Decision Capture and Learning
AI systems that learn from your decisions and improve over time through pattern recognition and workflow optimization
Every approval, rejection, and edit you make teaches the system what good looks like for your business.
What It Does
Decision Capture and Learning records every decision you make across all automations - approvals, rejections, edits, preferences - and analyzes these patterns to improve AI performance over time. When you consistently approve certain types of outputs or frequently edit the same elements, the system identifies these patterns and surfaces insights about where automation is working well and where it needs refinement.
Why It Matters
Traditional automation is static. You set it up once, and it keeps making the same mistakes until someone manually updates it. Your team develops expertise by learning from experience, but your automations don't. This creates a maintenance burden where you're constantly fixing the same issues instead of improving the underlying system.
Decision Capture and Learning closes this gap by treating your decisions as training data. The system learns what you value, what you reject, and how you modify AI output - then uses these insights to suggest improvements and optimize workflows over time.
Business Impact
Over 3-6 months, businesses typically see 40-60 percent reduction in approval rejections as the system learns what you want. An automation that starts with 70 percent approval rate improves to 90+ percent as decision patterns accumulate. Your team spends less time correcting AI and more time on strategic work.
Compound Learning: Improvements in one workflow can inform similar processes. If you consistently edit email tone in sales outreach, the system can suggest applying similar adjustments to customer support communications.
How It Works
Capture Every Decision
System records all approvals, rejections, and edits across workflows and agents with full context of what was presented and what you chose.
Pattern Recognition
Analytics engine identifies patterns: high approval rates (AI performing well), high rejection rates (needs improvement), consistent edits (systematic adjustments needed).
Workflow Optimization Analysis
System analyzes decision patterns to surface insights: which workflows need prompt refinement, where approval gates could be removed (AI proved reliable), where additional guardrails are needed.
Improvement Recommendations
Based on accumulated patterns, system suggests specific optimizations: prompt adjustments, workflow refinements, approval threshold changes.
Continuous Improvement
As you implement recommendations and make more decisions, the learning cycle continues - automations become more aligned with your preferences over time.
Use Cases
Common Questions
Does this mean AI will change my workflows without asking?
No. Decision Capture analyzes patterns and surfaces recommendations - you decide whether to implement them. The system never modifies workflows autonomously. You maintain full control over when and how improvements are applied.
How much data does the system need before it can make recommendations?
Depends on the pattern. Obvious issues (90 percent rejection rate) surface within 10-15 decisions. Subtle patterns (tone adjustments, formatting preferences) require 30-50 decisions for reliable recommendations. The system won't suggest changes until it has statistical confidence in the pattern.
Can I see what data is being captured?
Yes. Full transparency into decision data: see every approval, rejection, and edit with timestamps and context. Review what patterns the system has identified and the data supporting each recommendation. Export decision data for your own analysis if needed.
What if my preferences change over time?
Decision Capture weighs recent decisions more heavily than old ones. If you change email tone preferences, the system adapts as new patterns emerge. You can also manually reset learning for specific workflows if you're making intentional strategic changes.
Last Updated: 2025-11-20