Pavo for Enterprise
Pavo turns production goalsinto measured improvements.
Our applied science platform and embedded scientists find, test, and help ship improvements across your existing production systems. Start with one system, one metric, and agreed success criteria.
Across these surfaces
6How it works
Every improvement starts with understanding your system.
You approve the plan and the production test; Pavo runs the work between checkpoints.
The operating model
An 8-week engagement around one production metric.
A Pavo scientist works with your team to define success, evaluate candidates, and run an approved production experiment.
What happens
What you keep
Evidence returns to W1
A Pavo scientist works inside your team at each decision—the metric, the evaluation, and the production call.
- Your team’s role
- Provide scoped access, a metric owner, and reviewers at agreed checkpoints.
- Handover
- You retain the knowledge, evaluations, and playbook for running the next loop.
Case studies
All case studies
How Seekho replaced broadcast blasts with category-level personalization
Pavo mapped the notification system, fixed the signal driving it, and ran a phased program from four interest groups to sixteen category segments.
Read the story+43%
Notification click-through rate
+94%
Second-video initiation
How Dashverse lifted Shortfree Autoplay activation with an offline-eval loop
Pavo built an offline-eval engine to screen ~50 candidate policies without live traffic, then shipped the one variant that moved activation in production.
Read the story+3.1pp
Autoplay activation, live in production
~50
Policies screened offline before one reached users
Compounding advantage
Every experiment strengthens the next.
Pavo retains the system context, evaluation, and findings, so the next investigation builds on work already done.
| Metric | A/B test loop 1 Measure | A/B test loop 2 Segment | A/B test loop 3 Specialize | A/B test loop 4 Route |
|---|---|---|---|---|
| Scientific cycle time | ~5 weeks | ~3 weeks | 8–9 days | ~5 days |
| Knowledge reused | — | ~25% | ~55% | ~75% |
| Product understanding gained | Evaluation was biased | Personalization helps some users and hurts others | Different user states need different policies | Route each user to the policy that works best |
| Production approach | Global policies | Segment-aware policies | Segment-specific interventions | Production policy router |
Deployment and onboarding
Pavo works on top of your current stack.
Choose the deployment that fits your security posture. Then connect, verify, and hand Pavo the first problem statement without rebuilding your stack.
Runs inside your cloud account. Your IAM, network, and data policies stay in force.
Connect
- Warehouse
- Codebase
- Experimentation platforms
- Docs
Read access
Your cloud boundary
Your environment
Systems + data
Your IAM · network · data policies
Customer controlled
Pavo runtime
Your account · your region
Connect only
what Pavo needsRead access only. No migration, no new pipelines, no rearchitecting.
Less than an hour
from your engineersYou grant credentials through existing controls. Pavo does the rest.
Production stays behind
your approval gateEvery proposed change is tested in an isolated environment first.
Start with one metric
to moveOne surface, one owner, clear guardrails. Prove it before you expand.
Security and trust
The security posture your compliance team wants
ISO 27001
Certified
SOC 2 Type II
Compliant
Encryption
Encrypted in transit and at rest, with managed production keys.
Access control
Least-privilege access, enforced with MFA and reviewed quarterly.
Data control
Tenant-segmented, with retention and customer-controlled deletion.
Monitoring and response
Continuously monitored, centrally logged, and ready to respond.
Tested and patched
Independently pen-tested, scanned, and kept current against threats.
Resilient by design
Multi-AZ with backups and a tested continuity and recovery plan.
Full reports, policies, and the complete control list are available on request through the Trust Center.
The Enterprise trial
One system. One metric. A scoped trial.
What the trial contains
01Scope
- Problem-scoping workshop
- Agreed metric, success criteria, and guardrails
02Set up
- Platform and system-knowledge setup
- Integration plan for your existing stack
03Evaluate
- Initial candidate approaches
- Offline evaluation
04Decide
- Production experiment recommendation
- Next-step plan based on the evidence