The Work Before AI
Every company has ideas for generative AI. Turning one into a working system is the hard part, and the difficulty starts well before any model is chosen.
AI depends on the systems around it. In most companies, data sits scattered across tools that hold different versions of the same record. Legacy code runs core operations and connects poorly to anything new. The workflows AI is meant to improve live in people's heads and in email threads, still waiting to be mapped.
Integrating AI means building that foundation on both sides. On the technical side, data needs structure, legacy systems need clean connections, and security has to hold across all of it. On the business side, teams need to map how work flows today, agree on what success looks like, and decide where people keep control of the output.
That combined effort decides whether an AI project becomes part of daily operations or stays an idea. It is also the part a client finds hardest to judge before signing.
The Case for AWS
Flatiron builds its AI work on AWS because the platform covers what production demands. It gives access to leading foundation models through one service, so a client can test and switch models as the market moves. It keeps data inside the client's own environment, under the security and access controls the client already trusts. And it scales with use, so cost follows real demand.
Our engineers design alongside AWS specialists, which keeps each architecture current with a platform that moves as fast as the models do. AWS funding programs reduce the cost of early projects for clients, and AWS account teams join through co-sell as an engagement grows. A first AI project becomes a funded, lower-risk step in a longer plan.
The expertise came first, built over years of client work. The Competency is AWS confirming it.
Two Kinds of Proof
To award the Generative AI Competency, AWS reviewed Flatiron across two tracks, using four real client engagements as the evidence.
The first is case study validation. AWS asked for the business problem behind each project, the goals agreed with the client, and the measurable results after launch. Each engagement needed defined KPIs, a baseline, and a clear method for how results were measured. AWS also confirmed that each solution fits its own definition of generative AI work, and that each one has a plan for continuous improvement.
The second is technical validation. AWS specialists reviewed how each system is built and run. They checked how client AWS accounts are governed and who holds access. They reviewed a cost analysis for every engagement, to confirm the architecture follows the client's economics. They looked at monitoring, deployment readiness, and the runbooks a team uses when something breaks. And they reviewed architecture diagrams against AWS's own reference patterns.
Where AWS found gaps, the feedback was specific, and closing each one made the work stronger. The result is one standard applied across every engagement, checked by the team that sets it.
The Work Behind It
Four engagements carried Flatiron through the Competency. Each one moved generative AI from an idea into daily operations, in four very different businesses.
1. An automotive commerce platform. Car buyers dropped out of long forms full of financial terms, and the core product ran on a legacy stack that made experiments risky. Flatiron built a conversational assistant that lets buyers and sales reps search for cars and qualify for financing by chat or voice. It connects directly to the existing systems and data, so the client's own team kept its focus on the roadmap. By month four, the assistant was fully integrated and rolling out to select clients.
2. An US audio media company. Editorial teams at one of the largest audio companies in the US scanned 40 to 60 news sources by hand to prepare daily shows. Flatiron built an AI pipeline that gathers those sources, summarizes each article, and groups the results by show section. Editors review and adjust the briefing before it goes out, and the whole system runs inside the client's own AWS account. It reached production in under five months and delivers the briefing every night.
3. A global technology publication. The publication wanted AI summaries on its articles and wanted to keep its existing publishing platform as it was. Flatiron built a plugin that connects that platform to AWS, so editors generate a summary on demand and choose which model writes it. The work performed well enough in production that the publication and Flatiron released it as open source for the wider media industry.
4. Snapshot AI. Flatiron's own product pulls data from nine engineering tools and turns it into AI-guided insight for engineering leaders. Its AI assistant reads that context to flag delivery risks and performance trends. When adoption reached large organizations, Flatiron added AI-powered onboarding that maps people, teams, and projects automatically. It runs in production for internal and external teams, which makes it the clearest view of how Flatiron builds AI: on its own product.
What It Means for Your Next AI Project
Every company planning AI faces the same question before it starts: are our data, systems, and workflows ready to support it? The answer depends on technical and business work that is hard to judge from the outside.
The AWS Generative AI Competency answers it in advance. The parts of an AI system a client finds hardest to check (governance, cost, operations, and architecture) have already been checked by AWS.
Flatiron starts every AI project with the business. We shape the engineering around each client's goals, and AWS backs that work on the technical side. For your next AI project, that is the line between a vendor who can plug in a model and a partner whose work AWS has already stood behind.
To talk through an AI project on AWS, reach the Flatiron team at contact.
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