AI Delivery, Done Right
From “Proof of Concept” to “Proof You’re Crushing It”
We Don’t Just Ship AI. We Manage the Mayhem.
From misaligned teams to scope creep nightmares, our delivery process keeps your AI project on the rails—and off the post-mortem list.
Most AI Projects Fizzle. Ours Finish.
80% of AI initiatives stall somewhere between the fancy prototype and… total silence. Not here. Our delivery management approach combines old-school project chops (hello, milestone tracking) with modern agility (sprint planning that doesn’t sprint you into burnout). Translation? Real results, fewer fire drills, and less Slack drama.
Less AI Theater, More AI Results
Most AI delivery plans sound like a TED Talk in a lab coat. Ours? It’s built for the real world.
We combine the best parts of Agile, MLOps, and actual accountability to move your AI project from demo to delivery—without disappearing after launch.
Think of it like opening a restaurant:
- 🧠 You figure out what people want (strategy)
- 🥬 You gather the right ingredients (useful data)
- 🔧 You build the kitchen (infrastructure)
- 🧪 You test the recipes (model validation)
- 🚀 You serve it up—hot, safe, and ready (deployment + monitoring)
No black boxes. No ghosting. No serving spaghetti at a sushi joint.
🚧 AI Doesn’t Fail. Delivery Does.
AI projects rarely die because of bad code. They die because delivery was duct-taped together with crossed fingers and Slack emojis. No planning? 👎
of AI Projects
Fail without proper management
of Project Time
Spent on data cleaning and prep
of AI Projects
Are considered “alchemy” without mature delivery processes
💣 The Stakes Are Higher Than Your Burn Rate
AI isn’t a magic box—it’s a high-maintenance genius. Skip steps or feed it junk, and it’ll cost you more than just pride in your prototype.
🛠️ Delivery Isn’t the End—It’s the Engine
AI projects rarely fail because the model flopped—they fail because delivery was duct-taped together at the last minute. We built a system to fix that: tight coordination, zero guesswork, and momentum that doesn’t stall after “go live.”
This isn’t about heroics. It’s about managing the moving parts so your AI solution actually ships—and sticks.
How It Works (Step-by-Step)
Every great AI project starts with a real problem, not just a shiny tool. Our seven-phase framework blends structure with agility, balancing the nerdy stuff (MLOps, governance, pipelines) with the human stuff (clarity, alignment, and actual results). Here’s how we do it:
1. North Star Alignment
No more “solutions in search of problems.”
We start with a business-first discovery workshop. No feature wishlists, no tech jargon. Just one hard-hitting question:
“What’s slowing you down or costing you money?”
2. Data Quest Begins
The “no, your spreadsheet is not ready” phase.
We wrangle the data—internal systems, third-party sources, sometimes handwritten notes on napkins. We set up ETL pipelines, define schemas, enforce governance, and clean data until it shines. If you’re migrating to the cloud, we handle that too.
Garbage in, garbage out. 70–80% of AI project time is spent here for a reason—it’s the single biggest factor in whether your model thrives or fails.
3. Design & Build Like a Boss
Where brains meet blueprints.
We architect the solution and model it out—think workflows, APIs, ML models, agents, or all of the above. Our team iterates through design sprints, algorithm testing, and feasibility tuning. This is also where we make tech stack decisions based on your needs (custom models vs pre-trained APIs, on-prem vs cloud, etc.).
This is where ideas become infrastructure. It’s also where we keep you from reinventing the AI wheel—speed and scalability come from using what works, not building everything from scratch.
4. The AI Test Kitchen
Breaking it before your customers do.
We unleash a full validation gauntlet—technical tests (accuracy, performance, bias), user acceptance trials, simulated agent interactions, and success metric checks. We run pilots or controlled rollouts to iron out real-world surprises. And yes, we test for explainability and fairness, especially for sensitive domains.
A model that works in a Jupyter notebook and crashes in production? We don’t do that here. This phase is about confidence before commitment.
5. Lights, Camera, Deployment!
Making it real, without making a mess.
Deployment includes cloud provisioning, model serving (often via CI/CD pipelines), agent configuration, and integration with your real systems (ERP, CRM, web apps). We also run training sessions and create SOPs to help your teams actually use this thing.
All the value lives in production. This is where the work turns into working software. Bonus: we set up “day 2” processes—error handling, rollback procedures, and support pathways.
6. Feedback Loop Forever
Maintenance isn’t boring—it’s ROI protection.
AI systems degrade. Models drift. People find new use cases. That’s why we build in dashboards, performance monitoring, automated alerts, and retraining mechanisms. Your solution evolves, just like your business does.
This step prevents “set it and forget it” syndrome. With continuous monitoring, we catch issues early and make iterative improvements that keep your AI valuable over time.
7. Bonus Round: Pilots, Change Management & Compliance Crunch
These optional add-ons aren’t bells and whistles—they’re your safety net.
- Pilot Projects: We de-risk things by testing on a small scale. Think: one department, one workflow, one user cohort.
- Change Management: We help you roll out internal comms, update policies, train humans, and actually get adoption.
- Regulatory Readiness: HIPAA? GDPR? NIST AI RMF? We do compliance like accountants do taxes: thoroughly, carefully, and with a little caffeine-induced stress.
Real AI Delivery Means Real Adoption
No training? No change management? No chance.
You can’t duct tape AI to your business and expect it to stick. The projects that scale—the ones that actually deliver ROI—have more than clever models. They’ve got the right people, the right prep, and a delivery plan that doesn’t fall apart after the demo. This is the difference between an expensive science project and a solution that transforms your business.
of Successful Deployments
Include user training and change management
of AI Projects
Achieve widespread adoption across the enterprise due to lack of change management, misaligned incentives, and rollout chaos
of Organizations
Cite compliance and governance as their top barrier to AI deployment
What You’ll Love (and What Might Make You Nervous)
Is This Right for You?
Let’s Build AI That Actually Works
Our process isn’t magic—it’s just proven. (But hey, it feels magical.)
Behind the Scenes at Inventive
One of our project leads once compared AI delivery to baking with an experimental oven: “You measure everything, follow the recipe—and still burn half the batch unless you’re watching it like a hawk.”
That’s us. The hawks. With spreadsheets. And personality.
We don’t just show up with a checklist—we obsess over the why behind your goals, cheer on your team like it’s a World Cup match, and bring humor to every Zoom call that’s dangerously close to nap time.
“I’ve never seen a delivery process that felt this human,” one founder told us. We cried a little. Then we fixed his data pipeline.
