Production AI consulting.
Not another proof of concept.
95% of AI pilots never made it to production in 2025. The gap between a working demo and a deployed system is where most consulting firms disappear. MLDeep builds what it scopes, deploys it to your environment, and hands it off documented.
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 published price, a fixed scope, and a 2-week delivery window. No open-ended retainers, no hourly billing, no "let's discover together."
RapidOps Automation Sprint
$5,000 -- $8,000 · 1-2 weeks
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 -- in 1-2 weeks.
Best for: founders and operators who need one specific workflow automated now. Reporting, routing, enrichment, data briefs.
AI Stack Audit
$15,000 · 2 weeks
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.
What production AI delivery looks like in practice
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 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 -- the agents wouldn't have shipped without it.
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.