A/B Smartly vs Iris.ai
Compare research AI Tools
10% Similar based on 1 shared tag
A
A/B Smartly
Enterprise experimentation platform with a sequential testing engine event based pricing and flexible deployment so product teams run faster trustworthy A B tests share insights broadly and keep governance strong across web mobile and backend.
Pricing Contact sales
Category research
Difficulty Intermediate
Type SaaS
Status Active
Iris.ai
Enterprise retrieval and evaluation platform for secure agentic AI over private corpora with workflows for ingestion testing and governance.
Pricing By quote
Category research
Difficulty Beginner
Type Web App
Status Active
Feature Tags Comparison
Only in A/B Smartly
experimentationab-testingstatisticsserver-sidewarehouse-native
Shared
governance
Only in Iris.ai
retrievalenterpriseevaluationsecurity
Key Features
A/B Smartly
- • Sequential testing engine: stop earlier without inflating error rates so winners ship faster and inconclusive tests end decisively saving time and traffic
- • Warehouse native workflows: route events to your lake or house so analysts reuse metrics segments and joins with lineage and reproducibility across teams
- • SDKs across stacks: integrate once into web mobile and backend so feature flags exposures and metrics remain consistent across platforms and services
- • Source control friendly: treat experiments as code with reviewable configs CI checks and templates that prevent errors before traffic hits production
- • Collaboration and notes: attach hypotheses screenshots and decisions to each test so outcomes are searchable and shareable in postmortems and planning
- • Event based pricing: avoid per seat or per test limits grow programs with predictable unit economics and fewer internal license battles
Iris.ai
- • Governed Ingestion: Connect wikis drives and repos then normalize content with metadata access rules and retention policies for compliance
- • Evaluation Workflows: Run automatic metrics and human rubrics to measure accuracy hallucination rate and coverage before launch
- • Guardrails and Policies: Define prompts filters and safety limits that block sensitive data flow and unsafe responses in production
- • Observability and Drift: Track quality usage and model costs then alert owners when performance moves outside accepted ranges
- • Integrations: Use existing vector stores model providers and identity controls so deployments align with current architecture
- • Red Teaming: Exercise prompts tools and environments to uncover jailbreaks and leakage risks before go live
Use Cases
A/B Smartly
- → Feature rollout gates: validate impact behind flags then graduate safely once primary metrics clear with acceptable side effects across segments
- → Checkout funnel fixes: trial copy layout and sequencing while monitoring revenue and refunds to avoid profitable but risky changes
- → Search relevance tuning: compare ranking tweaks with guardrails for speed stability and engagement beyond a single click proxy
- → Performance tradeoffs: measure latency shifts alongside conversion so teams understand when speed investments or regressions are acceptable
- → Paywall and pricing tests: explore presentation and eligibility while keeping fairness guardrails and refund tracking visible to finance
- → Notification systems: iterate cadence and targeting while measuring retention spam complaints and app store optics over weeks
Iris.ai
- → Stand up secure knowledge assistants for employees that search approved sources with clear citations
- → Reduce support handle time by routing assistants to articles with evaluation backed accuracy and policy bounds
- → Enable research teams to explore large archives and synthesize findings with traceable sources for compliance
- → Run pilots that compare prompts models and retrieval settings to pick the highest quality approach
- → Prepare audit evidence with documented controls and results to satisfy internal and external requirements
- → Connect identity and permissions so assistants respect document level access across departments
Perfect For
A/B Smartly
growth leaders, data scientists, product managers, experimentation engineers, analysts and SRE partners at companies with strong telemetry security and compliance expectations
Iris.ai
enterprise knowledge leaders compliance teams information security and platform engineers who need measurable safe retrieval over private data
Capabilities
A/B Smartly
Sequential Statistics Professional
Warehouse and SDKs Professional
Policy and Audit Professional
Knowledge Sharing Intermediate
Iris.ai
Governed sources Professional
Quality and safety Professional
Policies and guardrails Intermediate
Drift and reporting Intermediate
Need more details? Visit the full tool pages: