Your AI initiative is stalled.
The data foundation under it isn't ready.
You have the team, the mandate, and a list of AI initiatives. Every spike stalls at the same point -- the data layer underneath isn't production-ready. I build the foundation and unblock the agents. In weeks, not quarters.
$15,000 AI Stack Audit · 2-week verdict · Scored across 5 dimensions · 90-day roadmap
source systems unified into a single clean data layer
AI agents in production within 3 months of engagement
to build the dbt + Terraform + BigQuery foundation
Series B SaaS · ~80 people · Head of data, AI initiative stalled on the foundation
You're not behind on AI. Your data layer is.
Your AI initiative is on the roadmap. Leadership is asking questions. Your team has the capability. Every sprint stalls at the same point -- agents failing on inconsistent data, no single source of truth, eight source systems that don't agree with each other.
Every failed POC costs credibility with the engineering org and leadership. The question has shifted from "should we do AI" to "why can't we ship anything?" That's a data foundation problem -- not an AI capability problem.
The pattern I see most often
- Data scattered across Salesforce, Snowflake, Google Sheets, and 5 other tools with no unified layer
- No tested transformation layer -- reports disagree depending on who ran them
- AI agents that work in notebooks but fail on real production data
- No CI/CD for data -- every deploy is a manual process and a coordination risk
- POCs that never shipped because the underlying data wasn't trustworthy
Where do you need to start?
Most engagements begin with the AI Stack Audit. Some teams already have the verdict and need the build. Both are scoped and priced upfront -- no open-ended retainers.
Not sure if your stack is ready for AI?
A scored assessment across 5 dimensions -- data quality, infrastructure, transformation layer, observability, and AI readiness. You get a clear verdict and a prioritized 90-day roadmap. Not a slide deck. A decision document your team can act on.
- Scored verdict across 5 dimensions
- Prioritized 90-day action list with specific tasks and owners
- Fixed price, fixed 2-week scope
- Will tell you honestly if you're already AI-ready
Already know the gap -- need it built?
If you know what's broken, I scope the build and deliver it. dbt transformation layer, Terraform infrastructure, BigQuery warehouse, CI/CD pipelines -- the full foundation your AI agents need to run on clean data in production.
- dbt + Terraform + BigQuery, production-grade
- CI/CD for data -- tested on every deploy
- Full documentation and runbooks on handoff
- Fixed scope, fixed price agreed before kickoff
One specialist. Three reasons to trust the delivery.
Domain depth
9 years. Full stack. dbt Labs partner.
Data engineering, cloud infrastructure (Terraform, BigQuery), and production AI agents -- all in one practitioner. No hand-offs between the person who designed it and the person building it. Published research: AgentDoctor.
Buyer specificity
Built for your exact situation.
Series B/C SaaS, 2-5 person data team, AI initiative stalled on the foundation. Not a general-purpose consultancy. The specific gap between "we need AI" and "we have the stack for it" is the only thing I do.
Proof on record
Production, not POC.
8 source systems unified. 2 AI agents in production within 3 months. Foundation built on Terraform + dbt + BigQuery with CI/CD. The case study above is in the client's production repo -- not a demo environment.
"Will this just be another strategy deck?"
The most common objection from heads of data who've been burned by consultants before. Let me be direct about what the deliverables actually are.
AI Stack Audit deliverable
- Scored assessment across 5 dimensions -- not a gut-feel summary
- Prioritized 90-day action list with specific tasks, owners, and tools
- Honest verdict: I will tell you if you're already AI-ready and don't need more work
- A document your team can act on -- not a vendor pitch for phase 2
Foundation build deliverable
- Working dbt + Terraform code in your repo, tested and documented
- CI/CD pipeline your team can run and extend without me
- Runbooks so your team owns it after handoff -- no dependency created
- Fixed price agreed before kickoff -- no scope creep, no surprise invoices
Who this is for -- and who it isn't.
Good fit
- Series B or C, 2-5 person data team
- AI initiative stalled because the foundation isn't production-ready
- Head of data or data engineering manager with budget authority
- Need a verdict or a build in weeks -- not a 6-month engagement
- Want working code and documentation, not a presentation
Not a fit
- Pure BI or dashboarding with no AI component
- Looking for open-ended staff augmentation
- Pre-product or pre-revenue (no real data to work with yet)
- Need a named firm on an RFP response
- Expecting AI to solve what is really a process or people problem
15 minutes. No commitment.
Tell me where your AI initiative is stalled. I'll tell you whether it's a data problem, an infrastructure problem, or something else -- before any money changes hands. If it's not a fit, I'll say so.
AI Stack Audit: $15,000 fixed price · 2-week delivery · Scored verdict + 90-day roadmap