Empowering Fieldstone AE executives with disciplined capture, collaborative intelligence, and audit-ready handoffs. Built for 50 users and governed by the F1–F6 MVP scope.
This MVP is locked to six core features. Every section of the platform reinforces disciplined capture → collaborate → organize → execute workflows with audit-ready AI assistance.
All content aligns with the approved MVP consensus (F1–F6 only).
Confidence badges distinguish AI output from human updates.
Interface honors F-pattern scanning and mobile quick actions.
Purpose
FAEVision is the strategic intelligence nerve center for Fieldstone AE. It captures operational signals, builds executive consensus, and drives solutions to formal handoff—without adding unauthorized scope or technical debt.
Capture-to-solution cycle reduces executive triage time by 40%.
Strategic tagging turns raw signals into trend intelligence the moment they enter the system.
AI highlights duplicate or related inputs, so executives focus on the work that matters most.
Every workflow step includes audit logging and role-based controls.
Executive overrides are tracked with timestamps, actor IDs, and AI provenance metadata.
Security, performance, and accessibility standards stay aligned with MVP quality gates.
Features
Six executive-vetted features, nothing more. Each capability aligns with MVP consensus and maintains AI transparency, executive override, and performance targets under 2 seconds.
Workflow
Click through each step to see how inputs move from capture to FRD handoff. Every stage maintains AI transparency, human override, and compliance with MVP quality gates.
Contributors and executives record problems, opportunities, and general inputs with AI assistance that stays transparent and overrideable.
Capture & Tag
Signals recorded, AI tags + duplicates surfaced.
Organize & Align
Hotspots prioritized, requirements approved.
Execute & Handoff
Solutions launched, FRD generated, telemetry tracked.
Auto-tagging, duplicate detection, and AI confidence badges keep data trustworthy.
Technology
Our AI-assisted pipeline combines rule-first domain logic with explainable machine intelligence. Every stage is observable, recoverable, and aligned with the F1–F6 scope for 50 Fieldstone executives.
EnhancedTaggingEngine v1.0.0 blends curated A&E term banks with GPT-4 outputs validated by Zod schemas.
Inputs run through domain heuristics before calling generateEnhancedTags, ensuring context-rich prompts and fallback when confidence < 0.7.
AI responses capture root cause, issue hierarchy, entities, and business impact; metadata records processing time, model version, and override flags.
Domain classification leverages rule matches for confidence distribution; overrides queue into future training metadata.
ExecutiveHybridClusteringEngine v3.0.0 organizes signals into 4–6 action-ready hotspots.
Stage 1 applies root-cause and department rules to guarantee business relevance.
Stage 2 performs semantic refinement using confidence-weighted vectors across 60 domain + 25 semantic + 4 executive features produced by the MultiDimensionalFeatureEngine.
Stage 3 optimizes for executive consumption, producing summaries, action templates, and quality metrics with full processing telemetry.
Similarity services prevent noise and surface related intelligence at capture time.
Duplicate checks blend cosine similarity of embeddings with rule-based thresholds for department and issue-type alignment.
Similarity suggestions expose acceptance ratios, confidence, and AI provenance so executives can trust recommendations.
Feature-engineering caches similarity calculations in memory for repeat queries, keeping response times under 500ms.
Designed for clear executive accountability and rapid recovery.
Batch jobs persist to tagging_jobs and tagging_job_logs tables with processing, complete, or error states.
Operations console surfaces AI job telemetry, run durations, success/error counts, and retry options.
Every AI call has circuit breakers, human fallback workflows, and audit trails meeting security standards.
Guidance
Role-specific playbooks keep executives, contributors, and admins aligned. AI-generated outputs are always flagged with confidence scores so humans stay in control.
Displays model confidence (0–100) with provenance and last update timestamp.
Executive adjustments note the actor and time directly on the card metadata.