Product owners
The demo was great. Three months later nobody owns the model. That is this job.
After the model ships
A model that worked in a trial will rot in production. We put watching, retraining, and a rollback around it so the live product does not silently get worse.
Infrastructure behind products including AICO and IbisHR.
When answers get worse, someone is told
A bad update is not a permanent surprise
The path is the same at ten times the use
The offer
The care plan for models and AI features: how they ship, how you know they still work, and how you update them without a hero weekend.
If you do not have a model yet, start with machine learning solutions.
Who it is for
If you own the budget, the product, or the operation, this page should help you decide.
The demo was great. Three months later nobody owns the model. That is this job.
You can ship software. You need the same discipline for the AI piece.
You need a trail: which version answered, and how to turn it off.
What you get
The work buyers look for, described as outcomes.
Services
Pick a starting point. We will confirm it on a call.
A short working session so you know what to fund first.
Faster model deployment
Improved model reliability
Challenges
If this sounds like your week, we should talk.
Time
We design mlops & ai infrastructure so this shows up in the business, not only in a demo.
Risk
We design mlops & ai infrastructure so this shows up in the business, not only in a demo.
Scale
We design mlops & ai infrastructure so this shows up in the business, not only in a demo.
Trust
We design mlops & ai infrastructure so this shows up in the business, not only in a demo.
Why Solvefy
Named products in production, a call that ends with a next step, and AI when it helps the live system.
Proof
Results from live products.
Voice AI kept healthy in live dispatch, with a way to roll back
Read full case study
How we work
A sequence you can follow without a glossary.
Step 1
We confirm this step with you before we move on.
Step 2
We confirm this step with you before we move on.
Step 3
We confirm this step with you before we move on.
Step 4
We confirm this step with you before we move on.
Step 5
We confirm this step with you before we move on.
Guides
Useful if you are sharing this page with a colleague.
Copilots, agents, and help inside a live product.
The prediction itself.
A cloud plan that matches cost, risk, and growth.
Questions teams ask before they hire for MLOps & AI Infrastructure.
Yes, if customers depend on it. One model that silently fails is enough to need watching and a rollback.
Same idea, different object. DevOps is how the app ships. MLOps is how the model ships, ages, and gets replaced.
Yes. We start with how it is called in production, then add watching and a safe update path.
Tell us what is stuck. We will map the next step on a call.