Two projects that show what happens when you combine deep enterprise experience with AI-accelerated delivery — platforms launched faster, smarter, and at a fraction of the traditional cost.
A healthcare startup needed a community platform for end-of-life care. Their first attempt with an offshore agency produced an unusable product.
Fermat Solutions stepped in as the fractional CTO — providing strategic leadership and hands-on development as a single-person team.
Build a full community platform — chat, forums, profiles, calendars — and get it into real users' hands, fast.
Think of it like hiring a full tech team — CTO, architect, and developer — but getting it all from one person powered by AI, at a fraction of the cost.
Continuous feedback loop: Build → Test → Feedback → Repeat — with a real non-profit partner throughout.
vs. 16 months traditional
cost savings
The Takeaway: Startups don't need enterprise budgets for enterprise-grade technology. With the right partner combining deep expertise and AI-powered workflows, you can go from zero to a fully launched platform in a fraction of the time and cost.
4-5 analysts spent their days manually listening to earnings calls, taking notes, and synthesizing insights — up to 5 reports each per day.
Fermat Solutions designed and built a multi-agent AI application that does the analysis work automatically — using both OpenAI and Anthropic models.
Give analysts back their time by automating the grunt work — so they can focus on the high-value thinking that actually makes money.
Imagine your best analysts never missing an earnings call again, and getting analyst-ready reports in minutes instead of hours — that's what Fermat built in just one month.
The Takeaway: If your most valuable people spend their time on work AI can now do faster and more thoroughly, you're leaving money — and competitive advantage — on the table. Two years ago, this would have been a six-figure, multi-month project. Fermat Solutions built it in one month.
Ongoing development to make the platform smarter and more personalized for each analyst over time — learning their preferences, their coverage areas, and the specific signals they care about most.