Midas Labs is a forward-deployed AI engineering entity — a new category of product studio purpose-built for the AI era. We were architected from the ground up around AI-native delivery: AI systems do the heavy lifting, and a small cadre of senior engineers provide the judgment, context, and decision-making that AI alone cannot.
We act as a technical co-founder for venture builders and startups, and as a high-velocity engineering partner for established firms. We step in to accelerate timelines, eliminate technical debt, and resolve critical development logjams — delivering outcomes that traditional teams cannot match on cost, speed, or quality.
Traditional consultancies sell headcount. They staff projects with layers of junior engineers supervised by a senior lead, and they bill by the hour. The economics reward slow delivery. Midas inverts this model entirely.
We deploy AI systems as the primary workforce — autonomous agents that analyze codebases, profile databases, generate documentation, review code for vulnerabilities, map schemas, and draft architecture specifications. Our senior engineers direct the AI systems, validate outputs, make architectural judgments, and own the decisions.
The result: ClubSpeed receives the depth and breadth of output that would traditionally require an 8–10 person team over 6–8 weeks — delivered by a three-person senior team in 3 weeks, at a fraction of the cost. This is a structurally different operating model.
ClubSpeed is the world's leading venue management software for family entertainment centers, serving go-karting tracks (with hardware timing device integration), trampoline parks, and multi-attraction complexes. Concurrently, ClubSpeed operates Resova, an activity booking platform for smaller venues.
Built circa 2007 with incremental updates. SQL Server with complex bi-directional on-premise ↔ AWS sync. Significant operational complexity.
MySQL with 1,500+ per-tenant databases on shared schema. Severe architectural fragmentation blocks platform-wide rollouts.
ClubSpeed's strategic objective is to replatform and merge both products into a single, unified system. This transition must be completely seamless — requiring "zero-downtime" automated migration. Speed is not optional — it is the primary competitive differentiator.
Evaluate both architectures end-to-end, normalize data structures, assess the in-progress prototype, and produce a detailed, actionable replatforming strategy.
Comprehensive assessment of both legacy platforms — architecture diagrams, data model documentation, integration inventory, and infrastructure review.
Detailed evaluation of the CPO's build — code quality, salvageability, gaps, and production-readiness with clear keep/rework/replace recommendations.
Normalized target data model and step-by-step "single-click" migration strategy with transformation logic, validation gates, rollback procedures, and phased cutover.
Complete mapping of all capabilities across both platforms — categorized into carry-forward, redesign, consolidate, and deprecate.
Technology stack recommendations, ADRs, infrastructure topology, security model, observability design, and cross-cutting concern strategies.
Testing standards, performance benchmarks, observability architecture, and canary deployment / feature-flag rollout plan.
Migration cohort segmentation, communication templates, support escalation framework, knowledge transfer plan, and training recommendations.
Prioritized risk assessment, phased execution plan, team structure, milestone definitions, cost-of-delay analysis, and estimated build timeline.
Detailed breakdown of resources, timeline, and cost for the fully unified platform — ready for immediate approval and kickoff.
Presuming that necessary system access, repository access, and stakeholder availability are provided promptly at kickoff.
| Week | Focus | Key Activities |
|---|---|---|
| Week 1 | System Evaluation & Data Deep-Dive | Environment access, codebase review, data model audit, integration mapping, AI-accelerated schema profiling |
| Week 2 | Prototype Review & Migration Design | CPO build assessment (AI-assisted), unified data model, migration strategy, feature parity matrix |
| Week 3 | Architecture, Quality & Roadmap | Target architecture, testing/observability strategy, customer comms plan, risk assessment, Build SOW |
Three senior humans backed by AI systems as the primary analytical and production workforce. No junior roles — AI replaces the labor layer entirely. Every human exists to make decisions, not to write boilerplate.
