Homter, a buyer-focused mortgage broker, worked with Crata AI on an AI Quickstarter consulting engagement to diagnose its business, prioritize its AI opportunities and define an executable roadmap. The result was 15 detailed AI solutions and a 3, 6 and 9 month roadmap with three investment and ROI scenarios.
Many AI opportunities and the need to decide where to start
Homter is a mortgage broker founded in 2017 with one distinctive trait. It acts as the buyer's agent, defending only their interests without the conflicts common in the sector, and guides them through the entire home-buying process. A strong digital focus meant it was already applying AI and automation across several of its processes.
That digital-first approach had already generated plenty of ideas. Homter had numerous automation and AI opportunities across sales and operations, and what it needed was a company-wide framework to prioritize them. With so many opportunities on the table, the question was no longer whether to adopt AI. The challenge was deciding which to tackle first, how much to invest and in what sequence to move.
Without that map, each initiative competed with the others with no shared criteria of value, cost and feasibility. Homter wanted to move from a list of possibilities to a clear plan, with priorities, dependencies and an estimated return that would let it decide with confidence.
A diagnosis that turns opportunities into a roadmap
With Crata AI, Homter ran an AI Quickstarter consulting engagement to diagnose its business, data, processes and technology stack. From there, it identified and prioritized its AI opportunities, estimated investment and return, and defined an executable roadmap spanning three-, six- and nine-month horizons.
The engagement began by mapping how Homter operates and which tools it already uses, then ranking each opportunity by value, cost and technical feasibility. Rather than an implementation, it gave Homter an investment-ready blueprint. This set out what to build, in what order, at what cost and with what expected return.
The engagement covered the entire diagnostic process:
- Diagnosed the business, data, processes and tools, assessing not just the data itself but its quality and reliability.
- Identified AI use cases and the clearest opportunities.
- Prioritized each opportunity by value, cost and technical feasibility.
- Analyzed investment and return across different scenarios.
- Defined a phased roadmap with specifications for the future implementation.
