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AI-Native · Agentic quality · Ratl · Live in production at Reliance Retail

Ratl.
The agentic quality OS for Reliance Retail.

Ratl is Fynd's AI-first reliability layer across development, testing, release, and production. AI agents autonomously test APIs, web, mobile, and performance — end to end — and emit a confidence score for every release before it ships.

Live in production at Reliance Retail, where 100+ releases/month and millions of daily users outpaced manual QA models.

Productivity gain
3–5×
Same team delivering 4–5× the output
Release cycles
70–80%
Faster vs. pre-Ratl baseline
Automation coverage
0 → 100%
Across 33+ backend services and 85+ storefronts
01 · What Ratl is for Reliance

Problem · Solution · Impact.

Problem

Manual QA could not keep up.

  • · 100+ releases/month across Reliance Retail brands
  • · 35+ storefronts at start, growing to 85+ in production
  • · Millions of daily users · zero margin for silent regressions
  • · Manual & assisted QA models priced engineering time out of reach
Solution

AI-driven, agentic quality systems embedded into the SDLC.

  • · Natural-language → test generation · auto-mapped via OpenAPI + changelogs
  • · Failure clustering and root-cause analysis on every run
  • · AI-generated release confidence score · risk class · go/no-go
  • · Synthetic monitoring continuously across every storefront
Impact for Reliance Retail

Reactive testing replaced by autonomous release systems.

  • · 70–80% faster release cycles
  • · 40–50% reduction in UI defects · ~90% reduction in broken links
  • · 35–45% reduction in regression issues
  • · 60–70% reduction in manual operational checks
02 · What's live · seven engines

Seven engines. One reliability layer.

Each engine ships as a live surface in the Ratl console. Used together as one stack in production at Reliance Retail.

Live RR estate · headlineLive · AJIO × JCP
1,300
test cases running on the AJIO × JCP estate · Boltic + Ratl AI-first testing pipeline
01Live

Release Intelligence

Aggregates signals across API, web, mobile, load, security, and compliance. Every release ships with a confidence score, a risk class (Critical / Medium / Low), and an automated go/no-go recommendation.

02Live

Autonomous Testing

Natural-language to test generation. Auto-mapped via OpenAPI and changelogs. 10,000+ validations/day across services · 1,000+ scenarios per release · ~80% reduction in manual test creation.

03Live

Synthetic Monitoring

AI agents continuously capture screenshots and detect visual failures across every storefront. Catches blank pages, broken UI flows, and silent production failures before customers notice.

04Live

Mobile AI Automation

Agentic mobile testing in natural language across Android and iOS · real-device cloud infrastructure · crash analysis with auto-fix and PR generation. Continuous validation of P0 user journeys, ANR, frame drops, CPU.

05Live

Compliance & Security

80,000+ URLs scanned for compliance · autonomous penetration testing · continuous audit-ready validation. Replaces weeks of manual audits with real-time validation; reduces dependency on external vendors.

06Live

Automation Utilities

PDP compliance validation · SEO/GEO checks · broken-link detection · image-quality checks · delivery and pincode validation. 60–70% reduction in manual operations.

07Live

Catalog Intelligence

End-to-end catalog observability — source systems → pipelines → consumer apps. Detects out-of-stock inconsistencies, add-to-cart failures, unserviceable listings, sync failures, and price/catalog mismatches in real time. Evolving into the core intelligence layer for Jiomart OS — see §05 below.

03 · Architecture · the agentic SDLC layer

Four layers. One reliability layer across the SDLC.

Signals flow up from the systems under test; intelligence flows back into developer, release, and oncall surfaces. Every layer is live.

Layer 01 · SDLC surfaces · where Ratl shows up
Developer · IDE + PR
Release Manager · console
Oncall · alerts + runbooks
Storefront Ops · dashboards
▴
Layer 02 · Release Intelligence · the decision layer
Confidence score
Risk classification
Failure clustering
Go / No-go
▴
Layer 03 · Agentic engines · what executes the work
Test Generation
NL → tests · OpenAPI mapping
Autonomous Execution
10K+ validations/day
Visual Diffing
Synthetic monitoring agents
RCA Agent
Failure clustering · root cause
Crash → Fix → PR
Mobile auto-remediation
Pen-Test Agent
Autonomous security probing
▴
Layer 04 · Signals · what Ratl tests
API· Web· Mobile (Android · iOS)· Load· Security· Compliance· Catalog pipelines
33+ backend services · 85+ storefronts · web, mobile, SDKs, admin · 80,000+ URLs in compliance scope
04 · Deep dive · the live console

Three surfaces from the running platform.

Screenshots from the live Ratl console used by Reliance Retail brand teams. Click any image to enlarge.

View at ratl.ai →

Release Intelligence · confidence score, risk classification, and go/no-go recommendation per release
01 · Release Intelligence

Per-release confidence score, risk class, and go/no-go view. Aggregates API, web, mobile, load, security, and compliance signals into one decision surface.

Synthetic Monitoring · continuous storefront screenshot capture and visual-failure detection
02 · Synthetic Monitoring

Continuous AI capture and visual-diff across 85+ storefronts. Catches blank pages, broken UI flows, and silent production failures before customers do.

Compliance Monitoring · 80,000+ URLs scanned for compliance with audit-ready dashboards
03 · Compliance Monitoring

80,000+ URLs scanned continuously. Audit-ready dashboards. Replaces multi-week manual audit cycles with real-time validation.

05 · Catalog Intelligence · the Jiomart OS foundation

Catalog Intelligence.

Live · Catalog observability Building · Jiomart OS layer

An AI-driven platform that tracks catalog data end-to-end — from source systems through pipelines to consumer apps — and maps every breakpoint directly to customer impact.

Catalog Intelligence · site operations view with cross-pipeline observability
Catalog Intelligence · site ops

Cross-pipeline view of catalog health. Real-time visibility across the catalog stack — source through storefront.

Signals detected
  • · Out-of-stock inconsistencies
  • · Add-to-cart failures
  • · Unserviceable or stale listings
  • · Sync failures across systems
  • · Price / catalog mismatches
Business impact
  • · Improved conversion rates
  • · Reduced revenue loss from catalog issues
  • · Faster issue resolution
  • · Real-time visibility across the catalog pipeline
Forward beat · evolving into the Jiomart OS intelligence layer

Catalog Intelligence is being extended into the core intelligence layer for Jiomart OS — unified data observability, cross-system orchestration, and AI-driven decisioning. Building.

06 · Outcomes · the running scorecard

What changed once Ratl was running.

Numbers below are pre-Ratl baseline → post-Ratl steady-state across the Reliance Retail ecosystem.

AreaOutcomeEvidence
Release velocity 70–80% faster release cycles 100+ releases/month sustained
Engineering productivity 3–5× output from the same team ~80% reduction in manual test creation
Automation coverage 0 → 100% across in-scope services + storefronts 33+ services · 85+ storefronts
UI defects 40–50% reduction Synthetic monitoring across all storefronts
Broken links ~90% reduction Automation utilities · continuous scan
Regression issues 35–45% reduction 1,000+ scenarios per release
Compliance scope Real-time validation replacing weeks of manual audit 80,000+ URLs scanned continuously
Operational checks 60–70% reduction in manual operations PDP · SEO/GEO · image · pincode utilities