AI that does real work in your systems
Most AI demos die on contact with a real business. The model answers a chat window. Your team still copies the result into HubSpot by hand. I build the other kind of AI: wired into the tools you already run, with logging, fallbacks, and a clear job to do.
If you want a research lab or a six-month "AI transformation program," this is not me. If you want ChatGPT or Claude extracting invoices, triaging tickets, or drafting inside a portal your staff already use, that is the work.
Who this is for
Ops and support teams drowning in PDFs, email threads, and form submissions that follow a pattern a model can handle.
Product and portal owners who need one intelligent feature, not a new company org chart.
Teams mid-experiment who tried a plugin chatbot, got noisy answers, and need reliability before customers or staff trust it.
Who this is not for
Companies shopping for "an AI strategy deck." I ship software.
Regulated bets that need a dedicated ML research staff and multi-year model training. Buy a specialized vendor or hire full-time specialists.
Anyone who wants AI instead of fixing a broken process. Automating chaos just makes chaos faster. Fix the workflow first; then add models where they save real hours. For the non-AI side of that cleanup, see how workflow automation reduces manual work.
What I actually integrate
Support and chat that escalate. Bots for common questions, with confidence thresholds and a path to a human. No black hole of fake helpfulness.
Document processing. Pull fields from invoices, applications, contracts, and forms. Summarize, classify, route. Your team stops retyping what the PDF already said.
CRM and portal helpers. Draft replies, suggest next steps, score leads, surface account context inside HubSpot or a custom portal. See also HubSpot integration.
Search that understands intent. Semantic search over your docs and knowledge base when keyword search fails.
Workflow steps, not magic. Pair models with rules and human review. Larger process work lives under workflow automation. When the real pain is still spreadsheet re-entry, start with when spreadsheets become a business liability.
How I work (and what it costs)
I start with the expensive problem: hours wasted, tickets stuck, revenue friction. Then a thin proof of concept in about 1–3 weeks so you can judge quality before a full build. Production means auth, logging, rate limits, cost controls, and a fallback when the model is wrong.
AI features for SMBs usually sit inside a broader project in the same ranges I publish for portals and software: often a slice of a $30K–$100K build, not a separate "AI tax." API usage (OpenAI, Anthropic, and the rest) is billed on your accounts so you see spend. For how those project numbers break down in practice, read understanding custom software costs.
Local, hands-on AI consulting with Pacific-hours support is the sister page: AI Specialist Seattle.
Next step
Bring the messy workflow and the systems it touches. Schedule a consultation. I will tell you whether a model earns its keep, what off-the-shelf already covers, and what a fixed-price slice should cost.
