Integrate AI into your existing stack without rewriting everything
Senior advisory + hands-on implementation for companies with working products that need to add AI intelligently. No pilot paralysis, no over-engineering — just AI that delivers measurable value integrated with what you already have.
- Audit your stack in week 1, roadmap in week 2
- Ship a real pilot in 4–6 weeks
- Train your team so you own it long-term
Who I consult for
I work with companies that have a real product and real users — and need a senior AI partner who's been through the full lifecycle, not a pitch deck consultancy.
Product companies adding AI features
You have a SaaS doing $500K–$50M ARR and your board is asking about AI. You need someone who can tell you what to actually build vs what to ignore.
CTOs with no LLM experience in-house
Your engineering team is excellent but hasn't shipped LLM products. You need a senior voice in architecture review + a hands-on engineer to ramp the team.
Enterprise innovation teams
You need to pilot AI in a way that maps to your security, compliance, and procurement constraints. You need someone who understands both sides.
Operators with wedge domain expertise
You know an industry deeply and see an obvious AI play — you need a technical co-founder-level partner to pressure-test and execute.
Why most AI integrations fail
The average AI integration project I'm asked to rescue has one of three root causes: pilot paralysis (12 months of prototypes, nothing in production), wrong-tool syndrome (chose a vector DB when a cache would've worked, or vice versa), or misaligned architecture (AI bolted onto a monolith in a way that makes every change painful).
The companies that succeed with AI integration aren't the ones with the biggest AI team — they're the ones who pick the right first project, design for reality (cost, latency, observability, compliance), ship something real in 6–8 weeks, and iterate based on actual usage.
My role is to compress that timeline. I've already lived the mistakes — vendor lock-in, overconfident models, prompt drift, cost explosions, misaligned agents — and I bring that pattern-matching into your project from day one.
How I approach AI integration
Every engagement starts with understanding the business, not the tech. The AI is just a means to an end.
Audit first, ship second
Week 1–2 is always understanding what you have: current architecture, data, team capability, compliance constraints, and business priorities. Only then do I recommend what to build and in what order.
Pick the right first project
The wrong first AI project burns trust and budget. I help pick the project with the best ROI-to-risk ratio — meaningful impact, scoped clearly, measurable result in 6–8 weeks.
Design for your stack
AI integration means fitting AI into your existing architecture, tools, and workflows — not forcing a greenfield rewrite. I work with your languages, your deploy patterns, your monitoring stack.
Compliance-aware
HIPAA, SOC 2, GDPR — if your business has compliance requirements, we design the integration around them: private endpoints, data residency, audit trails, PII handling. Not retrofitted later.
Knowledge transfer built in
Every engagement includes documentation, architecture decision records, and walkthrough sessions with your team. When I leave, your team can extend and maintain what we built.
Vendor-agnostic perspective
I don't have partnerships with AI vendors, so my recommendations are based on what's best for your use case — not what kicks back. OpenAI, Anthropic, open models, Azure, AWS Bedrock, local inference — all considered.
Engagement structure
Flexible to your needs — typical engagements fall into one of three patterns.
AI Audit (2 weeks)
Review your current stack, team, data, and goals. Deliverable: a written AI roadmap with recommended projects, architecture, cost projection, and capability gaps. Starts at $5,000.
Advisory Retainer
Monthly engagement: architecture review, code review of AI work, team coaching, executive briefings, vendor selection. $4,000–$8,000/month depending on hours.
Pilot Build
Scoped 6–10 week engagement to ship the first AI integration into production. Fixed-price. Includes knowledge transfer and documentation so your team owns it after.
Embedded CTO
Part-time (typically 2 days/week) fractional CTO-level engagement for companies building an AI product line. 3–6 month minimum.
What you get
Investment
Engagements shaped to your needs — audit, advisory, build, or embedded.
AI Audit
$5,000 – $12,000
2-week engagement. Deliverable is a written roadmap: what to build, in what order, how, and what it'll cost. Most clients extend into a pilot build after.
- Stack and team audit
- Use case prioritization
- Architecture recommendations
- Cost + timeline projections
- Written roadmap document
- Readout + Q&A session
Advisory Retainer
$4,000 – $8,000/month
Ongoing senior advisor engagement. Architecture review, code review of AI work, team coaching, vendor selection, executive briefings.
- Weekly 1-hour calls
- Architecture reviews
- Code reviews on AI work
- Vendor evaluation help
- Executive briefings as needed
Pilot Build
$15,000 – $45,000
6–10 weeks. Ship the first real AI integration into your production stack. Handover and training included.
- Scoped production integration
- Architecture + code
- Observability setup
- Team training sessions
- Full documentation
- 30-day post-launch support
Embedded CTO
$10,000 – $18,000/month
Part-time fractional CTO for companies building an AI product line. Typically 2 days/week, 3-month minimum.
- Dedicated weekly capacity
- Architecture ownership
- Team mentorship
- Strategic planning
- Hands-on implementation when needed
Tech Stack
Working With Clients Across
Frequently asked
We're already working with an AI vendor. Can you plug in?
Yes. I regularly complement in-house or vendor work — I don't need to own the whole stack to add value. I often come in as a technical advisor or second set of senior eyes on architecture and decisions.
Do you handle compliance (HIPAA, SOC 2, GDPR)?
I've designed integrations for companies with all three constraints. Specific patterns (private endpoints, BYOK, EU-only data residency, audit logging) are baked into the architecture from day one, not retrofitted.
What if we want to use open-source models?
Great — open models (Llama, Mistral, Qwen) often make sense for specific use cases: cost, privacy, or latency. I can design hybrid architectures that route between hosted and open models based on the request.
Can you work with our existing dev team?
That's most of what I do. I bring senior AI engineering expertise to teams that need it temporarily. I work through your team's tools (Linear, Jira, GitHub, Slack), review their code, and pair on the hard problems.
How quickly can you start?
Audit engagements can usually start within 1–2 weeks. Build engagements depend on current capacity — typically 2–4 weeks out. Advisory retainers can start faster.
Do you sign standard enterprise agreements?
Yes. I'm comfortable with MSAs, NDAs, SOWs, and standard vendor onboarding. For enterprise engagements I can work under your procurement process or through an intermediary entity.
Ready to start?
Every engagement starts with a 30-minute discovery call. I'll listen, ask sharp questions, and send a written proposal within 48 hours.
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