Services

AI Consulting & Implementation Services

Most AI projects need some combination of these three things. Pick what you need, skip what you don't.

01

AI Strategy

Figure out where AI actually makes sense for your business, and where it doesn't. No hype, just honest analysis.

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What you get

  • Audit of your current operations and data
  • Ranked list of AI opportunities by impact vs. effort
  • Architecture that fits your existing stack
  • Realistic cost and timeline estimates
  • The "don't do this" list (often more valuable)

Key outcomes

  • Know exactly where to start
  • A plan your engineers can actually execute
  • Budget numbers you can defend to leadership
  • Avoided the "build a chatbot" trap
02

AI Agents & Automation

Multi-agent systems that handle real workflows, not toys that break when users do something unexpected.

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What you get

  • Agent architectures that handle edge cases
  • LLM apps with proper guardrails and fallbacks
  • RAG systems that actually find relevant context
  • Integrations with your existing tools
  • Monitoring so you know when things go wrong

Key outcomes

  • Workflows that run without babysitting
  • Hours back for your team each week
  • Systems that get better with feedback
  • Fewer "the AI said what?" incidents
03

Data & ML Engineering

Turn messy data into models that work, and models into systems that keep working six months later.

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What you get

  • Data pipelines that don't break on Mondays
  • Models trained on your actual use cases
  • Feature stores you can reuse across projects
  • Dashboards people actually look at
  • Documentation your future self will thank you for

Key outcomes

  • Predictions you can trust enough to act on
  • One source of truth instead of five spreadsheets
  • ML that improves automatically over time
  • Data team that ships instead of firefights
The Process

How engagements work

01

Understand

02

Find the lever

03

Build small

04

Scale

I start small, prove it works, then expand. No multi-month discovery phases. No building in isolation. Working software, early and often.

Questions

Things you're probably asking

The stuff most consultants make you get on a call to find out.

It depends on scope: a focused strategy engagement costs a fraction of a full production build. After one conversation you'll have a clear, fixed estimate before you commit to anything. No open-ended billing.

It depends on scope, but most projects follow this pattern: 2-4 weeks for strategy and scoping, 4-8 weeks for a working proof of concept, then ongoing iteration. I don't do 6-month discovery phases. You'll see working software early.

Both, but with different approaches. Startups usually need focused MVPs that prove a concept. Enterprises need solutions that integrate with existing systems and pass security review. I adjust the process accordingly.

Great. I work alongside them, not around them. I can lead the AI-specific work while your team handles integration, or advise while they build. The goal is to leave your team more capable, not dependent on me.

I'm pragmatic, not dogmatic. For LLM work: OpenAI, Anthropic, Gemini, or open-source models depending on your requirements. For agents: Google ADK, LangGraph, CrewAI, or custom architectures. For data: Python ecosystem, dbt, modern warehouses. I pick what fits your constraints.

I'll tell you. If a simpler approach solves your problem, that's what I'll recommend. I'd rather have a short honest conversation than a long expensive project.

Not sure which service you need?

That's fine. Most projects touch all three. Let's figure out what makes sense for your situation.

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