Databricks Community Genie-Powered App Challenge. Track A: Real-World Problem Solver.
Managing a construction portfolio means constantly answering questions across schedule, cost, tasks, contractors, inspections, change orders, and field reports. The problem is that the answer to a single question can be split across structured project data and pages of project documentation.
Crux brings those two worlds together.
A user asks a question in plain English. Databricks Genie establishes the authoritative structured picture: project risk, schedule variance, forecast cost, blockers, overdue work, contractor performance, and portfolio comparisons. When the question also needs context, Crux retrieves supporting project evidence that explains what is happening and why.
Genie discovers and quantifies the problem. Documentary intelligence explains and substantiates it. Crux turns both into a decision.
The idea, and who it is for
Consider a project manager trying to understand why a project needs attention.
The schedule may show a delay. The cost data may show a forecast overrun. Tasks may reveal overdue work. An inspection record may identify a blocker. A field report may finally explain the operational reason behind it.
Getting the complete picture normally means moving between different systems, reports, and datasets or relying on someone who knows how to query them.
Crux is built for project managers, operations teams, and construction leaders who want to ask those questions directly:
- Which projects are at high risk?
- Why is Stellar Heights at risk?
- What inspection is blocking it?
- What about its forecast variance?
The user does not need to know the schema, write SQL, or manually search through project documents.
One design decision became fundamental while building Crux:
Not every source should have equal authority.
Genie owns the structured business facts. Project documents provide supporting explanation and evidence.
One project, end to end
Take Stellar Heights.
Crux includes a Projects workspace where the portfolio can be explored before moving into a deeper investigation. Selecting Stellar Heights takes the user into the Intelligence workspace with the project context prepared:
- What should I know about Stellar Heights?
Genie establishes the authoritative structured state:
- High Risk
- 18 days behind schedule
- 8.10% forecast cost variance
- $3,928,500 forecast overrun
- 7 overdue tasks
- 1 active blocker
Those numbers answer what is happening.
But construction decisions often require the next question: why?
For the same inquiry, Crux retrieves relevant documentary evidence describing issues affecting Stellar Heights, including an HVAC fabrication backlog and staffing constraints, the INS-2003 electrical inspection issue, and its downstream impact on drywall work.
The two intelligence sources contribute to the same experience without competing for authority.
Genie establishes the measurable project state. Documentary intelligence explains the conditions behind it.
The answer is also inspectable.
Documentary claims include citations back to the retrieved project evidence, with source metadata such as the document and relevant location in the source.
The structured analysis is transparent too. Crux exposes the SQL generated by Genie alongside the result, so the user can inspect how the structured answer was produced instead of treating it as a black box.
The investigation can then continue naturally.
Ask:
- What inspection is blocking it?
Crux understands that the conversation still refers to Stellar Heights and identifies INS-2003, together with the relevant supporting evidence.
Then ask:
- What about its forecast variance?
Genie continues with the existing project context and returns the forecast variance without requiring Stellar Heights to be repeated.
The previous turns remain available in the same intelligence thread, including structured results, documentary evidence, citations, and generated SQL.
Genie at the core
Remove Genie from Crux and the core product no longer works.
The documentary layer could still retrieve project reports, but Crux could no longer establish authoritative project risk, schedule variance, forecast cost variance, overdue work, blockers, contractor performance, rankings, or portfolio-level metrics.
Genie is load-bearing in several ways.
- Genie owns the structured truth.
Project and portfolio metrics come from Genie over governed Databricks data. Crux does not independently recalculate those metrics after Genie responds. - Genie turns business questions into structured analysis.
The user asks in natural language. Genie determines the relevant data, generates the SQL, executes the analysis, and returns the structured result. Crux preserves and exposes the generated SQL. - Genie holds the structured conversation.
Follow-up questions continue the same Genie conversation. A question such as “What about its forecast variance?” can therefore build on the previous discussion instead of starting from zero. - The Genie Space carries the construction semantics.
Instructions, business definitions, relationships, and verified examples guide how concepts such as project risk, schedule variance, blockers, change orders, and contractor performance should be interpreted. - Genie stays authoritative when documents enter the answer.
A project report may explain why something happened, but it cannot redefine a Genie-owned KPI. Documentary evidence can support the metric; it does not become the source of truth for it. - Crux decides when Genie is required.
A supervisor routes each question into one of three paths: GENIE_ONLY, RAG_ONLY, or GENIE_PLUS_RAG. Structured business questions go through Genie, documentary questions can use retrieval, and broader project questions can invoke both.
That responsibility boundary became the core of the architecture:
Genie tells Crux what is true in the structured portfolio. Documentary intelligence helps explain why.
What you can ask it
Crux is not built around a fixed set of dashboard filters or pre-written queries.
Users can ask questions such as:
- Which projects are at high risk?
- Which of those is the most delayed?
- What should I know about Stellar Heights?
- What inspection is blocking it?
- What about its forecast variance?
- Which contractor has the most task slippage?
The user does not need to know which table contains the answer, how the tables join, which document mentions the issue, or what SQL should be written.
For a portfolio question, Genie performs the structured analysis.
For a documentary question, Crux retrieves the relevant project evidence.
For questions requiring both, the two paths are combined while maintaining their authority boundary.
Architecture
Crux runs as a Databricks App.
A Databricks-served supervisor receives the user's question and chooses between structured intelligence, documentary intelligence, or both.
The structured path uses Databricks Genie over governed Unity Catalog data for projects, project costs, tasks, contractors, inspections, and change orders through a SQL warehouse.
The documentary path begins with project files stored in Unity Catalog Volumes. Documents are parsed and deterministically chunked into governed tables, then indexed with Databricks Vector Search using hybrid retrieval.
For mixed questions, Genie establishes the authoritative structured state while the retrieval pipeline supplies supporting documentary evidence. Crux then combines the two deterministically so that the evidence layer cannot overwrite Genie-authoritative metrics.
The application also preserves the active Genie conversation and bounded project context for multi-turn questions.
What I learned building it
- Authority was harder than routing.
Deciding whether to call Genie or retrieval was only part of the problem. Once both systems contribute to an answer, defining which system is allowed to state which facts becomes much more important. - More model calls did not mean a better system.
An earlier architecture included additional model calls for documentary planning and final synthesis. Evaluation showed they were unnecessary. Removing them produced a simpler mixed path while keeping Genie authoritative. - RAG quality starts before retrieval.
Document identity, parsing, chunking, versioning, serving eligibility, indexing, and citation metadata all had to be deterministic before the retrieval layer became dependable. - A chat interface does not automatically create a conversation.
Multi-turn behavior required preserving the real Genie conversation and resolved project context. After a reset, an ambiguous question such as “What about its forecast variance?” correctly loses that context. - Transparency belongs in the product.
Generated SQL and documentary citations are visible to the user. Natural language makes Crux accessible; inspectability makes the answer useful for decision-making.
The result
Construction intelligence is not only about finding a number.
A project leader needs to know which project needs attention, how significant the problem is, what is causing it, and what evidence supports that conclusion.
Databricks Genie provides the structured intelligence core that makes those questions accessible through natural language.
Crux builds the decision workflow around it.
Genie discovers and quantifies the problem.
Documentary intelligence explains and substantiates it.
Crux turns both into a decision.
