Services

AI services for teams without AI departments

You do not need a massive monthly subscription stack to get value from AI. You need the right workflow, clean inputs, sensible guardrails, and a system your team can operate after launch.

Good starting points

The best first project is usually a workflow your team already repeats every week. It should have a clear owner, a clear before-and-after, and enough volume that automation pays for itself.

A contact form that sends the prospect a polished confirmation, notifies your team, enriches the lead, and creates a follow-up task.
A competitor newsletter digest that reads the market every morning and tells leadership what changed.
Survey sentiment analysis that groups open-ended feedback into themes and suggests practical mitigation steps for managers.
ETL cleanup that replaces fragile spreadsheet handoffs with repeatable pipelines and data quality checks.
Resume scoring that compares applicant backgrounds to successful agent profiles while keeping a human in the hiring decision.
Territory planning that combines drive-time maps, Census data, and market context to choose better agency locations.
A private company GPT that answers internal questions from approved docs with source links.
A workflow that uses rules for 80% of the process and AI for the 20% that needs summarizing, drafting, or classification.
ai / automation

AI workflow automation

Most smaller companies do not need a giant AI platform. They need a few workflows that quietly save time every week. I build automations that capture leads, draft follow-ups, summarize inbound messages, route work to the right person, and keep a human approval step wherever judgment matters.

What you get

  • Website lead capture with automated confirmation and internal alerts
  • Email and CRM workflows that keep prospects warm without manual chasing
  • Approval queues for drafts, summaries, and customer-facing messages
  • Cost-aware routing so simple rules run before expensive AI calls
  • Monitoring, error handling, and handoff notes your team can understand

Typical stack

n8n Windmill Python PostgreSQL OpenAI Claude Vercel
competitive / intelligence

Competitive intelligence digests

Your competitors are already telling the market what they care about. Newsletters, blog posts, product pages, webinars, filings, and social posts leave a trail. I build digest systems that collect those signals on a schedule and turn them into a short read your team can actually use.

What you get

  • Daily, weekly, or monthly competitor newsletter digests
  • Theme tracking across products, pricing, messaging, and market claims
  • Executive summaries with links back to the original source material
  • Trend logs so you can see how competitor positioning changes over time
  • Delivery by email, dashboard, Slack, Teams, or internal knowledge base

Typical stack

Python RSS Email APIs Playwright PostgreSQL LLMs Dashboards
private / gpts

Private company GPTs

A private company GPT should feel like a trained internal teammate, not a blank chatbot. I build assistants that know your documents, policies, products, historical decisions, and preferred voice. They can answer internal questions, draft first passes, and help teams find the right source without exposing everything to a generic tool.

What you get

  • Company-specific GPTs for sales, operations, finance, or leadership teams
  • Private document search with citations and source links
  • Role-based prompts, guardrails, and answer formats
  • Usage tracking so you know what people ask and where knowledge gaps exist
  • Admin documentation for updating content and controlling access

Typical stack

OpenAI Claude pgvector PostgreSQL LiteLLM RAG Internal docs
data / process / foundations

Data and process foundations

AI works better when the inputs are clean and the repeatable logic is boring. I help teams replace spreadsheet glue, manual exports, and brittle reporting with scripts, pipelines, dashboards, and alerts. Then we add AI only where it improves the workflow.

What you get

  • Source system integrations and cleaned reporting tables
  • Deterministic scripts for repeatable calculations and transformations
  • Dashboards, scheduled reports, and exception alerts
  • Data quality checks before AI sees the data
  • Runbooks and training so the system is not a black box

Typical stack

SQL Python dbt PostgreSQL Snowflake Power BI Tableau
Have a workflow in mind?

Start with one useful AI system

If your team is manually chasing leads, reading competitor emails, answering the same internal questions, or copying data between systems, that is a good place to start.

Want examples? Drop your email and I will send practical AI workflow ideas for smaller companies.