Back office ops · Production

Build.inc uses LangGraph multi-agent architecture to complete CRE land diligence in 75 minutes versus four weeks manually

The problem

Land diligence workflows for energy-intensive CRE projects (data centers, renewable energy) took over four weeks, consumed nearly half of the total project timeline, and cost developers millions of dollars. The fragmented US regulatory landscape and the need for deep specialist expertise made traditional software solutions inadequate.

First attempt

Traditional software could not handle the complexity and variability, fragmented data ecosystem, and high-stakes specialist requirements of CRE development workflows.

Workflow diagram · grounded in source
1
Land diligence request initiated
trigger
“This is the task of researching a piece of land to understand if it is suitable for a particular project”
2
Master Agent orchestrates workflow
ai_action
“At the top is the Master Agent— "the Worker" —which coordinates the entire workflow and delegates tasks to Role Agents”
3
Role Agents handle specialized functions
ai_action
“Role Agents— "the Workflows" —who handle specialized functions like data collection or risk evaluation”
4
Task Agents execute individual tasks
ai_action
“Each Role Agent manages one or more Sequence Agents, which carry out Workflows— multi-step processes that can involve up to 30 individual tasks executed by Task Agents. The Task Agents are equipped with the most relevant tools, context a…”
5
Parallel async execution via LangGraph
ai_action
“we leverage asynchronous execution via LangGraph to run multiple agents in parallel, dramatically reducing overall processing time”
6
Land diligence output delivered
output
“accomplishes in 75 minutes what previously took humans over four weeks”
Reported outcome

Build.inc's first worker, Dougie, is now in production for CRE industry clients and completes land diligence in 75 minutes—what previously took humans over four weeks—with a depth and quality that human teams cannot match even over the course of several weeks.

Reported metrics
Land diligence workflow duration (automated)75 minutes
Land diligence workflow duration (manual)over four weeks
Sub-agent tasks orchestratedover 25
Individual tasks per sequence workflowup to 30
Show all 9 reported metrics
land diligence workflow duration (automated)75 minutes
land diligence workflow duration (manual)over four weeks
sub-agent tasks orchestratedover 25
individual tasks per sequence workflowup to 30
US jurisdictions with distinct regulationsover 30,000
pre-construction workflow share of project timelinenearly half of the total project timeline
project risk from mishandled land diligencecost them millions of dollars
developer cost of pre-construction workflowscost developers millions of dollars
output depth vs. human teamsdepth to the output that human teams can't match even over the course of several weeks
Reported stack
LangGraphLLMs
Source
https://blog.langchain.dev/how-build-inc-used-langgraph-to-launch-a-multi-agent-architecture-for-automating-critical-cre-workflows-for-data-center-development/
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Build.inc's first worker, Dougie, is now in production for CRE industry clients and completes land diligence in 75 minutes—what previously took humans over four weeks—with a depth and quality that human teams cannot m…

What tools did this team use?

LangGraph, LLMs.

What results were reported?

Land diligence workflow duration (automated): 75 minutes; Land diligence workflow duration (manual): over four weeks; Sub-agent tasks orchestrated: over 25; Individual tasks per sequence workflow: up to 30 (source-reported, not independently verified).

What failed first in this deployment?

Traditional software could not handle the complexity and variability, fragmented data ecosystem, and high-stakes specialist requirements of CRE development workflows.

How is this back office ops AI workflow structured?

Land diligence request initiated → Master Agent orchestrates workflow → Role Agents handle specialized functions → Task Agents execute individual tasks → Parallel async execution via LangGraph → Land diligence output delivered.

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