FlipIQ
Fractional CTO for a stalled platform rebuild: multi-tenant foundations, a real delivery process, half the hosting bill, and a product finally in customers' hands.
Mission results
- Lower hosting bill
- 50%
- Hand deployments
- 0
- From stalled rebuild to onboarding customers
- ~3 months
Where it started.
FlipIQ builds software for real estate investing teams. Its flagship product, Command, is a data- and AI-driven sales CRM that helps teams nurture agent relationships, spot opportunities in the market and work each deal from first contact to closing with less cycle time and more profit.
When we came in, Command was in the middle of a messy rebuild from a legacy platform. Many of the old architecture's flaws had carried over, the team was deploying by hand, hosting costs were far too high, and the product had not yet gone to market.
What we had to work within.
- C-01The data model was built for one user, the founder, and was never designed for multiple companies on one platform.
- C-02The legacy product was heavily over-engineered, and much of that complexity had carried into the rebuild.
- C-03A small team whose AI use ranged from pure vibe coding to almost none, with no shared practice.
- C-04No systems in place to manage the software development life cycle from plan to production.
Decisions we made.
- 01
Rebuild the data model for multi-tenancy
Command had to serve many investing teams, not one founder. We reworked the data model so multiple companies could run on the platform safely, before onboarding a single customer.
- 02
Lean out the product
The original was significantly over-engineered. We made product calls on what customers actually needed at launch and cut the rest, so the team could finish and ship.
- 03
Automate every deploy
We put full CI/CD in place and retired hand deployments. Releases became repeatable and low-risk, and the team got its time back for product work.
- 04
One AI practice for the whole team
We trained the developers on AI-assisted engineering best practices and showed them how to run several features in parallel, turning inconsistent experiments into higher throughput.
- 05
Pay only for what the product uses
We analyzed AWS usage, turned off services the product didn't need, and rebuilt the app to run on ECS. The hosting bill dropped by half.
What we built.
A multi-tenant data model on PostgreSQL that lets many investing teams use Command side by side, and a leaner product scoped to what customers needed at launch.
The delivery engine around it: a CI/CD pipeline replacing manual deploys, agile planning with professional tooling, and a team trained to use AI consistently and run features in parallel.
A right-sized AWS footprint, with unused services retired and the app rebuilt to run on ECS.
What changed.
Within about three months FlipIQ went from a stalled single-user rebuild to a multi-tenant product in market, onboarding its first customers, with a team that ships through a repeatable process at half the hosting cost.
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