done rate, intervention type distribution, AI code groundability, and Cognitive Debt Index — in the same interface.
KamiraFlow is live in your GitHub environment in under five minutes. No agents to deploy. No instrumentation required.
OAuth connection to your repositories. KamiraFlow ingests commits, pull requests, releases, deployment events, and AI-assisted development signals.
Your DORA baseline appears immediately. Alongside it: done rate, intervention distribution, AI code groundability, and Cognitive Debt Index — side by side, in the same interface.
Monitor the gap between DORA and outcome metrics over time. When they diverge, you have a signal. When they align, you have an answer: yes, you are better.
Each addresses a specific failure mode that DORA-class measurement cannot. Each appears in MeaningStack's pending patent on Groundability. Each computed from your GitHub events.
How deep is AI operating in your decision space, relative to where human cognitive authority should reside? Detects whether AI is committing decisions before humans can review them.
When humans review AI output, are they catching what needs catching, and accepting what should be accepted? A score below 0.5 indicates systematic over- or under-trust.
The interval between when something breaks and when any human notices. The 13-day blind spot DORA cannot see.
Five categories of human correction — cosmetic, semantic, scope, recovery, escalation. Same intervention rate, categorically different problems.
The rate at which the structure of who-touches-what is changing. Excessive velocity is a leading indicator of cognitive debt accumulation.
Herfindahl-Hirschman concentration of consequential review work. Detects when a few minds are silently consolidating risk.
Whether the codebase is structurally and semantically prepared for AI agents to operate in it safely — a composite of test coverage, documentation, structural cohesion, churn stability, and PR-size health.
Every standard DORA metric, plus the five-domain AI Impact radar — velocity, codebase shape, quality signals, team dynamics, review health — alongside the seven outcome trajectory signals above.
KamiraFlow's seven signals are not arbitrary engineering metrics. They are the operationalisation of Groundability — one of three patents pending in the MeaningStack platform. The signals measure how safely agents can operate in your codebase. The patent describes why that is the correct unit of analysis.
Most engineering metrics tools collapse three different cognitive jobs into one dashboard, leaving you to separate them mentally. KamiraFlow respects how engineering leaders actually think — by measuring through three distinct temporal lenses, each with its own UI and its own purpose.
Reconstruct the story. Where were we, how did we get here, what changed.
Time-series charts, intervention distributions over time, trend lines that show the trajectory of every signal across rolling measurement windows.
→ UI · Charts · Trends · Distributions
Take the system's vital signs at this moment — contextualised against thresholds and recent trend.
Current-state values for every signal in the framework, with status badges, trend direction, and threshold context. The diagnostic moment.
→ UI · Card values · Status badges · Live
Decide action — grounded in the intersection of current Assessment and recent History.
Recommendations not generated from current state alone, but from the trajectory of the current state. Goals you can pin and track. Actions ranked by predicted impact.
→ UI · Recommendations · Goals · Action plan
The three lenses don't compete; they layer. History informs Assessment. Assessment plus History informs Guidance. This is the architectural principle behind every screen in KamiraFlow.
MeaningStack provides infrastructure for operational verification, performance, and coordination under machine participation. KamiraFlow is the performance and measurement layer — live and deployable today. The platform extends further into runtime governance, operational coordination, and value transfer for enterprise environments. Read the platform overview →
Start free, no credit card. Move to a paid tier when you want full access, locked pricing, and historical data beyond 30 days. The first ten annual customers lock their rate for 24 months.
Permanent free tier. No credit card. No call required. Designed so anyone running a real engineering team can see what KamiraFlow does on their own data.
€870 upfront for three months. Continues month-to-month at €290 after that, or stop at any time with no further charge. Direct contact with the people building KamiraFlow — included, not extra.
For teams that want full access at a predictable annual rate. The first ten annual customers lock pricing for 24 months and shape what gets built next.
The first ten annual customers lock pricing for 24 months. Pricing changes for new customers after that. The free tier remains permanently free.
Those tools measure output — deployment frequency, lead time, cycle time. KamiraFlow measures outcome trajectory — whether your engineering system is improving on dimensions that DORA-class tools cannot see. The two are complementary. KamiraFlow runs alongside, not instead.
Groundability is the legibility of your codebase to agentic systems — how safely AI agents can operate in it. Most AI tooling tries to make agents better at understanding code. Groundability measures something different: whether the code is structured for agents to operate in it safely. A property of the substrate, not of the agent. KamiraFlow makes it measurable.
No. KamiraFlow connects directly to GitHub via OAuth and computes both DORA and outcome-layer metrics from your repository events. Free tier covers one repository and 30 days of historical data. No agent deployment required.
KamiraFlow reads repository events and metadata — commits, pull requests, releases, AI-assisted development signals — not your source code in full. Hosted in the EU. Founding partners can opt into EU-only mode for full data sovereignty.
KamiraFlow is the measurement layer. Steward Agent is the governance layer — runtime verification of agent rationale, in production with first design partner. Governed Escrow is the value-transfer layer — patent-pending research foundation for transactional governance. One platform. Three patents. KamiraFlow is the first deployable product.
Then KamiraFlow may show you a divergence you cannot currently see. A system can be fully output-optimised while simultaneously deteriorating on outcomes. The free tier lets you find out on your own data, with no commitment.
KamiraFlow is live in your GitHub environment in under five minutes. Permanent free tier. No credit card. No call required.
Connect with GitHub — free