Why Goodfoot
Founded in 2015. Building for production ever since.
We started Goodfoot as an IT consultancy serving the same industries we serve today—finance, telecom, and pharma. From the beginning, our work focused on the hardest part of enterprise technology: making it actually work in production. That meant integrating complex systems, navigating security reviews, and building software that survived contact with real infrastructure and real users.
We moved into AI early, applying transformer models to enterprise problems before GPT made the technology a household name. The pattern was familiar—promising technology stalling at the integration layer. Models that work in controlled settings fail when they meet legacy systems, messy data, compliance requirements, and users who did not attend the demo. That gap between proof-of-concept and production system is where our decade of enterprise engineering experience matters most.
Today, we scope every engagement around a specific business problem, build with production standards from the first sprint, and set go/no-go checkpoints so you see evidence before committing further. If a pilot is not hitting the KPIs we agreed on, we recommend stopping—because our reputation depends on the systems we put into production, not the projects we sell.
One Team, Start to Finish
We limit the number of engagements we take on so that every client works directly with senior engineers throughout—the same people from the discovery call through production deployment. There is no handoff to a separate delivery team. That means we turn down work regularly. It also means the people making architectural decisions in week one are the same people debugging edge cases in week twelve.
When we take on a project, our engineers are hands-on—writing code, reviewing architecture, sitting in your security review meetings. That is how we maintain quality, and it is why we deliberately cap the number of engagements we run simultaneously.
- The engineers you meet in the first conversation build your system
- Faster course corrections when priorities shift—no telephone game through coordinators
- We cap concurrent engagements to maintain direct involvement on every project
NYC Headquarters
Based in New York City—at the center of the industries we serve.
Center of enterprise
Home to the largest concentration of financial institutions, media companies, and healthcare organizations in the country.
Same-day collaboration
Complex enterprise AI projects move faster when your engineering partner can be in the room for the whiteboard session, the security review, or the executive presentation.
Local knowledge
We understand the pace, the standards, and the regulatory environment that Fortune 500 companies headquartered in the Northeast operate under. We have sat in those compliance meetings. We know what those security questionnaires look like.
We ship. That is the difference.
Enterprise AI requires three things at once: deep AI expertise to architect the right solution, engineering discipline to integrate it into your environment, and senior attention to see it through to production. Most firms are strong in one of these. We built Goodfoot around all three.
That means we scope, build, integrate, and deploy with the same team—from the first discovery conversation through production handoff. The result is fewer handoffs, faster iteration, and a system that works in your environment, not just in ours.
- Your team works directly with our principals, start to finish.
The same engineers who scope your project build it, review architecture decisions, and sit in your security meetings. We limit concurrent engagements so every client gets that level of attention.
- Integration is where we spend most of our engineering time.
Most enterprise AI projects stall at the integration layer, not the model. We plan for your CRM, your legacy database, your security requirements, and your users from discovery—before the first line of model code is written.
- We build systems designed to outlast the engagement.
Clean code, full documentation, knowledge transfer to your team, and optional ongoing support. Our goal is your self-sufficiency, not your dependency.
- We keep humans in the loop where it matters.
Every system we build includes review workflows, confidence thresholds, and escalation paths so the AI handles volume while your team handles judgment. Human oversight is not a bottleneck—it is what makes production AI trustworthy in regulated environments.
Ready to see how we work?
Start with a 30-minute conversation focused on your challenge, not our capabilities.