Your pilot works.
It's still not in production.
Rivia takes AI systems that work in a notebook and turns them into infrastructure your team can trust: monitoring, error handling, and a deployment that doesn't depend on one person remembering how it works.
Demos don't fail. Production does.
Most AI pilots don't stall because the model is wrong. They stall because nobody built the parts that don't show up in a demo.
Six months later it's still running in a notebook, because shipping it feels riskier than leaving it alone.
No one finds out it's broken until a user complains.
Missing monitoring and alerting
One bad API response and the whole system falls over.
No retries or graceful degradation
When it gives a wrong answer, nobody can say why.
No tracing or observability
It only runs on one laptop, and only one person understands it.
No deployment or handoff plan
The infrastructure layer between a demo and a system people trust.
Every engagement is scoped around your specific pilot, but it usually touches the same five gaps.
Tracing and observability
Every request logged, every failure visible, every cost tracked, so when something goes wrong you know exactly where and why instead of guessing.
Error handling and retries
The system degrades gracefully when a dependency fails, instead of crashing the whole pipeline over one bad response.
Deployment
Containerized and reproducible, running somewhere real, not dependent on one laptop or one person's local environment.
Monitoring and alerts
You find out about problems before your users do, with dashboards your team actually checks.
Documentation and handoff
Your team can operate and extend the system without Rivia on call. That's the actual goal, not a longer retainer.
Three ways to start, depending on where you are.
Most engagements start with the audit. It shows whether a full sprint is even the right next step.
Production Readiness Audit
Rivia reviews your current pilot and identifies exactly what stands between it and production: security gaps, missing monitoring, fragile error handling, unclear ownership.
You get
A written assessment and prioritized roadmap, useful even if the engagement ends there.
Pilot-to-Production Sprint
Rivia rebuilds the critical pieces, including observability, deployment pipeline, error handling and monitoring, until your pilot is a system your team trusts in front of real users.
You get
A production-deployed system, full documentation, and a team that understands how to run it.
Ongoing Operations
Once it's live Rivia monitors performance, tunes prompts and retrieval, and adjusts as usage patterns change.
You get
A system that improves as it's used, instead of quietly degrading.
A quick way to check fit before talking.
This is probably a fit if
- Your pilot has been "almost ready" for months.
- Nobody in-house owns AI infrastructure.
- You can't explain why the model gave a wrong answer last time.
- You want it shipped, not another slide deck about it.
Probably not yet, if
- You haven't built a working pilot yet.
- You need a full in-house ML team built from scratch.
- You're looking for the cheapest possible fix, not a durable one.
Where is your pilot actually stuck?
No pitch deck, no sales theater. Tell Rivia what's blocking you from shipping and you'll get an honest answer on whether an audit or a full sprint makes sense. Usually a 20-minute conversation before anything else.