This engagement is billed as a fixed-cost project. The fee covers all human labor, AI system usage, internal review cycles, and up to two rounds of revisions on all 9 deliverables. For context: a traditional consultancy would staff this scope with 5–8 people over 6–8 weeks at a cost of $75,000–$120,000+. Midas delivers the same scope in 3 weeks at less than half the cost.
| Milestone | Amount | Trigger |
|---|---|---|
| Project Kickoff | $11,500 CAD (33%) | Upon signed SOW and confirmed environment access |
| Mid-Engagement | $11,500 CAD (33%) | Delivery of draft Technical Audit, Prototype Assessment, and Feature Parity Matrix |
| Final Delivery | $11,500 CAD (34%) | Delivery and acceptance of all remaining deliverables |
All invoices in Canadian Dollars (CAD). GST/HST added as applicable. Net 15 days.
The following rates apply to the anticipated Build Phase. Every role is a senior decision-maker backed by AI systems — no junior tiers. Rates reflect the combined output of the human engineer plus the AI systems they direct.
| Role | Rate (CAD) | What You're Getting |
|---|---|---|
| Technical Product Manager | $200/hr | Strategic direction, stakeholder management, AI-driven roadmap and documentation output |
| Senior Solutions Architect | $175/hr | Architecture decisions, data modeling, AI-driven codebase analysis and code review at scale |
| Senior Cloud & Infrastructure Engineer | $140/hr | Infrastructure design, DevOps strategy, AI-driven config scanning and cost modeling |
| Senior Software Engineer | $120/hr | Production engineering, feature development, AI-driven code generation and testing |
How to compare: A traditional consultancy would staff with 1 senior architect ($175–$250/hr), 2–3 mid-level devs ($100–$150/hr each), and 1–2 juniors ($60–$90/hr each) — totaling $500–$800+/hr in blended team cost. Midas delivers equivalent output through a single senior engineer directing AI systems.
The following should be addressed prior to or during the kickoff meeting:
The global FEC market is projected to grow from $34.4B (2025) to $93.5B (2035) at a 10.5% CAGR. Software vendors are rapidly consolidating around cloud-native, all-in-one platforms with AI-powered features, cashless/RFID integration, and open APIs.
Widely recognized as the industry standard for go-kart and racing venues with a 4.9/5 rating on Capterra. Strengths: exceptional support, comprehensive all-in-one management, strong CRM/marketing. Vulnerabilities (from user reviews): steep learning curve, occasional bugs, integration lag — symptoms of the legacy architecture this effort aims to eliminate.
3,000+ venues, 30+ countries. $50M raise (Insight Partners + J.P. Morgan). $4B in annual transactions. 100M+ guest visits/year. Cloud-native with open API. Launched AI-powered ROLLER iQ. Acquired BookNow Software (Sep 2025). Claims 30% increase in average online revenue for switching venues.
20+ years in FEC. Strong North American base. All-inclusive pricing, no hidden fees. 15-minute POS training claim. US-based 24/7 support. Deep operator relationships.
RFID-integrated hardware: cashless payments, game card management, access control. Strong in arcade/game segment. Modern, purpose-built stack.
RFID cashless payments, access control, loyalty. Open platform enabling existing POS/ticketing integrations. Growing in waterparks and large attractions.
Emerging players: Aluvii (cloud-native all-in-one), Party Center Software (event-driven FECs), LEAP360 (digital-first marketing), Smeetz (AI dynamic pricing). Several are cloud-native from inception.
Every competitor is cloud-native. The on-prem/cloud sync is a competitive liability.
ROLLER's API ecosystem drives switching cost and retention. Must be API-first with dev portal.
ROLLER iQ launched late 2025. Architect for AI from day one, not as a bolt-on.
Timing device integration is a genuine, defensible differentiator. Must preserve and extend.
Multi-location chains need centralized reporting, config, and pricing. Key buying criterion.
Every month of delay extends ROLLER's window to capture market share with $50M war chest.