Strong convergence: the surface ask (feasibility prediction) masks the deeper problem—the data required to predict doesn't exist in usable form. Construction timelines live in MS Project, concert schedules in ROME, institutional knowledge in people's heads. The off-season window is a revenue constraint: a project that bleeds into the season destroys show dates. Most respondents independently named data consolidation as prerequisite.
Where views diverged: the majority proposed building a feasibility scoring model as soon as data was consolidated—timeline fit, historical comparables, schedule conflicts, IRR thresholds. Geoff Gibbins pushed harder: the problem is a fragmented workflow with no process owner. A prediction layer bolted onto ad-hoc process improves tasks without changing outcomes. His recommendation: redesign the workflow first, then build tooling.
Process ownership gap came up independently and is critical. Every respondent who touched it agreed: a feasibility tool without named ownership, defined review cadence, and agreed go/no-go criteria will underperform regardless of model quality.
Nearly all respondents structured the first 30 days identically: (1) walk through 2-3 real projects end-to-end with the VP to understand actual decision-making; (2) audit the data—Airtable, MS Project, and institutional knowledge; (3) start the ROME API conversation in Week 1. Critical point across responses: the people who know what drives delays aren't in systems—they're on the team. Week 1 knowledge capture is essential.
Respondents split on Day 30 deliverables. Conservative camp wanted validated data, feasibility model spec, and clear go/forward before building. Aggressive: James Word proposed a full 50-project portfolio dashboard by Day 30. Jan Beranek aimed for a working system tested on 5-10 real projects with feedback incorporated. The right path depends on data quality—if Airtable's 2.5 years of history is clean, speed is justified. If not, conservative sequencing is safer.
Critical tactical point: design ROME integration with manual fallback from day one. API access takes 4-8 weeks (developer, security, privacy). If the project requires live feeds before producing output, July is at risk. Solution: build on manually exported hold/confirmation data during API integration, treating the live feed as Phase 1 enhancement.
All respondents proposed phased builds with Phase 1 gates. Budget variance (\$110K–\$300K+) reflects scope: lower end = core model + Airtable; higher end = process redesign + full API + governance.
| Expert | Engagement Model | Budget | Fit |
|---|---|---|---|
| Allister Hercus / Stoop Studios | Full-stack build — phased 12-week delivery with Airtable-native integration, portfolio scoring, and decision-support dashboard | To be scoped on a call (full-build framing; $20–250K+ range per firm profile) | 8.9 |
| Graeme Crawford / Crawford McMillan | 3-phase: data consolidation → model build → Airtable integration | $120K–$175K | 8.5 |
| James Word / Next Focus | 2-phase: portfolio tool by Jul → production hardening post-season | $122K–$195K | 8.5 |
| Aanikh Kler / Lazer Technologies | 3-phase: requirements & feasibility → build → deploy | ~$250K–$300K | 8.5 |
The team is making go/no-go decisions on ~50 capital projects without a structured way to evaluate whether each one can actually be completed within the off-season construction window. The current process — manual data pulls across disconnected systems, institutional knowledge that doesn't transfer — doesn't scale to that volume. Projects are finishing late as a result.
This is a decision-support problem, not a forecasting problem. The team's expertise is strong; it just isn't codified or connected to data in a way that supports portfolio-scale evaluation.
What we'd validate first:
Week 1: Walk through 3–5 real projects end-to-end with the VP. Get into Airtable, pull MS Project samples.
Week 2: Translate VP's evaluation approach into a structured scoring model. Validate it captures how decisions are actually made.
Weeks 3–4: Working prototype scoring real projects connected to Airtable.
Phase 1 (Weeks 1–3): Discovery + Data Foundation — map evaluation workflow, audit data quality, codify decision criteria, scope integrations.
Phase 2 (Weeks 4–9): Build + Integration — decision-support tool with Airtable integration native; scheduling system integration when developer team is ready.
Phase 3 (Weeks 10–12): Portfolio Run + Hardening — run full ~50 project portfolio, iterate on edge cases, stabilize for year-over-year use.
Full-stack build framed as 12-week phased delivery with Airtable-native integration, portfolio scoring, and decision-support dashboard. To be scoped on a call; budget range $20–250K+ depends on firm profile and baseline tooling needs.
Data readiness: Can eat the timeline — approach: codify decision criteria first, layer in historical calibration as data becomes available.
Scheduling system integration: A coordination risk, not a technical one — plan to deliver value without it initially.
Construction document AI for a national supplier — automated pipeline ingesting construction docs, scoring bids against structured criteria, CRM-integrated. 80+ projects scored automatically in first month. AI Chief of Staff for a Private Equity COO — Airtable as persistent data layer, reads/writes records, tracks tasks across departments, keeps execution moving without manual status chasing. AI Project Cost Management for a Land Developer — invoice ingestion, line-item classification across projects/cost codes, AI analyst layer for plain-language cost queries.
The core issue: data doesn\'t exist in usable form. Timelines scattered across MS Project, separate systems, people\'s heads. Until consolidated, any model is guessing. Key validation: historical data completeness? API access precedent? Go/no-go authority?
Week 1: Kickoff: understand workflow, review 3-5 past projects, get system access.
Week 2: Catalog data—what exists, usable, missing. Start consolidation.
Week 3: Draft feasibility model on paper, validate against 5-10 projects.
Week 4: First prototype—score real 2027 projects for stakeholder reaction.
Phase 1 – Data Consolidation & Model Design (Weeks 1-4, ~$40-50K): Consolidate historical data (MS Project, Airtable, API). Define scoring model.
