Holston AI

Compares a Holston AI fixed-price 2–4 week build against a traditional ~6-month build. Returns build costs, labor savings captured, cost of delay, developer-weeks freed, payback week, and a month-by-month cumulative net position over the analysis window.

Generates a production-ready Next.js App Router repository layout, Prisma schema, and PostgreSQL DDL for a business domain (b2b_saas, marketplace, healthcare_admin, logistics, restaurant_retail, professional_services, manufacturing). Multi-tenant by default with org membership and audit logging; optional agent-run tables.

Audits a live public website and scores (0–100, grade A–F) how well AI assistants (ChatGPT, Claude, Perplexity) and search engines can find, read and cite it. Checks robots.txt rules for AI and search crawlers, server-rendered text, business and FAQ structured data, Open Graph, llms.txt, sitemap, HTTPS, mobile viewport and response time. Returns each check with a plain-English fix, the top 3 fixes, and a shareable report link. Takes up to ~20 seconds.

Builds an itemized Holston AI quote from fixed prices: one-time builds, monthly plans, first-year total (monthly plans have a 12-month minimum), timeline, terms, fitting add-ons, and a shareable link to the quote on holstonai.com. Ask what the customer needs first; do not add plans they did not ask for.

Draws a map of a company's software systems and how data moves between them (automatic, manual, export/import), as a Mermaid diagram with a shareable link. Flags manual handoffs, the same data kept in several unsynced systems, hub systems many processes depend on, and systems with no connections. Use after interviewing the user about their systems; nothing is stored.

Calculates what manual, repetitive work caused by disconnected systems costs per year: staff hours, full-time equivalents, labor and error costs, five-year cost, and the savings from automating it. Input a list of tasks with hours per week; hourly cost is optional.

Estimates the revenue a business loses each year to unanswered phone calls (missed calls, lost callers, lost customers, monthly and five-year loss) and how much an AI receptionist that answers every call could recover. Give calls per week (or missed calls per week) and the average job value; every other assumption has a sensible default.

Lists Holston AI's ready-built AI receptionist templates by industry (HVAC, plumbing, roofing, dental, law and more): the services, the questions the agent asks, the default call flow, optional steps, and emergency handling. Give an industry id for one template, or omit it for the list. Use it before design_ai_agent.

Designs an AI receptionist for a business from Holston AI's templates: the step-by-step call flow, what it does at any point (pricing questions, emergencies, transfers), and a link to holstonai.com where the owner can edit it and talk to the agent out loud in their browser for free. Only the industry and business name are required. Holston AI builds the real version on the business's phone line at a fixed price confirmed after a short call.

Reads a public website (homepage, sitemap and main pages) and writes a ready-to-upload llms.txt file that tells AI assistants who the business is, what it offers, where it works and which pages matter. Optional details (services, service area, hours, key facts) make it stronger. Use it when an audit shows no llms.txt.

Builds ready-to-paste Organization or LocalBusiness structured data (JSON-LD) with address, phone, hours, service area, services, social profiles and optional FAQs. If a website is given, details are pre-filled from it. Returns the <script> tag plus checks to fix before publishing. Use it when an audit shows missing business schema.

Sends the user's contact details and project summary to Holston AI so the team can follow up. Before calling: collect name, email or phone, and a summary of what they need (include any quote or report links from this conversation), show the user exactly what will be sent, and get their explicit agreement. Never call it without that agreement or with made-up details.