How AI Suggestions Work
When a quote is created (either by a client or internally), Alan analyzes each requirement and suggests:- T-Shirt Size: The recommended size (XS through XXL)
- Confidence Level: How certain Alan is about the suggestion
- Reasoning: Explanation of why this size was suggested
AI suggestions are generated automatically when requirements are created. They appear in the quote details for team members to review.
What Alan Considers
Brief Content
Alan analyzes the requirement details:- Goal and expected outcome
- Current behaviour (for development)
- Technical constraints
- Design types and target audience (for design)
- Timeline requirements
Historical Data
Alan reviews similar past tasks for this client:- Previous task sizes and outcomes
- Actual vs quoted time ratios
- Patterns in requirement types
Client Scores
Alan factors in client-specific performance metrics:Deliverability Score
How development/design tasks perform against estimates. A score of 1.15 means work typically takes 15% longer.
QA Score
How many QA cycles the client typically needs. A score of 1.8 means ~80% more QA overhead than average.
QA Impact on Suggestions
For development requirements, high QA scores may influence sizing:- QA Score > 1.5: Alan may recommend bumping up the size
- Reasoning: Explains if QA overhead influenced the suggestion
- Context: First-pass rate and average cycles shown in prompt
Understanding Confidence Levels
| Confidence | Meaning | Recommendation |
|---|---|---|
| Low | Limited information in brief or few similar tasks | Review carefully, may need adjustment |
| Medium | Reasonable information and some historical data | Good starting point, verify details |
| High | Clear scope and good historical data | High confidence in suggestion |
Viewing AI Suggestions
In Quotes
AI suggestions appear in the requirement details:- Suggested size badge next to the requirement
- Click to see reasoning and confidence
- Team can accept or override the suggestion
Reasoning Examples
- Standard Suggestion
- QA-Influenced
- Low Confidence
Suggested: Medium (3-6 hours)Based on the scope described (custom product filter), this aligns with similar filtering features we’ve built. Historical data shows Medium tasks for this client typically take 4.2 hours.
Scope Review (Check with Alan)
When you write scope manually (rather than using “Generate with Alan”), you can ask Alan to review it before finalising.When the button appears
The Check with Alan button appears in the scope editor header when:- You are editing a requirement
- There is scope content in the editor
- The scope was written manually (not AI-generated)
What the review checks
Alan evaluates your scope against the client brief on three dimensions:| Dimension | What it measures |
|---|---|
| Clarity | Is the scope clearly written? Would a client understand what they’re getting? |
| Completeness | Does the scope cover all aspects of the brief? Are deliverables and acceptance criteria present? |
| Brief Alignment | Does the proposed work match what the client actually asked for? |
Understanding the assessment levels
| Assessment | Meaning |
|---|---|
| Good | All scores 7+ with no high-priority suggestions |
| Needs Improvement | Some scores 5-6, or has high-priority suggestions to address |
| Major Issues | Critical gaps — scores below 5 or significant misalignment with the brief |
Applying suggestions
If the review finds areas for improvement, an Apply Suggestions button appears. Clicking it rewrites the scope using AI, incorporating the review feedback while preserving your original intent. After applying, an Undo button appears so you can revert to your original scope if needed.Pre-Calculated Subtask Suggestions
When a quote is approved and converted to a task, AI suggestions are pre-calculated for each subtask:What Gets Calculated
- Development subtasks: Adjusted by client’s deliverability score
- Design subtasks: Adjusted by client’s design score
- QA subtasks: Adjusted by client’s QA score
How It Works
- Quote is approved and task is created
- Subtasks are generated with base estimates (from t-shirt split)
- AI suggestions are calculated for each subtask
- Suggestions are stored and displayed in schedule dialog
In the Schedule Dialog
When scheduling a subtask:- “Alan suggests: Xh Ym” appears below the time input
- Apply button to accept the suggestion
- Basis explanation: Why this time was suggested
Suggestions are pre-calculated at task creation, so there’s no loading delay when opening the schedule dialog.
Score-Based Adjustments
Development/Design Work
Adjusted estimate = Base estimate x Deliverability Score Example for Medium task (4.5 hours base), client score 1.15:- Suggested: 5h 15m (4.5 x 1.15 = 5.175, rounded to 15 min)
- Basis: “Based on client history (+15% over quoted)“
QA Subtasks
Adjusted estimate = QA allocation x QA Score Example for QA review (30 min base), QA score 1.8:- Suggested: 54m (30 x 1.8 = 54)
- Basis: “Adjusted for high QA cycles (1.8x avg)“
Admin-Created Quotes
AI suggestions are now generated for both:- Client-submitted quotes: Via client portal
- Admin-created quotes: Via internal admin panel
Best Practices
Review Reasoning
Always read the reasoning - it explains what influenced the suggestion
Check Confidence
Low confidence suggestions may need manual adjustment
Consider Context
AI can’t see everything - use judgment for unique situations
Improve Briefs
Better briefs lead to better suggestions - add detail when possible
Related Documentation
Client Intelligence
Understanding deliverability scores
QA Scoring
How QA scores affect suggestions
T-Shirt Sizing
Understanding size ranges and estimates
Subtasks
Subtask scheduling and time estimates