From Pilot to Production in 90 Days
Most AI consulting engagements follow the same pattern. Month one: discovery. Month two: more discovery. Month three: a strategy deck. Month four: a prototype on clean data. Month six: a conversation about why it isn't working on real data. Month nine: a change order.
We don't do that.
We deploy production AI systems in 90 days. Not a prototype. Not a proof of concept. A system running on real data, used by real operators, in a production environment.
Here is how.
Weeks 1-2: Audit and strategy
We don't spend two months on discovery. We spend two weeks.
In the first week, we audit your data infrastructure. What systems exist. What condition the data is in. What access is available. What the integration points look like.
In the second week, we identify the one problem worth solving first. Not a wishlist of 15 use cases. One specific workflow where AI can create measurable impact. We map the technical path from current state to production deployment.
End users are in the room from day one. Not month three. Day one. Because if you build something the end users didn't ask for, they won't use it.
Weeks 3-6: Build and iterate
We build on your real data. Not sample data. Not synthetic data. The actual messy, incomplete, multi-source data your team works with every day.
Bi-weekly demos with your stakeholders. Every two weeks, you see a working version of the system. You give feedback. We adjust. This is not waterfall development where you see the finished product at the end and it is wrong. You are involved at every step.
By week 6, the core system works. It handles the primary workflow. It runs on real data. It may not handle every edge case yet, but it is functional and your team has been using it for two weeks.
Weeks 7-10: Harden and deploy
This is where most vendors stop. They hand over the prototype and move on. We do the opposite. We spend four weeks specifically on the things that make a system survive in production.
Edge case testing. What happens when the input data is malformed? When a source system goes down? When the model encounters something it has never seen? We map every failure mode and build handling for it.
Monitoring setup. Dashboards that show system health, model accuracy, data pipeline status, and user activity. When something goes wrong, you know about it before your users do.
Deployment to the target environment. Not a staging server. The actual production infrastructure your team will use. With the actual security controls and access patterns in place.
Weeks 11-14: Support and transfer
We don't deploy and disappear. After launch, we stay for 30 days.
Performance tuning based on real production usage. Retraining cycles as new data comes in. Bug fixes. Configuration adjustments. The first month of production always surfaces issues that testing doesn't catch. We are there to handle them.
Knowledge transfer happens throughout, but the final two weeks focus on documentation, training, and making sure your team can operate and maintain the system independently.
When we leave, you own it completely. The code. The models. The infrastructure. The documentation. No vendor lock-in. No ongoing licensing fees.
Why 90 days works
The constraint is the point. 90 days forces discipline. It forces you to pick the highest-impact problem instead of boiling the ocean. It forces decisions instead of committees. It forces real data instead of sample data.
Most AI projects don't fail because they didn't have enough time. They fail because they had too much. Unlimited timelines breed scope creep, feature bloat, and deliverables that never ship.
90 days. One problem. One system. Running in production. If it doesn't work, you don't pay.