The company needs a way to compare opportunities using value, feasibility, risk, data readiness, and operating cost.
Service 02 · AI systems in production
Put AI into production. Not another demonstration.
Calling a model is the easy part. The hard part is deciding where AI creates enough value, connecting it to the right data and actions, measuring whether it works, and fitting it into a process people can actually trust.
When to bring us in
The problem usually shows up before the right service name does.
Quality is inconsistent, evaluation is informal, or the system lacks permissions, observability, fallback, and human review.
Adoption is shallow because the workflow, ownership, standards, and incentives around the tool were never redesigned.
Specific engagements
A defined place to start.
The scope can expand when the evidence supports it. It does not need to start as an open-ended transformation program.
AI Opportunity & Readiness Review
Choose where to invest before committing a team to an attractive but weakly defined idea.
- Prioritized use-case portfolio
- Data and integration readiness
- Risk and economics analysis
- First implementation roadmap
AI Pilot to Production
Take one high-value use case through product design, system architecture, evaluation, and a working first release.
- Product and workflow design
- Data and model architecture
- Evaluation and release criteria
- Production capability with operating controls
AI Evaluation & Reliability Layer
Turn an AI feature from something that looks promising into something the team can measure, release, and improve.
- Test datasets and evaluation criteria
- Quality, safety, latency, and cost measurements
- Regression testing and monitoring
- Feedback and review loops
AI Workflow Integration
Redesign a real business or engineering workflow around AI, then put the process, tools, and ownership into practice.
- Current and target workflow
- Integrated tools and system changes
- Roles, controls, and escalation paths
- Training through actual work
Relevant experience
Built from work where the system had to hold up.
Our engineers have built proprietary data platforms, ML training pipelines, predictive systems, image-analysis workflows, and AI-enabled products at companies including Amazon, Meta, Klaviyo, Novartis, Novo Nordisk, and successful startups. We treat AI as a system component, not as a category of magic.
Start a conversation
Bring us the work you cannot afford to get wrong.
Tell us what needs to change, what is getting in the way, and what has already been tried. We will tell you plainly where we can help.
Talk with NewGeometry ↗