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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.

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What you are building

Construct an automated tenant inquiry workflow using Mastra AI step execution, tool calling, and Bito AI assistance interface integration.

How work is evaluated

Evaluation

Evaluates tenant lead qualification accuracy and calendar tour scheduling action triggers.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

correct_qualification_flag

Correctly flags lead qualification based on 3x rent income policy

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

qualification_accuracy

Percentage of correctly classified leads • target: 0.95 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

  • 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.

Resources

Dataset notes

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

Evaluation checks
  • Correctly flags lead qualification based on 3x rent income policy
  • Percentage of correctly classified leads • target: 0.95 • range: 0-1
Configured evaluation evidence
  • Benchmark: AgentOps-Leasing-Bench
  • Qualification Accuracy target: 0.95
  • 1 public reference case
Configured runtime
  • JavaScript execution harness
  • javascript sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • Bito AI · Required
  • Mastra AI · Optional
  • Metaflow · Optional
Orchestration
  • 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

  1. 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.

Frequently Asked Questions about Automated Multifamily Lead Qualification Agent with Mastra AI & Bito AI