Your data is already in SQL Server.
Your AI features should be too.
SQL Server 2025 ships with native vector search, DiskANN indexes, and embedding integration. For most organizations already running SQL Server, that means you can add semantic search, RAG, and AI-powered features without adding another database to your stack.
The hard part isn’t the technology. It’s knowing whether SQL Server can handle your specific use case, how to architect it correctly, and how to get your team ready to build and support it. That’s what these services are for.
Evaluate: AI Readiness Assessment
Flat fee · Results in one week
Not sure whether SQL Server 2025 can handle your AI use case? Don’t spend months building competing POCs to find out.
The AI Readiness Assessment gives you a definitive answer: can SQL Server handle it, what’s the architecture, and what does your team need to learn. If SQL Server isn’t the right answer, I’ll tell you that too — along with what to use instead and what it’ll actually cost.
What’s Included
Discovery Call (90 minutes): We dig into your actual use case. What data do you have, what searches do your users need, what does your current SQL Server estate look like, and what are your real constraints — timeline, budget, compliance, team skills.
Environment Review: I look at your SQL Server infrastructure: versions, editions, hardware, existing workload profile. The goal is to understand whether your current environment has headroom for vector search workloads, or whether you’re already pushing limits on the traditional side.
Recommendation Document: A written assessment that answers three questions.
Can SQL Server 2025 handle this use case? Not “maybe” or “it depends.” A specific technical answer with rationale. If yes: proposed architecture, embedding strategy, index approach, hybrid search design, estimated storage and compute impact, and an implementation roadmap.
If SQL Server isn’t enough, what is? When the workload genuinely needs a dedicated vector database, I tell you that — along with what to evaluate, how to integrate with existing SQL Server data, and the total cost picture including sync pipelines, operational overhead, and added security surface.
What does your team need to learn? A skills gap assessment: what your DBAs need to know about vector workloads, what your developers need to know about the database layer, and specific resources to get there.
Follow-Up Call (60 minutes): We walk through the recommendation together. You ask questions. I explain tradeoffs. You leave with a decision you can defend to leadership, not a slide deck that raises more questions than it answers.
What This Replaces
Without this assessment, teams typically spend 2-4 months in evaluation limbo: building competing POCs, sitting through vendor demos, arguing about architecture in meetings where nobody has the data to win the argument. This compresses that into a week.
Train: AI Team Workshop
Day rate · On-site or remote · Half-day and full-day options
One person buying a course is a start. Getting the whole team aligned on how vector search, embeddings, and AI workloads work in SQL Server — that’s what moves an organization forward.
This is a focused workshop, delivered live over 4-8 hours, built around your team’s actual environment and use cases. Not generic slides — every demo uses examples relevant to your data and workloads.
What We Cover
The core material comes from my Get AI-Ready With Erik course: vector data types, embedding generation, similarity search, hybrid search, DiskANN indexes, production patterns, and RAG architecture. But instead of pre-recorded videos, your team gets live instruction with real-time Q&A, tailored to their specific environment.
Format Options
Half Day (4 hours): Covers foundations, vector search, and hybrid search. Best for teams that need to understand the technology and make architecture decisions.
Full Day (8 hours): Adds DiskANN deep dive, embedding pipeline design, production patterns, and hands-on exercises. Best for teams that will be implementing vector search.
What’s Included
- Live instruction (on-site or remote via Teams/Zoom)
- Pre-workshop environment review and customized demos
- Course access for all attendees (Get AI-Ready With Erik)
- 30-day follow-up email support for implementation questions
Implement: Vector Search Consulting
Prepaid hours · Same model as all consulting engagements
Already know what you’re building? I work hands-on with teams implementing vector search on SQL Server 2025. Same prepaid hour model I’ve used for 600+ clients — buy a block of hours, we get started within days.
Common engagements include:
- Vector search architecture review and design
- DiskANN index tuning and performance optimization
- Hybrid search implementation (vector + full-text)
- Embedding pipeline design: batch processing, incremental updates, error handling
- Migration planning to or from dedicated vector databases
- Production monitoring and ongoing performance tuning
This is the same consulting practice I’ve run for years — just extended into AI workloads. If your SQL Server is slow and it involves vectors, I’ll make it faster.
Learn: Self-Paced Course
$500 · Immediate access · 28 videos
Prefer to learn at your own pace? The Get AI-Ready With Erik course covers everything from vector data types and embeddings through hybrid search, DiskANN indexes, production patterns, and RAG architecture. Built on the StackOverflow demo database so you can follow along with real data at real scale.
Monitor: Free Performance Monitoring
Free and open source · Built-in MCP server
My free SQL Server monitoring tool includes a built-in MCP server for AI-powered performance analysis. Real-time diagnostics, email alerts, and AI-assisted troubleshooting — made by someone who does performance tuning for a living.
Not sure where to start?
Tell me what you’re trying to build and I’ll point you in the right direction.