Founder-led AI adoption studio
I build the AI workflows your team can actually use: automations, MCP servers, assistant skills, and rollout plans that survive contact with real work. In short: working AI systems, not slide decks.
David advises, builds, tests, and hands over the working system.
Assistants connect to the tools, data, and permissions your team already uses.
Strategy is tied to adoption, documentation, training, and ownership.
My work starts with a real process: support triage, project handoff, reporting, research, proposal drafting, internal search, engineering review, or whatever is slowing the team down. Then I build the system that removes steps, routes exceptions, and gives people a better way to work.
Services
The language stays plain for decision-makers. The implementation is deep enough for technical teams.
Selected service
End-to-end automations for the steps people repeat every week: intake, routing, summarization, drafting, reporting, approvals, follow-up, and handoff.
Builder and advisor
I can sit with leadership and decide where AI fits in the roadmap. I can also sit with the team, write the MCP server, tune the assistant skill, wire the workflow, and show people how to use it.
That matters because most AI projects die in the gap between "good idea" and "someone owns this every Thursday at 09:00." AIAdapt Solutions closes that gap.
Model judgment
Claude and ChatGPT are the daily drivers: design work, collaborative work, Codex, code workflows, agent patterns, and internal assistant design. Gemini and Grok are familiar terrain. Open-source models like KIMI, Qwen, and others stay in the evaluation set when privacy, cost, latency, or local control matter.
Example builds
These are sample engagement blueprints, not claimed client results. They show the kind of systems AIAdapt Solutions can ship first.
Incoming requests are classified, enriched with account context, drafted into the right response, routed for approval, and logged back into the team's system.
A custom MCP server lets approved assistants search company docs, read project state, create tickets, and run safe internal actions from one controlled interface.
A package of skills, prompts, review rules, and workflows that turns scattered AI usage into a repeatable operating habit for a sales, product, support, or engineering team.
Process
Map the process, tools, permissions, data sources, and failure points. Pick the first useful build.
Build a narrow version quickly so the team can judge behavior against actual work.
Add tests, guardrails, docs, monitoring notes, and clean handoff paths.
Train the users, refine the workflow, and leave the team with ownership instead of dependency.
Speaking
For company events, leadership days, and team sessions, I give practical talks on how AI accelerates real processes. The session can include live workflow teardown, model comparisons, examples of MCP and assistant skills, and a clear path for what the audience should try next.
Where AI belongs in the roadmap, where it is a distraction, and how to sequence adoption.
Hands-on patterns for using assistants inside daily work without lowering standards.
MCP, skills, plugins, and automations explained through a working example.
Reach David
A good first conversation is specific. Tell me which process is slow, which tools are involved, and what the team has already tried. I will come back with the clearest next step.
FAQ
No. The first useful step is usually a workflow audit. If the best answer is "do not build yet," I will say that.
The client should own the code, prompts, tool definitions, deployment notes, and runbooks. I build so the team can keep using the system without a black box.
Yes, with the right boundaries. MCP servers, assistant tools, and automations should respect authentication, permissions, audit needs, and data handling rules from the start.
Both, in sequence. The business case decides what should be built. The technical work proves whether it can be made reliable.