Design-partner programme now open
Know what AI coding really costs you, and whether it's safe to merge.
Agent vs human delivery and failure rates, the true cost of every feature, and release dates from your team's real pace. Measured from GitHub and your AI usage. Nobody updates a ticket.
- EU-hosted
- Can run in your own AWS account
- Your code never trains a model
- No production access needed
How it works
A couple of integrations, not a dozen. Zeynlo reads the raw events from the systems your teams already use and turns them into numbers you can open, check and act on. It is not another dashboard stacked on top of your existing dashboards.
- GitHubrequired
- AI coding assistants & agentsrecommended
- Your org, goals & plansone CSV import
- Figmaoptional
- Runtime telemetry (OpenTelemetry)optional
Links every commit, review, token and deploy to the task, epic and goal it serves, then prices it.
- Agent vs human DORA metrics
- Trust score on every pull request
- True cost per feature
- Forecasts from real pace
- Goal lineage & strategic drift
- SLOs, incidents & on-call
Two connections to start
GitHub, plus your AI usage for AI cost. Figma and runtime telemetry add design readiness and reliability when you want them.
Raw events, not other dashboards
Zeynlo works from commits, reviews, deploys and tokens themselves, so there is no stack of reporting tools to feed and reconcile.
No ticket hygiene
Progress comes from merged code, so nobody relabels tickets or writes status updates to make the numbers right.
What it does today
True feature cost
Hours × each person's rate, AI tokens × the model's price, and the cloud cost of the services involved, attributed to the epics the work actually went into. Every total opens to the people and agents behind it and reconciles to the cent.
Demo org: cost per merged pull request $2,211, people plus AI.
Guardrails for agent code
Every pull request is scanned for secrets, then reviewed twice by AI: once for correctness and risk, once against your engineering policies and the epic's acceptance criteria. The result is a trust score and a plain list of blockers. Agent-written code never merges without a human.
Demo org: agent changes fail in production 10.7% of the time, human changes 6.6%.
From commit to company goal
Tasks close when their pull requests merge, and work rolls up from epic to strategy to key result. AI suggests which key result a strategy serves; a person confirms it. You see how much engineering spend serves no key result at all.
Demo org: 51% of engineering cost served no key result last month.
Demo figures come from the demo organisation (127 engineers, 30 days). The sandbox audit measures yours.
Built to be trusted
EU-first security
EU data residency, encryption in transit and at rest, and secrets and personal data stripped before any AI review.
Numbers you can check
Every figure opens to the records behind it and the calculation with that period's real counts.
Next to your tools
Start alongside what you run today. Nothing to rip out, no tickets to relabel, no status updates to write.
Built for every role
Executives, product, leads and engineers each see what they need. Personal numbers stay private to the engineer and their lead.
Answers for your security review
Your source code and telemetry stay under EU data residency.
- Where it runs
- In the EU by default, or inside your own AWS account.
- AI models
- Reviews run on a model service in the same EU environment. Your code and telemetry are never used to train a model.
- Before any AI sees code
- Secrets (keys, tokens, passwords) are detected and the change is stopped; e-mail addresses and phone numbers are removed.
- Data handling
- Encrypted in transit and at rest. Raw telemetry is deleted automatically after 7 days.
- Access
- Administrator approval before anyone sees data. Personal numbers are visible only to the engineer and their lead.
- Paperwork
- Mutual NDA and a data processing agreement (DPA) on request.
Prefer to talk first? Book a demo
30 minutes with the founder: a walkthrough of the product on demo data, and your questions on setup, security and the sandbox audit. Pick a date and time that suits you.
The calendar is provided by Calendly and loads its own cookies once you open it.
Apply for the zero-risk sandbox audit
No production access and no contract. In 5 days you see your own engineering bottlenecks, from one repository's history.
- Day 1A 30-minute kickoff. You share read-only access to one repository's pull-request history, or an anonymised export.
- Day 2–3We import it into a private sandbox. Names and e-mail addresses are anonymised; production is never touched.
- Day 4We analyse review wait, change failure by author type, scope churn and forecast accuracy.
- Day 5A 30-minute readout: your bottleneck signature and three concrete recommendations. Then we delete the data.
What you get
- Agent vs human delivery and change failure rate
- Where reviews and handoffs slow your team down
- How much work changed after it started
- Three recommendations, with the numbers behind them