Not a prototype. Not a demo. A production-grade platform with real data, real users, and real infrastructure — in 30 calendar days from our first conversation.
Most agencies quote 30 days and deliver in 6 months. We have five live production platforms that prove otherwise. The reason we can actually hit 30 days comes down to how we work, not how hard we work.
Every IPS project runs on a lean stack chosen for speed without sacrificing production quality: static HTML or Next.js on Vercel, Supabase PostgreSQL for the backend, and custom pipelines for whatever data or AI layer the project requires. No enterprise frameworks, no committee-driven architecture, no build systems that require three sprints to configure.
There are no project managers between the client and the engineers. There are no weekly status meetings to prepare slides for. There are no approval chains that add two weeks between decision and implementation. Senior engineers own every layer and can make decisions in real time. That is the structural reason 30 days is possible.
This is not a theoretical framework. This is the actual sequence we run on every project. It works because we own every layer and can move without waiting on anyone else.
We start with a single call. You tell us what you need. We ask questions that reveal what you actually have: Where does your data live? What does the source look like? What are the real user workflows, not the ones on a slide deck? By end of week 1 we have a defined schema, an architecture decision, and a confirmed scope of what ships on day 30. This week is the most important — a wrong decision here costs two weeks later.
This is the hardest week. We build the data pipeline: scraping, API ingestion, ETL, or whatever the data source requires. We stand up the Supabase project, deploy the schema, and start populating real data. By end of week 2, the database has real records in it and core backend functions are running. This is where most projects that claim to be fast actually stall. Data is always messier than expected. We budget for that reality.
The frontend comes to life this week. We build the interface against real data — not mock APIs, not placeholder content. If the project includes AI or ML components such as model inference, embeddings, predictions, or automated scoring, those get wired in during week 3 while the frontend is taking shape. By day 21 you have a working version of the platform you can log into and actually use. It is not polished. It is functional, and that matters more at this stage than visual perfection.
Authentication, access control, and deployment polish happen in the final week. We wire up Supabase Auth with role-based access, deploy to Vercel or your target hosting environment, configure your domain, set up monitoring, and run end-to-end tests against production data. By day 30, the platform is live at your domain. Real users can log in. The database is populated. The CI/CD pipeline is set. You have runbooks for every operational procedure. We hand you the keys and the full codebase.
Every platform listed below is live in production today with real users, real data, and real infrastructure. Not demos. Not beta software. Production deployments that were built on the 30-day process described above.
The most advanced public MMA prediction system in existence. Built on a database of 8,500+ professional fights going back decades, with a 5-model ML ensemble that generates probability predictions for every upcoming UFC and Bellator card. The system retrains weekly on new fight data automatically, without manual intervention. Fighters are ranked by a custom Elo-derivative rating system that accounts for opponent quality and performance trajectory, not just wins and losses.
A congressional corruption prediction engine. GovGreed aggregates eleven federal data systems — STOCK Act disclosures, congressional bill text, committee memberships, lobbying filings, FEC campaign finance, SEC EDGAR filings, and government contracts — and runs ML models to predict which bills will pass because committee members have financial incentives to make them pass. The dashboard has generated 13,052 active ML predictions across 42,143 scored bills. Live at govgreed.vercel.app.
An AI-powered CRM built specifically for cannabis dispensaries. StickySignal includes a purpose-built SMS campaign platform with carrier-grade delivery (no Twilio), AI customer segmentation by purchase behavior and lifetime value, real-time POS integration with Dutchie, Flowhub, Treez, Jane, and Blaze, and a HIPAA-aware multi-tenant architecture. It exists because every standard CRM either prohibits cannabis businesses or cannot handle the compliance requirements. We built the telecom layer ourselves.
An AI sales intelligence platform that generates comprehensive dossiers on any business or individual in under 8 minutes. Lead Detective runs 19 parallel scrapers and data sources simultaneously — LinkedIn, company databases, public filings, news archives, social signals, and proprietary sources — aggregates the results, and uses AI to synthesize a structured intelligence report that tells a salesperson exactly what they need to know before a call. Built for B2B sales teams that need intelligence at scale, not just a data dump.
An AI-native construction operations platform live at cdohr.ai, running day-to-day operations for a 65-person California roofing crew. GPS-verified selfie clock-in replaces paper timesheets, and an AI timecard triage layer flags discrepancies and cites the specific CA Labor Code section behind every compliance issue it surfaces. Built on roughly 210 Supabase tables and 153 edge functions.
Every IPS project ships with the same core deliverables. Not extras, not add-ons. This is the baseline of what you receive on day 30.
Database Design and Build
Full Supabase PostgreSQL schema, indexes, RLS policies, and seed data. Designed for your specific data model, not a generic template.
Data Pipeline
Whatever gets data into your system: web scraping, API ingestion, file imports, or ETL transforms. Scheduled and automated from day 1.
Frontend Dashboard
A complete user-facing application built in static HTML or Next.js. Tables, charts, filters, search — whatever the data requires to be useful.
Authentication and Access Control
Supabase Auth with email/password login, role-based access control, and row-level security enforced at the database layer.
Production Deployment
Live at your domain on Vercel. SSL certificate, CDN, environment variables configured. Not localhost. Not staging. Production.
AI/ML Integration (if applicable)
Model inference pipelines, embedding generation, automated scoring, or predictive features baked into the platform — not a bolt-on.
Documentation
Technical documentation covering the schema, API surface, deployment process, and all operational procedures. Written for engineers, not marketing.
Runbooks
Step-by-step operational runbooks for every common procedure: adding users, updating data sources, deploying changes, restoring from backup.
Full Code Handoff
Every line of code, every configuration file, every deployment script — transferred to your repository on day 30. No vendor lock, no ongoing dependency on IPS.
30 days is real, but it has preconditions. We are being direct about what does not fit in this model so you can assess whether it is right for your project.
Unlimited Revisions
30 days requires a locked scope. Scope changes mid-build push launch dates. We will flag and discuss any scope change the moment it is raised.
5-Stakeholder Approval Rounds
If every design decision requires sign-off from a committee, 30 days is not your model. We need one decisive contact on your side.
Indefinite Maintenance Contracts
We hand you the code. Post-launch support engagements are available but separate. The 30-day project ends with a handoff, not a lock-in.
Vague Requirements
We cannot build to a vibe. The discovery week requires you to know what problem you are solving, who the users are, and what data you have.
Enterprise Procurement Cycles
If your organization requires 3 months of vendor approval before a project can start, the 30 days does not begin until that is resolved.
Guaranteed Scope Expansion
We ship what we scoped. If you want more features after launch, that is a second engagement — not a renegotiation of the first one.
This model works best when:
You have a clear problem, a defined data source, a single decision-maker on your side, and a bias toward shipping over perfecting. If that is you, 30 days is not only possible — it is exactly how we prefer to work.
The questions we get before almost every project conversation.
One email starts the conversation. We will respond with questions — real ones about your data, your users, and your timeline. If we think 30 days is achievable for your project, we will tell you exactly how. If it is not, we will tell you that too.
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