Service
AI Solutions & Integrations
Deploy AI that actually does something.
Scale throughput and consistency without proportionally growing headcount
From customer-facing assistants to internal flows that read, route, and act on your data — I design and build AI-backed systems that tie into the tools you already use. The goal is measurable: fewer manual handoffs, faster responses, and outcomes you can review in numbers — not a slide-deck demo that nobody runs in production.
Work is scoped in slices: prove value on a narrow path first, then widen. That might be a support copilot grounded in your help center, a qualification flow that updates the CRM, or document intake that routes to the right owner. Integrations are explicit—no “black box” that your team cannot operate. When you also need durable automation between systems, this service lines up with workflow automation; when public answers must stay citable, it lines up with AEO on your site.
What this work is (and is not)
This is applied AI inside your operations: APIs, retrieval, guardrails, and handoff to humans or downstream systems. It is not generic “AI training” for its own sake, a one-off ChatGPT wrapper with no ownership, or an autonomous agent that takes irreversible actions without checks you sign off on.
If the outcome cannot be stated as a metric, a workflow, or a risk boundary, we clarify that first—so effort goes into what your business can run and measure, not experiments that stall after the kickoff.
How builds are typically structured
Discovery aligns on goals, data sources, approval rules, and where AI should stop. A first slice ships to real users or a pilot group with logging and simple evaluation. Then we iterate: better retrieval, tighter prompts or tool policies, and integration hardening. Production gets monitoring, fallbacks, and documentation your team can use without a constant engineering seat next to them.
Vendor and model choices stay flexible: the architecture should survive a model upgrade or a regional data requirement without a full rewrite.
Fit and honest boundaries
A strong fit: you can name an owner for the workflow, provide access to content or systems the AI must use, and accept that quality comes from iteration with real traffic—not a single launch day. Also a fit when you are outgrowing ad hoc scripts or a single overloaded inbox for triage.
A poor fit: no willingness to define data rules, or a mandate for full autonomy over money or safety-critical actions with no human checkpoint. In those cases we either scope something narrower or I will say so directly.
What you get
- Grounded answers from your content (RAG) with clear limits and fallbacks
- Tool-calling and webhooks into CRM, helpdesk, email, and custom APIs
- Model-agnostic integration: OpenAI, Claude, Gemini — chosen for fit, not fashion
- Document and ticket triage pipelines with human-in-the-loop where risk requires it
Common questions
Straight answers for this service—before you book a call.