Felix
Forecasting Agent
Predicts revenue, CAC, ROAS 90 days out. Learns from every miss. 78% accuracy → 91% in 90 days.
Remembers:
- •Seasonal patterns
- •Your growth curve
- •Past forecast errors
How Our AI Workforce Actually Works
Six specialized agents. One orchestration system. Continuous learning that compounds daily. Here's exactly how they work together.
The Team
Meet the 6 Agents
Each agent specializes in one thing. Together, they compound knowledge.
Maya
Memory Agent
Stores every conversation, preference, and constraint. Never forgets your CAC cap or that TikTok test from August.
Remembers:
- •Your $65 CAC cap
- •847+ past conversations
- •Every test you've run
Sam
Strategy Agent
Tests unlimited scenarios. Simulates budget shifts before you spend. 87% confidence on outcomes.
Remembers:
- •Elasticity curves
- •Test constraints
- •Scenario results
Parker
Performance Agent
Calculates true incremental ROAS. De-biases platform lies. Shows what actually drove revenue.
Remembers:
- •Incrementality baselines
- •Attribution models
- •Holdout test results
Dana
Data Agent
Pulls from Meta, Google, TikTok, Shopify. One source of truth. No more reconciling 6 dashboards.
Remembers:
- •Platform credentials
- •Data mappings
- •Sync schedules
Dex
Delivery Agent
Auto-sends reports to Slack, Sheets, email. Learns when you want them and how you want them formatted.
Remembers:
- •Report schedules
- •Format preferences
- •Recipients
Agent Communication
How They Talk to Each Other
Real example: You ask one question. Six agents collaborate to answer it in 30 seconds.
YouMaya
Should we shift 20% from Google to Meta?
MayaFelix
User asking about 70/30 Meta/Google split. Pull their CAC constraints and past Meta performance.
FelixDana
Need Q4 revenue forecast if we shift 20% to Meta. Their CAC cap is $65.
DanaParker
Pulling last 90 days Meta vs Google performance. What's true incremental ROAS for each?
ParkerSam
Meta incremental: 3.1x. Google: 2.9x. But Meta CAC trending up. Need scenario test.
SamDex
Ran 1,000 simulations. 70/30 increases CAC to $71 (over cap). Revenue drops $12K. Don't do it.
DexYou
Don't shift. 70/30 split puts CAC at $71 (vs your $65 cap) and drops revenue $12K/month. Current 60/40 is optimal.
Total time: 30 seconds
Six agents coordinated. Your CAC constraints remembered. 1,000 scenarios tested. Decision made with 87% confidence. You didn't spend a dollar.
Learning Loop
How 78% Becomes 91%
Every decision teaches them. Every outcome refines the model. Accuracy compounds.
1. You make a decision
Shift $20K from Google to Meta
2. Agents execute & track
Dana monitors daily. Parker tracks incrementality.
3. Outcome measured
CAC went to $68. Revenue up $8K. ROI positive.
4. Agents learn from delta
Felix: 'I predicted $10K revenue lift, actual was $8K. Meta elasticity lower than model.'
5. Models update
Felix adjusts Meta elasticity curve. Maya stores constraint: 'Meta performs 20% below Google at high spend.'
6. Next forecast improves
Next time: 'Meta shift will yield $8K (±$1K)' — More accurate because he learned.
The Result
Month 1: Felix forecasts with 78% accuracy (industry baseline)
Month 3: 83% accurate (learned from 50 decisions)
Month 6: 89% accurate (learned from 200+ decisions)
Month 9: 91% accurate (learned from 500+ decisions)
Every decision makes the next one better. That's compound learning.
Memory System
How 847 Conversations Are Stored
Maya never forgets. Here's how memory actually works.
Conversational Memory
Every question you've asked. Every preference you've stated. Stored with full context.
- 847+ conversations stored
- Perfect recall of past discussions
- Context preserved across sessions
Constraint Memory
Your CAC caps, margin requirements, budget limits. Never needs to ask twice.
- $65 CAC cap remembered
- 15% minimum margin stored
- Budget guardrails enforced
Test Memory
Every campaign you've tested. What worked, what didn't, why. Never repeat failed tests.
- TikTok test from August stored
- Creative performance tracked
- Channel learnings preserved
Pattern Memory
Your seasonal trends, customer behavior, channel elasticity. Learned from your data.
- Q4 spike patterns identified
- Weekend vs weekday performance
- Creative fatigue curves
Why This Matters
When your analyst quits, all tribal knowledge walks out the door. When Maya stores a conversation, it's preserved forever. Every future agent benefits from every past interaction.
Zero knowledge loss. Perfect institutional memory.
Multi-Agent Advantage
Why 6 Agents Beat 1 AI
Specialization creates mastery. Coordination creates intelligence.
Single AI
Jack of all trades, master of none
Mediocre at everything
Cresva (6 Agents)
Each agent masters one domain
Excellence through specialization
How Specialization Works
Felix only forecasts. Gets really good at forecasting.
Parker only does attribution. Becomes expert at de-biasing.
Maya only manages memory. Never forgets a thing.
Agents share knowledge but maintain specialization.
Compound learning across specialized domains.
Result: Each agent operates at expert-level in their domain. Together, they compound into something no single AI can match.
Watch Them Talk to Each Other
Ask once. Watch six agents work together, remember everything, and deliver an answer in 30 seconds.