Intelligent Intake & Decision Logic Architecture
A full redesign of the intake and evaluation system — transforming scattered manual judgment into a structured decision engine that ingests data, applies consistent criteria, and outputs clean CRM records, clear routing, and reliable operational handoffs.
Reducing ambiguity. Increasing throughput. Enabling scale.
Project Type: Intake Architecture, Decision Systems
Client: Confidential SaaS Company
Timeline: 6 weeks
Scope: Intake redesign → Decision engine → Ops integration
The Problem
The team was drowning in ambiguity. Multiple intake sources, inconsistent forms, unclear evaluation rules, and ad-hoc human judgment were creating bottlenecks and unpredictable outcomes.
Decisions that should have taken seconds required Slack threads, manual reviews, and guesswork. Leadership didn’t have confidence that the right customers were being fast-tracked — or that the wrong ones weren’t slipping through.
The system wasn’t scaling. It wasn’t even stable.
The Work
Three layers of intervention: Diagnose → Re-architect → Operationalize.
Diagnose the Chaos
I began by mapping the real workflow — not the idealized one. I traced every source of incoming data, every inconsistent field, every ambiguous decision point, and every human intervention.
The output was a clear diagnostic:
The problem was not volume — it was fragmentation.
Too many sources. Too many variations. No unified logic.
Build a Unified Intake Architecture
I redesigned the intake into a multi-stage, structured system:
Stage 1: Clean data capture
Stage 2: Validation and error-proofing
Stage 3: Routing logic inputs
Each stage fed clean data into the decision engine. Instead of collecting everything upfront, the system now collected the right inputs at the right step.
Ambiguity dropped immediately.
Create a Decision Logic Layer That Scales
The core of the solution was a decision engine that applied:
Explicit criteria
Thresholds
Risk indicators
Completeness checks
Weighted evaluation rules
The engine produced two outcomes:
Auto-Approve (Fast Track)
Manual Review (Queue)
Crucially, I added a feedback loop that allowed decisions to improve over time without breaking the operational model.
This turned judgment calls into a repeatable framework.
Strengthen Downstream Operations
A clean decision layer meant the downstream systems finally had reliable inputs.
The outputs now included:
Clean CRM records
Prioritized review queues
Clear routing decisions
Automated handoff notifications
What used to require human correction or triage now happened automatically — and consistently.
The Result
The organization moved from ad-hoc, inconsistent judgment to a predictable, scalable evaluation pipeline. Manual review volume dropped, routing accuracy improved, and downstream teams received only qualified, correctly prioritized cases. Leadership gained a clear, shared understanding of how decisions were made — and a foundation that supports future automation and productization without increasing operational risk.