POC vs. production: what's the actual difference?
Most AI consulting produces a proof of concept. That's not what MLDeep delivers. Here's what production AI looks like versus a demo.
A proof of concept
- Runs on sample or cleaned data, not real production data
- Deployed to a notebook or local environment
- Breaks on edge cases that weren't anticipated in the demo
- No monitoring, no alerting, no error handling
- Not documented: only the consultant knows how it works
- Requires the consultant to maintain or extend it
Production AI
- Runs on your real data infrastructure, with real edge cases handled
- Deployed to your environment (not a demo sandbox)
- Monitored and alerting when something goes wrong
- Error handling and fallbacks built into the system
- Documented with runbooks your team can follow
- Your team owns it after the handoff
Two fixed-scope production AI engagements
Every MLDeep engagement has a fixed scope and a 2-week delivery window, with the price scoped to your use-case on the fit call. No open-ended retainers, no hourly billing, no "let's discover together."
Fixed scope · 1-2 weeks · priced on the fit call
RapidOps Automation Sprint
You have one manual workflow that's costing your team 3-10 hours per week. We scope it, build it, deploy it, and hand it off documented. Delivered in 1-2 weeks.
Best for: founders and operators who need one specific workflow automated now. Reporting, routing, enrichment, data briefs.
Explore RapidOps →
Fixed scope · 2 weeks · priced on the fit call
AI Stack Audit
You need a clear verdict on whether your data stack can support production AI, before you commit a quarter of engineering time to an initiative that might stall.
Best for: data teams and analytics leads at Series A-B companies with AI initiatives that need a foundation assessment first.
Explore the AI Stack Audit →What production AI delivery looks like in practice
Reporting automation
Automated reporting agents
A Series A client automated 3 hours per week of Monday morning reporting: pulling from Stripe, HubSpot, and spreadsheets into a Slack brief. Deployed in 5 days. Still running.
Data infrastructure
Data foundations for agents
A Series B client got dbt + Terraform + CI/CD built from scratch. Their data team unblocked two AI agent deployments within 3 months. The foundation made the agents possible. Without it, those agents would not have shipped.
AI readiness
AI readiness assessments
Data teams that aren't sure whether their stack is ready for production AI get a scored verdict (not a questionnaire, but an actual assessment of their systems), with a sequenced roadmap for what to fix first.
Why 95% of AI pilots never ship to production
The problem is almost never the model. It's the stack underneath it.
AI agents that run in demos read from clean sample data. They run in sandboxes where environment configuration is controlled. They don't hit the inconsistencies, staleness, and undocumented edge cases that exist in every real data stack.
When those demos get handed off to the engineering team with instructions to "put it in production," the real work starts, and it's usually work the consulting firm didn't scope and won't help with. The agent breaks. The data quality issues surface. The consultant has already moved to the next client.
Production AI consulting means the scope ends at a deployed system, not at a presentation of a system that could be deployed if the data were cleaner and the infrastructure were more reliable.
Common questions about production AI consulting
What is production AI consulting?
Production AI consulting is consulting that ends with a deployed, working AI system, not a proof of concept, a presentation, or a strategy roadmap. It means the consultant builds what they scope, deploys it to your environment, documents it for your team, and leaves you with something that runs in production. MLDeep delivers production AI through two fixed-scope engagements: the RapidOps automation sprint and the AI Stack Audit, each priced to your use-case on the fit call.
What is the difference between a POC and production AI?
A proof of concept (POC) runs in a controlled demo environment, uses clean sample data, and is not maintained after the demo. Production AI runs on your real data infrastructure, handles edge cases and failures, has monitoring and alerting, is documented so your team can maintain it, and is deployed to a real environment that real users depend on. 95% of AI pilots in 2025 never made it from POC to production.
How much does production AI consulting cost?
MLDeep offers two fixed-scope production AI consulting engagements: the RapidOps automation sprint (1-2 weeks, one workflow scoped and deployed) and the AI Stack Audit (2 weeks, scored readiness assessment with 90-day roadmap). Both are fixed-scope, with the price scoped to your use-case and region on the fit call.
Tell us what you need to move from POC to production. We will scope it honestly.
A short working conversation about the problem, the available data, and whether there is a credible next step.