ScalePie REL.ASSURE / v2.6
SYSTEM LIVE
AI-NATIVE RELEASE ASSURANCE · 2026

Know if your software
will fail—before
it reaches production.

ScalePie is an AI-native Release Assurance platform that predicts software risks, designs optimal QA strategies, and ensures production-ready releases across automotive, aviation, medical, and embedded systems.

Get Release Risk Assessment
0%
Production risk reduction
0%
Regression effort cut
0%
Avg. prediction confidence
RIE · 014 Release Risk Engine
ANALYZING
RISK SCORE
78
78 42
CONFIDENCE 86%
SYSTEMAutomotive · ADAS
BUILDv18.4.0-rc2
SCAN 142 / 2,300 selected
IMPACT · MAP High-Risk Modules
live
  • Brake Control Unit68%
  • Sensor Fusion (ADAS)74%
  • CAN Gateway41%
  • Telemetry Service18%
DEP · GRAPH Dependency Propagation
42 nodes
02 SIGNAL · LOSS

Most software failures are predictable—yet still happen.

Across enterprise releases, the same patterns repeat. Risk hides in dependencies, regression scope balloons, and teams ship hoping the test matrix was enough. It usually wasn't.

P-01

Hidden Risks

Dependency chains that engineers can't see by eye. Failure conditions buried in version-to-version drift.

P-02

Missed Regression

Test scopes chosen by intuition. The 12 cases that mattered weren't in the run. The 2,288 that didn't were.

P-03

Inefficient QA

Full regression cycles burning days. Coverage rising; confidence not. Spend doesn't track value.

P-04

Production Failures

Field incidents. Recalls. Hotfixes at 2am. The cost of a bug after release is 100× the cost before.

03 SYSTEM · INTENT

AI-Native Release Assurance

Instead of reacting to bugs → prevent them before release.

INPUT
Your System History
commits · tests · incidents · CI signals
CORE
ScalePie Intelligence
prediction + execution
OUTPUT
Release Decision
Go / No-Go · scoped · confident
04 PLATFORM · CORE

From Uncertainty to Release Confidence

ENGINE · 01

Release Intelligence Engine RIE

A decision system for releases — not a feature. Reduce production failures before they happen, using your own system history.

Before every release, ScalePie tells you
what will break · where it will break · why it will break.

  • ◉Risk Score · 0–100 with confidence %
  • ◉Failure Prediction · module + type + probability
  • ◉Failure Hotspot Map
  • ◉Impact Analysis
  • ◉Failure Memory System
RIE.OUTPUT · AUTOMOTIVE
build v18.4.0-rc2
RISK SCORE
82 CONFIDENCE 86%
HIGH-RISK MODULES
  • Brake Control Unit (BCU)
  • Sensor Fusion Module (ADAS)
PREDICTED FAILURES
  • Delayed braking response · sensor latency pattern
    68%
  • Object misclassification · ADAS perception pipeline
    74%
HISTORICAL REFERENCE
Seen in v7, v12, v16 — similar sensor timing & validation issues. FAILURE MEMORY MATCH
SUGGESTED TEST SCOPE
  • Brake response timing validation HIL
  • Sensor fusion edge-case scenarios SIM
  • Regression: 142 selected from 2,300 total −93.8%
ENGINE · 02

Execution Intelligence Engine EXE

Not manual QA. An AI-driven execution system that tells QA exactly what to do — and cuts regression cycles from days to hours.

Don't run 10,000 tests.
Run the right tests.

  • ◉Smart Regression Optimizer · 40–70% effort cut
  • ◉Test Case Generation
  • ◉Automation Script Generation
  • ◉Intelligent Bug Reporting
  • ◉QA Strategy & Coverage Intelligence
EXE.OPTIMIZER · LIVE
regression scope
FULL REGRESSION
2,300
SCALEPIE SCOPE
142
−93.8%
tests skipped, safely
7h
cycle, was 4 days
100%
predicted-failure coverage
selected · skipped
RIEtells what will fail
+
EXEensures it's tested & fixed
=
Release Assurancepredict · execute · assure
Lower release risk
Lower QA cost
Higher release confidence
05 SEQUENCE · OPERATION

How it works

Four steps. Two weeks. Zero integration to start.

  1. 01

    Ingest

    Connect commits, test history, incidents, and CI signals — or hand us a historical export. No production access required.

  2. 02

    Model

    RIE builds a failure-memory model from your system's actual history. Patterns, timings, validation drift, dependency chains.

  3. 03

    Predict

    Score the upcoming release. Surface what will break, where, why — with confidence. Suggest scoped tests.

  4. 04

    Assure

    Execute targeted tests through the Execution Engine. Validate predictions against real outcomes. Decide Go / No-Go with evidence.

06 ENGAGEMENT · MODELS

Three ways to start

PILOT

Release Risk Audit

2 weeks

No integration required. We run on your historical data and your upcoming release — and show you the predictions before ship day.

  • Predictions before your release
  • Validate accuracy against real outcomes
  • Risk + scope + Go/No-Go report
CORE

Release Assurance

Quarterly · ongoing

Continuous prediction + execution embedded in your release cycle. RIE + Execution Engine + AI-augmented QA experts.

  • Every release, scored and scoped
  • Targeted regression — not full cycles
  • Coverage intelligence dashboards
PLATFORM

Platform Licensing

Annual

Run ScalePie inside your environment. SSO, on-prem connectors, dedicated failure-memory model, and your own RIE instance.

  • Self-hosted or VPC
  • API + SDK access
  • Enterprise support
07 OUTPUT · DECISION

Decision-ready insights

For engineering. For leadership. One artifact, every release.

RELEASE ASSURANCE REPORT · build v18.4.0-rc2
SIGNED · ScalePie RIE
RISK SCORE
42 / 100
CONFIDENCE
86%
HIGH-RISK MODULES
2 flagged
PREDICTED FAILURES
5 scenarios
SUGGESTED SCOPE
142/2,300
DECISION
GO · conditional on scoped run
08 DEPLOY · DOMAINS

Built for systems where failure is not an option

🚗

Automotive

ADAS · BCU · powertrain

⚙️

Embedded

RTOS · firmware · HIL

🩺

Medical

device firmware · IEC 62304

✈️

Aviation

avionics · DO-178C

☁️

SaaS

complex distributed systems

💳

FinTech

regulated · transactional

09 HUMAN · LAYER

AI-augmented QA experts

Engineers who use ScalePie to focus where risk actually lives. The model surfaces. People decide.

01

Risk-first focus

Time goes to the modules the model flagged — not the ones tradition flagged.

02

Evidence over opinion

Every recommendation is grounded in your system's failure memory, not a generic playbook.

03

Human judgment

Engineers validate, override, and feed corrections back into the model. The system learns your system.

10 DIFFERENCE · TABLE

Traditional QA vs ScalePie

Traditional QA
ScalePie
Reactive after failure
Predict before release
Full regression cycles
Targeted intelligent testing
Limited visibility
System-wide insights
Fix after production
Prevent before release

Don't release blindly.

Run a 2-week Release Risk Audit on your next build. See predictions before ship day.

Email info@scalepie.com
12 CAREERS · OPEN

We are hiring

QA professionals to work on AI-native quality engineering systems. Move beyond traditional testing.

Send your resume

Email info@scalepie.com and include:

  • Availability
  • Preferred location
  • Expected CTC

Work in an AI-first environment where intelligent systems and engineering expertise come together to predict and prevent software failures. If you want to move beyond traditional testing and work on next-generation quality systems, we'd like to hear from you.

Apply via Email