Automated Multifamily Lead Qualification Agent with Mastra AI & Bito AI
Multifamily operators struggle with high lead volumes and slow response times, resulting in missed lease opportunities. Build a TypeScript lead-handling agent with Mastra AI and Bito AI assistants to process tenant inquiries, score credit compliance, and schedule property tours with 95% automated accuracy.
What you are building
The core problem, expected build, and operating context for this challenge.
Construct an automated tenant inquiry workflow using Mastra AI step execution, tool calling, and Bito AI assistance interface integration.
How work is evaluated
Evaluates tenant lead qualification accuracy and calendar tour scheduling action triggers.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
correct_qualification_flag
Correctly flags lead qualification based on 3x rent income policy
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
qualification_accuracy
Percentage of correctly classified leads • target: 0.95 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Design a Mastra AI workflow that ingests tenant inquiries and extracts intent.
Implement a credit score and income verification rule check.
Integrate Bito AI assistant context to dynamically craft personalized tour scheduling responses.
Achieve 95% classification accuracy across varied tenant communication styles.
Reference links and supporting material
100 simulated tenant inbound inquiries across email, SMS, and portal messages with ground-truth income and lease preferences.
Challenge setup
Evaluates tenant lead qualification accuracy and calendar tour scheduling action triggers.
Challenge input
JSON containing lead_id, message, income_monthly, desired_move_in
Mastra AI
Framework assignment for building TypeScript workflows.
Bito AI
Tool required for response synthesis and developer assistance.
Evaluated output
JSON with qualified boolean, target_unit_type, follow_up_action
- Correctly flags lead qualification based on 3x rent income policy
- Percentage of correctly classified leads • target: 0.95 • range: 0-1
- Benchmark: AgentOps-Leasing-Bench
- Qualification Accuracy target: 0.95
- 1 public reference case
- JavaScript execution harness
- javascript sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- Bito AI · Required
- Mastra AI · Optional
- Metaflow · Optional
- Mastra AI · Optional
Evaluation contract
- correct_qualification_flag · Weight 1
- qualification_accuracy · Weight 1
Metrics
- qualification_accuracy · Target 0.95
Benchmarks
- AgentOps-Leasing-Bench
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing lead_id, message, income_monthly, desired_move_in
Output: JSON with qualified boolean, target_unit_type, follow_up_action
Execution harness: JavaScript
Evaluation tools
- Mastra AI · TypeScript Agent Orchestration
- Bito AI · Assistant & Code Generation Interface
Recipe freeze marker
No recipe freeze time is recorded.