Technical Deep DiveFebruary 5, 20259 min read

Progressive Disclosure:
80-90% AI Cost Savings

How PROCUX intelligently filters context to send only relevant information to AI models, reducing costs by 80-90% while maintaining response quality.

How does Progressive Disclosure save 80-90%?

Traditional AI sends massive context (millions of tokens) to LLMs. PROCUX filters context to only what's relevant using agent-specific templates and relevance scoring, reducing costs from $4.50/query to $0.45/query—a 90% reduction.

The Hidden Cost of AI

LLM Token Pricing (per 1M tokens):

$30
OpenAI GPT-4
$3
Anthropic Claude 3.5
$1.25
Google Gemini Pro

PROCUX Analysis: The average enterprise query uses <5% of available context, but traditional AI systems send 100%—meaning 95% of AI costs are wasted on irrelevant information.

Cost Comparison

Traditional AI (All Context)

Tokens:2.5M tokens
Cost:$75/query
Time:10-15s

PROCUX Progressive Disclosure

Tokens:25K tokens
Cost:$0.75/query
Time:5-8s

99% Cost Reduction

How Progressive Disclosure Works

1

Intent & Domain Classification

Classify query intent (analytical, strategic) and domain (financial, marketing)

2

Base Context Analysis

Extract key entities, time references, stakeholders

3

Agent-Specific Template

Apply CFO template for financial queries, CMO for marketing, etc.

4

Context Source Gathering

Prioritize sources by relevance to agent and domain

5

Relevance Scoring

Score each context component (0-1), filter low-relevance items

6

Enriched Context Output

Final filtered context (10-20% of total data)

Agent-Specific Templates

Each C-level executive has a specialized context template:

CFO

Financial, budgeting, forecasting, risk management

CMO

Marketing, campaigns, customer insights, branding

CTO

Technology, architecture, security, infrastructure

CEO

Strategy, vision, stakeholder, growth

Context Source Confidence

Company DNAYour company knowledge base
95%
Database QueriesDirect data queries
98%
Executive AnalysisAI reasoning output
80%
Web SearchExternal data
60%
DocumentUploaded PDFs, docs
90%
InferenceAI guesses (needs verification)
50%

Production Impact

87%
Token Reduction
Average across all queries
$0.82→$0.11
Cost Reduction (GPT-4)
Per query average
$0.08→$0.01
Cost Reduction (Claude)
Per query average
$650K+
Annual Savings (10K queries)
Enterprise scale
No degradation
Response Quality
Same accuracy
+50ms
Response Time
Minor latency for filtering

Start Saving on AI Costs

See your potential savings with our ROI calculator

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