Why MLDeep?
The senior practitioner IS the only practitioner. No hand-offs, no junior surprises, no bait-and-switch.
MLDeep vs. big consulting firms
Fixed Price, Not Hourly Billing
$15K diagnostic, fixed scope. Not hourly billing that balloons into six figures before you see a deliverable.
2 Weeks, Not 6 Months
You get a scored AI readiness assessment in 2 weeks. Not a 6-month engagement cycle that ends with a slide deck.
Direct Access to the Expert
The person who scoped your project is the person who delivers it. No partner-to-analyst hand-off. No telephone game.
Outcomes, Not Overhead
No inflated scope, no travel expenses, no "team onboarding" fees. You pay for deliverables, not a consulting firm's overhead structure.
Clear MSA and SOW, No Surprises
Every engagement starts with a straightforward Master Service Agreement and Statement of Work. No ambiguous terms, no surprise change orders, no 40-page contracts designed to confuse.
MLDeep vs. freelancers
One Person, Full Stack
Data engineering, cloud infrastructure, AI agents, and analytics dashboards. One practitioner spanning the entire stack, not a specialist who can only do one piece.
Fixed Scope, Clear Deliverables
Every engagement has a defined scope, timeline, and set of deliverables. Not time-and-materials where the meter runs indefinitely.
Credentials That Matter
dbt Labs Technology Partner. Published research (AgentDoctor). Not just a LinkedIn profile with "AI Expert" in the headline.
Enterprise-Grade Delivery
CI/CD pipelines, automated testing, security scanning, monitoring. Production-grade infrastructure -- not "it works on my laptop."
Professional MSA and SOW
Every engagement comes with a Master Service Agreement and Statement of Work. Fixed scope, clear deliverables, IP ownership clauses, and defined success criteria. Not a handshake and an invoice.
MLDeep vs. in-house hire
$15K vs. $150K+ Salary
Start for $15K and get a scored assessment in 2 weeks. Compare that to a 6-month hiring process plus a $150K+ annual salary plus benefits.
Zero Ramp-Up Time
No benefits, no management overhead, no 3-month onboarding period. Productive from day one because this is what I do every engagement.
Battle-Tested Across Engagements
Patterns refined across multiple companies and industries. An in-house hire learns on your dime. I bring patterns that already work.
Walk Away With Deliverables
You get documentation, runbooks, and code your team can maintain. No vendor lock-in, no consultant dependency. Your team owns it after I leave.
MLDeep vs. boutique data consultancies
Fixed Pricing, Not "Contact Us"
Most consultancies hide pricing behind a "schedule a call" button. MLDeep publishes $5,000-$8,000 automation sprints and $15,000 diagnostics right on the website. No sales pitch required to learn what things cost.
Published Research, Not Just Credentials
MLDeep publishes original research papers and open-source tools like AgentDoctor. Other consultancies list partner badges. I publish the work that earned them.
No Minimum Commitments
Some fractional data firms require 3-month minimums at $5,500+/month. MLDeep offers single-sprint engagements from $5,000. Try before you commit.
Knowledge Transfer, Not Dependency
Every engagement includes documentation, runbooks, and code your team can maintain. The goal is to make your team self-sufficient, not to create a recurring billing relationship.
Right-Sized Stack, Not Tool Religion
No preferred vendor. No certification wall dictating your architecture. The stack fits your stage and your data. BigQuery free tier for early-stage, Snowflake for scale, Databricks for ML-heavy workloads.
The MLDeep guarantees
The Scope Lock
Your price is your price. If scope changes during the engagement, I re-scope and re-price before continuing. No surprise invoices, no change orders you didn't approve.
Knowledge Transfer Promise
Every engagement includes runbooks your team can follow. If your team can't maintain what I built after the handoff, I update the documentation at no charge.
Honest Assessment
If the diagnostic shows you're already AI-ready, I'll tell you. If you don't need consulting, I'll say so. No manufactured problems to justify more engagements.
One person, full stack
Most consultancies use the solo practitioner model as a limitation to apologize for. At MLDeep, it's the entire point.
When one person spans data engineering, cloud infrastructure, AI agents, and analytics dashboards, there are no hand-offs. No context lost between teams. No "that's not my department." The person who understands your data warehouse is the same person who builds your AI agent and deploys it to production.
For engagements that need additional capacity, I partner with Domain Methods, a delivery partner with complementary expertise. You still get a single point of accountability. The work still ships on time. But the ceiling is higher when you need it.
Data Engineering
dbt implementation, data modeling, pipeline orchestration, data quality testing. The foundation that makes everything else possible.
Cloud Infrastructure
Terraform, CI/CD, cloud governance, security hardening. Infrastructure as code, not infrastructure as guesswork.
AI Agents
Design, build, audit, and deploy production-grade AI agents. From architecture to monitoring, one practitioner end-to-end.
Ready to see if your data is AI-ready?
Start with a free 30-minute discovery call or take the AI Readiness Assessment to get your score.