Phase 2 – Model Build & Validation (Weeks 5-8, ~$40-60K): Build model, back-test, incorporate variables. Deliver working model with recommendations.
Phase 3 – Integration & Handoff (Weeks 9-12, ~$30-65K): Embed into Airtable workflow, train team, document for year-over-year reuse. Total: $120K–$175K.
Historical data quality: If past project timelines were never recorded consistently, the prediction model has nothing to learn from. Assign a dedicated point person for the data collection sprint in Weeks 1-3.
API access timeline: Start that conversation in Week 1, not Week 5. If the developer team is resource-constrained or privacy review takes weeks, the concert schedule integration becomes a bottleneck.
Data infrastructure & predictive analytics. Digital OOH (data warehouse, revenue forecasting). PE leasing (15.7K units, ERP optimization).
The core problem is decision infrastructure. The team is evaluating 50 capital projects without a shared, structured view of what each project requires vs. what each venue can support. The inputs exist—construction timeline estimates, venue off-season windows, concert schedules, historical project data, IRR models—but they are fragmented. The prediction layer is valuable but secondary: it's only meaningful once the data is organized. Before you can predict whether a project is feasible, you need to know how long similar projects have actually taken. That means a structured historical data foundation is prerequisite. Key validation questions: What does Airtable already capture? How variable is the off-season window by venue? Is IRR calculated from a standard template?
Week 1: Airtable API access Day 1-2; stakeholder interviews Day 3-5 to understand how timeline estimates are made and get the canonical IRR model; initiate ROME developer conversation and set up manual import path in parallel.
Week 2: Airtable integration live, historical data assessment (how many completed projects with reliable outcome data?), MS Project extraction for planned vs. actual durations.
Weeks 3-4: Core build—feasibility scorecard logic (timeline fit, historical realism, financial filter, portfolio conflicts), IRR template, first batch of concert schedule data ingested.
Weeks 5-8: Portfolio tool: all 50 projects visible with feasibility signals; VP calibration walkthrough where model outputs are validated against his expert judgment.
Phase 1 – Rapid Data Foundation & Portfolio Assessment Tool (Weeks 1-8, ~$60-90K): Airtable integration, historical benchmarks by project type and scope, feasibility scorecard for all 50 projects, IRR template, portfolio dashboard. Designed specifically to support July go/no-go decisions.
Phase 2 – Production Hardening & Year-Over-Year Intelligence (Months 3-6, ~$62-105K): Outcome tracking as 2027 projects execute, prediction model recalibration, scheduling integration hardening if on manual path during Phase 1, process documentation for year-over-year use. Total: $122K–$195K.
ROME integration: Design from day one with two parallel tracks: automated API path and manual CSV import. The manual path protects the July deadline regardless of API timeline.
Historical data quality: If Airtable data can't be normalized within the first two weeks, predictions should be framed as directional ranges with explicit confidence intervals, not point estimates. Be transparent about prediction confidence from the start.
Builds data integration systems and AI-assisted decision pipelines for portfolio-level support. (1) Multi-source integration—project management systems, performance databases, external APIs; portfolio decisions compressed days to hours. (2) AI-assisted workflow and feasibility assessment—decision pipeline with confidence scoring, qualification gates, and feedback loop; throughput increased with improved decision quality.
The team is evaluating ~50 capital projects per cycle without a system that can reliably predict whether each can be completed within the off-season construction window. The underlying challenge is that feasibility depends on interconnected variables — permit timelines, contractor availability, material lead times, venue-specific constraints — that currently live in disconnected systems and people's heads. A prediction model without reliable input data will produce unreliable outputs; the data foundation and the modeling layer have to be built together. Key questions before scoping: What's actually in Airtable today and how consistently is it populated? What does the concert scheduling system expose — API, database, or exports? Which constraints are truly venue-specific vs. generalizable across the portfolio? What does a 'wrong' prediction cost the business?
Days 1–7: Full data audit — map every field in Airtable, pull MS Project samples, document what the scheduling system exposes. Identify which variables are populated consistently enough to model against.
Days 8–14: Work with the VP to reconstruct 10–15 historical project decisions. Build a ground truth dataset: what inputs predicted the outcome?
Days 15–30: Prototype a scoring model on that historical dataset. Validate accuracy against known outcomes. Deliver: a working prototype with documented accuracy metrics and a clear go/no-go recommendation on the full build.
Phase 1 — Data Foundation & Prototype (Weeks 1–4, ~$40K–$60K): Data audit, historical reconstruction, prototype feasibility model validated against past projects. Deliverable: working model with accuracy metrics and full build recommendation.
Phase 2 — Production Build (Weeks 5–10, ~$80K–$120K): Full pipeline — project intake from Airtable, feasibility scoring engine, portfolio dashboard. Scheduling system integration when developer team is available.
Phase 3 — Portfolio Run (Weeks 11–12, ~$20K–$30K): Run all ~50 projects through the system, edge case refinement, production stabilization. Total: ~$140K–$210K.
Input data quality is the primary risk — scenario-based modeling requires consistent historical inputs to produce reliable confidence intervals. If Airtable data is sparse or inconsistent, Phase 1 will surface this early enough to adjust scope.
Scheduling system integration is a dependency outside this engagement's direct control — the build plan should not assume it's available from day one.
Lazer Technologies applies ML-based risk modeling and scenario analysis to complex capital decision problems. Prior work includes feasibility and risk modeling for large-scale construction and infrastructure projects, with a focus on probabilistic confidence intervals and decision-support architecture. The firm brings strong technical depth in data pipeline design and model validation.