The gap between an AI proof of concept and something your organization actually runs on is wider than most vendors will admit. recruit22 builds AI solutions that cross that gap — scoped honestly, integrated properly, and deployed by practitioners who've done it before.
The failure mode is almost always the same. A promising proof of concept gets built in isolation — impressive in a presentation, disconnected from the systems, workflows, and data realities the organization actually runs on. Then the gap between the pilot and production turns out to be larger than anyone planned for. The project stalls. The investment sits unused
We've built AI solutions that went all the way to production and stayed there. That experience shapes how we scope, how we integrate, and how we set expectations from the first conversation. If you're evaluating AI initiatives, starting a build, or trying to recover a stalled implementation — we'll tell you what it actually takes.
Most projects stall here — after the demo, before the systems integration begins
The most common failure root cause — infrastructure, pipelines, and preparation are underestimated at the scoping stage
Where we focus — from first conversation through deployment and validation
Before you build, you need to know whether your data, infrastructure, and organizational context are ready to support it. We assess your environment honestly and give you a clear picture of what an AI initiative would actually require.
The right model depends on your problem, your data, and your constraints — not on what's generating the most press. We evaluate options against your specific use case and recommend based on fit, not trend.
When off-the-shelf models don't fit your domain, your language, or your data — we train and fine-tune on your specific context. We've trained models on institutional language and deployed into production workflows.
An AI model that operates outside your existing systems creates more work than it saves. We map your workflows, identify integration points, and build connectors that let AI work inside the tools your team already uses.
AI is only as good as the data behind it. We structure, clean, and prepare training data — and where needed, design the pipelines that keep that data current and usable as your solution scales.
Getting a model to production isn't the end of the work — it's where accountability starts. We manage deployment, validate outputs against real use cases, and stay engaged through the period where edge cases surface.
AI vendors are everywhere right now, and most proposals sound similar: large language models, automation, transformation. The language moves faster than the implementations. We're not going to tell you AI will solve every problem or that every organization is ready for it today.
What we will tell you is what we've seen work, what we've seen fail, and what your specific situation actually calls for. If the honest answer is that you're not ready yet, or that a simpler solution would serve you better, we'll say that. That's not a sales pitch. It's how we work.
Production AI built on real data infrastructure, integrated into existing workflows, validated against actual use cases — not demos. Every engagement we point to made it past deployment.
Pilots built in isolation from real data. Scope set by timeline, not by what the problem requires. Deployment treated as the finish line instead of the starting line.
We'll tell you if you're not ready. We'll tell you if a simpler solution fits better. We scope honestly before the work starts, not after it goes sideways.
A city government needed to translate public health mandates and news releases into all seven of its official languages — in real time, as information was being published. We built a microsite integrated into their existing CMS, trained a custom AI model on the city's own language and institutional voice, and integrated the model directly into the publishing workflow. Every mandate went live in all seven languages automatically — no manual handoff, no delay. Throughout the COVID-19 pandemic, every constituent received accurate public health information in their language at the same time it was published.
Whether you're starting from scratch, evaluating vendors, or trying to understand why a previous effort stalled, tell us where you are. We'll give you a straight answer on what it would take to move forward.
