Challenge · 01
Tenant boundaries
application data and workers must preserve organization scope.
<- AI Systems Dossier
ENTRY NO. 05Revenue Operations / R&DUnder developmentReact / Express / Prisma / rules + ML scaffoldingREVOPS / UNDER DEVELOPMENT
Revon
Identity · Lead triage and decisioning prototype
ML-ready lead scoring workspace for enrichment, routing, review, and RevOps automation.
React / Express / Prisma / rules + ML scaffolding
Evidence boundary
What is demonstrated, and where the claim stops.
Repository factConfiguration
What this proves
- /A tenant-aware lead workspace and scoring workflow are implemented.
- /Config-driven rule scoring can produce inspectable score reasons.
- /Training and inference paths exist for three baseline model tasks.
What this does not prove
- /A trained or calibrated production scoring model.
- /Measured prioritisation accuracy or inference latency.
- /Delivered revenue uplift, time savings, or production business impact.
Data boundary
The public demo path includes mock/generated data. Checked-in ML artifact, export, and report directories do not contain trained result artifacts.
Evidence type
repository fact / configuration
No trained model artifact, calibration report, or production outcome is claimed.
Known limitations
- /No checked-in trained artifact or evaluation report.
- /The repository mixes mock discovery with service-dependent product paths.
- /The root README needs a canonical product quickstart.
What production would require
- /Versioned training data, artifacts, held-out reports, and calibration.
- /Deployed workers, database, monitoring, and integration health.
- /Governed tenant onboarding and audited CRM/data-provider integrations.
Problem
A lead workspace needs explainable decisioning and an evidence path from rules to evaluated ML.
System Flow
5 stages · click to inspect
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Click any stage to inspect
Signature Module · Lead triage and decisioning prototype
Inspect current decisioning
Follow a tenant-scoped lead through enrichment hooks, rule scoring, review, workers, and the ML extension boundary.
Revon · current prototype boundaryinspectable
INPUTtenant-scoped lead record
ENRICHcompany and person provider hooks
SCOREconfig-driven rules + inspectable reasons
WORKERSenrichment · scoring · reporting · integration
MLtraining and inference scaffolding
BOUNDno trained result artifact claimed
Demonstrated Change
Demo scopeBefore
Lead data and decisions spread across disconnected steps.
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Demonstrated build
One prototype workspace with rule scoring, workers, reporting, and ML extension points.
The prototype demonstrates the application and worker architecture needed to test that path.
No trained model artifact, calibration report, or production outcome is claimed.
Proof Signals
Classified evidence3
ML training paths
Repository fact
4
worker roles
Repository fact
1
ML feature contract
Repository fact
Every signal is classified as a repository fact, recorded test, configuration, or disclosed data boundary.
Why This Is Hard
3 engineering challengesChallenge · 02
Explainability
current rule signals need inspectable reasons.
Challenge · 03
ML evidence
training code is not a model result until artifacts and reports exist.
Engineering Depth
3 topics · click to expandThe checked-in scoring engine is config-driven and computes explainable industry, size, seniority, engagement, completeness, and source-quality signals.
Key numbers
Built withReactExpressPrismaPostgreSQLTypeScriptPython