Home AI services
From the first assessment to a system running in production and monitored every month. Every engagement starts with a business question and ends with a number that shows whether it worked.
Decide where AI will pay off before you spend on it.
A fixed-price review of your workflows, data and risks that ends with your three best AI opportunities, ranked by return.
For example: A 30-person firm learns that intake and scheduling, not marketing, is where AI will save the most time.
Typical timeline: 2 to 3 weeks
For leadership teams who need a 12-month plan: which tools to buy, what to build, what to leave alone and how to budget for it.
For example: A regional distributor replaces six overlapping AI subscriptions with two, and funds one custom forecasting model with the savings.
Typical timeline: 3 to 6 weeks
Hands-on workshops that teach your staff to use AI tools well and safely, built around their real daily tasks.
For example: An office team cuts weekly report preparation from five hours to one after a half-day session.
Typical timeline: Half day to 4 weeks
Systems that take repetitive work off your team, connected to the tools you already use.
Agents that handle multi-step back-office work such as quoting, follow-ups, data entry and reconciliation, with a person approving anything important.
For example: A home services company sends accurate quotes in minutes instead of the next day.
Typical timeline: 4 to 10 weeks
A phone agent that answers every call, books appointments, answers common questions and hands off to staff when needed.
For example: A dental office stops losing after-hours callers to the practice down the street.
Typical timeline: 3 to 6 weeks
A chat assistant trained on your own content that answers questions accurately, captures leads and knows when to bring in a human.
For example: An e-commerce store answers sizing and shipping questions at midnight and sees fewer abandoned carts.
Typical timeline: 3 to 5 weeks
Read, extract, check and route the documents your team handles by hand: intake forms, invoices, claims, contracts and bills of lading.
For example: A billing team stops retyping intake packets and catches missing signatures before submission.
Typical timeline: 4 to 8 weeks
A private assistant that answers staff questions from your manuals, policies and past work, and drafts content in your voice.
For example: New hires find answers in seconds instead of interrupting senior staff.
Typical timeline: 4 to 8 weeks
Data science that turns the records you already keep into forecasts and decisions.
Forecast demand, revenue, staffing and inventory with honest ranges, so you plan for what is likely rather than a single guess.
For example: A retailer orders closer to real demand and carries less dead stock into the next season.
Typical timeline: 4 to 8 weeks
Scoring and classification models for churn, lead quality, no-shows, fraud and risk, each with the reasons behind every score.
For example: A subscription business calls at-risk customers a month before they would have cancelled.
Typical timeline: 6 to 12 weeks
Dashboards your team actually opens, fed by data that updates itself, with the few numbers that matter on top.
For example: An owner sees margin by job every Monday instead of at quarter end.
Typical timeline: 3 to 8 weeks
Focused studies that answer one expensive question: where customers drop off, which products lose money, why costs are rising.
For example: An online store finds that one shipping rule is causing a third of its abandoned checkouts.
Typical timeline: 2 to 5 weeks
Keep AI accurate, safe and accountable after it goes live.
An inventory of the AI your company uses, a practical policy and the documentation that customers, insurers and regulators increasingly ask for.
For example: A company answers a large client's AI security questionnaire in a day instead of a month.
Typical timeline: 2 to 6 weeks
Independent testing of an AI system you built or bought: how often it is right, how it fails and whether it can be pushed into saying things it should not.
For example: A software company finds and fixes a support bot that was inventing refund policies.
Typical timeline: 2 to 4 weeks
Ongoing care for AI in production: accuracy and drift tracking, cost control, updates and a monthly report on results.
For example: A forecasting model is retrained the week a supplier change shifts the pattern, not three months later.
Typical timeline: Monthly
We choose tools for your situation, not the other way around, and we avoid locking you into anything you cannot leave.
Claude, GPT, Gemini and open-weight models such as Llama and Mistral, selected per task for accuracy, cost and privacy.
Python, scikit-learn, XGBoost, PyTorch, forecasting libraries and SHAP-style explanations.
SQL databases, BigQuery, Snowflake, Power BI, Looker Studio and Tableau.
Google Workspace, Microsoft 365, HubSpot, Salesforce, QuickBooks, Shopify and common practice-management software.
That is what the assessment is for. In two to three weeks you will know exactly where AI pays off in your business, and where it does not.