MLOps & AI Engineering
Models that survive deployment.
Observability, evaluation, and rollback baked in from day one.
In plain English.
We build the operational backbone that keeps AI systems healthy after launch: evaluation, monitoring, versioning, and rollback. Most AI projects die not in the build but in the months after, when quality drifts, costs creep, and nobody notices until a customer does.
We instrument your models and prompts so you can see accuracy, latency, and cost in real time through Datadog and Sentry, catch regressions with automated evals before they ship, and roll back safely when something breaks. Deployments become routine instead of risky.
Whether you're hardening one critical system or standing up MLOps across a portfolio, we put the practices in place that let you ship AI changes with the same confidence you ship application code, on AWS, GCP, or Azure, integrated with your CI on GitHub.
When you need this
- An AI system is live and you have no visibility into its quality or cost.
- Model or prompt changes ship blind, with no way to catch regressions.
- Quality has quietly drifted and you found out from a customer.
- You're scaling from one AI system to several and need real operations.
The deliverables, plainly stated.
- Real-time monitoring of accuracy, latency, and cost via Datadog and Sentry
- Automated evaluation suites that gate model and prompt changes
- Versioning and safe rollback for models and prompts
- CI/CD integration for AI changes through GitHub
- Cost-control and alerting on token and inference spend
- Runbooks and on-call practices for AI incidents
Typical duration
3 to 6 weeks
Investment band
$$$Significant investment
We scope in bands, not fixed numbers. Final pricing follows a quick scoping call.
A process built for this service, not a generic playbook.
- 01
Instrument the system
We add logging and metrics for accuracy, latency, and cost, surfacing them in Datadog and Sentry dashboards.
- 02
Build the eval suite
We create automated evaluations that run on every change and block regressions before they reach production.
- 03
Add versioning and rollback
We version models and prompts and wire safe rollback into your CI on GitHub so changes ship like normal code.
- 04
Operationalize
We set cost alerts, write incident runbooks, and establish the on-call practices that keep AI healthy at scale.
Team composition
A lead MLOps engineer and an AI engineer, with a solutions architect aligning observability to your cloud and CI setup.
Tools & frameworks
- Datadog and Sentry for observability and alerting
- Automated evaluation suites for regression gating
- GitHub Actions for CI/CD of AI changes
- AWS, GCP, or Azure for deployment
What we tie this engagement to.
Every engagement carries a revenue-tied KPI. These are the outcomes this service typically anchors on.
Full visibility into AI quality, latency, and cost
Regressions caught by evals before customers see them
Routine, low-risk AI deployments with safe rollback
Works with your stack
We deliver MLOps & AI Engineering inside the tools you already run.
MLOps & AI Engineering: common questions
What is MLOps and why does it matter?
MLOps is the operational practice of running AI systems reliably after launch: evaluation, monitoring, versioning, and rollback. It matters because most AI projects fail in the months after deployment when quality drifts and costs creep unnoticed.
How do you catch model regressions before they ship?
We build automated evaluation suites that run on every model or prompt change and gate the deployment, so a change that degrades accuracy is blocked in CI rather than discovered by a customer.
What do you monitor in production?
Accuracy, latency, and cost in real time through Datadog and Sentry, plus token and inference spend with alerting, so you see drift and budget overruns the moment they start.
Can you set this up on our existing cloud?
Yes. We deploy on AWS, GCP, or Azure and integrate with your existing CI on GitHub, so AI changes ship through the same pipeline and practices as the rest of your software.
How long does an MLOps engagement take?
Typically 3 to 6 weeks to instrument a system, build the eval suite, add versioning and rollback, and establish cost alerts and incident runbooks your team can run.
Often paired with this.
Ready to put MLOps & AI Engineering to work?
Tell us where you are and we'll tell you what's blocking revenue.