Regrity
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Product

AI-led enterprise delivery for Salesforce and SAP

Regrity helps teams assess impact, guide implementation, focus targeted regression testing, prepare releases, and recover faster in production using maintained enterprise context across the delivery lifecycle.

Enterprise-aware AI for governed delivery beyond code generation.

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Lifecycle

Governed agentic delivery across the lifecycle

Each stage combines maintained context, delivery intelligence, and governed execution.

Persistent operating layer across all stages: maintained enterprise context, delivery intelligence across the lifecycle, and governed delivery execution.

Plan — AI-led impact analysis

Question

What could change?

Regrity contribution

Ground risk and scope using maintained enterprise context.

Output

Impact map + risk scope

Build — grounded implementation guidance

Question

What should be implemented?

Regrity contribution

Guide implementation with enterprise-aware AI across app constraints.

Output

PR/transport-ready change

Validate — AI-driven targeted regression testing

Question

What must be tested?

Regrity contribution

Prioritize test scope using historical coverage and dependency context.

Output

Targeted regression testing + coverage gaps

Release — governed release readiness

Question

What proves readiness?

Regrity contribution

Apply approvals and readiness gates with evidence generation.

Output

Approvals + evidence pack

Operate — AI-assisted RCA and fix validation

Question

What changed in production?

Regrity contribution

Link release context to incident signals and guide faster recovery in production.

Output

Root cause analysis and fix validation

Capabilities by stage

AI-led execution with stage-specific outputs

Governed AI execution produces artifacts your engineering, QA, and release teams can act on.

Plan

Define impact scope and risk before implementation starts.

Build

Produce PR/transport-ready changes and unit-test coverage.

Validate

Focus targeted regression testing from existing suites and flag gaps.

Release

Route approvals and generate release evidence for sign-off.

Operate

Accelerate root cause analysis and fix validation for faster recovery in production.
Operating layer

How Regrity improves delivery decisions

Regrity keeps current enterprise context and uses it to improve both generation quality and better delivery decisions across the lifecycle.

Enterprise context

Resolve relationships across workflows, permissions, data relationships, integration contracts, prior changes, validation patterns, and production signals.

Lifecycle decision support

Ground lifecycle decisions for impact analysis, implementation guidance, targeted regression testing, release readiness, and faster recovery in production.
Differentiation

More than retrieval. Built for delivery decisions.

Regrity is not a thin fetch-and-enrich layer. It maintains context and turns AI guidance into governed delivery execution.

Maintained context, not one-time lookups.
Relational understanding, not document-only lookup.
Governed delivery execution, not answer-only generation.
Scenarios

Common operating patterns

Feature launch

Input: scoped requirement with dependencies

Output: implementation + regression plan

Proof: validation artifacts for release approval

Legacy replacement

Input: migration target with parity constraints

Output: cutover plan and staged validations

Proof: transport checks and readiness evidence

Brownfield enhancement

Input: change request on active production process

Output: targeted build and test scope

Proof: non-prod validation and sign-off package

Production incident

Input: incident signal and recent release context

Output: RCA and validated hotfix path

Proof: fix verification before rollout

Fast time-to-value

What you can see in ~10 days

Practical outcomes using your current stack.

Impact map + required regression.
Targeted regression testing plan from existing suites.
One validated PR/transport (non-prod) with approval-ready evidence.
One incident replay summary with root cause analysis and fix validation.

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