AI development company for service businesses

Plainbuilt is an AI development company for service businesses. The work is applied AI on foundation models: voice, chat, SMS, and email agents, retrieval over your own data, and AI inside the app you already run, wired into ServiceTitan, Jobber, or HubSpot. It ships as production software, live in 3 to 8 weeks, with a written ROI commitment.

It is the AI half of the custom software practice: an engineering practice since 2014 with 200+ shipped projects, we build the systems our AI runs on, and the seven AI automation modules run on them.

Four shapes cover most of the work, each with a page and a number behind it.

Agents that answer and act. An AI voice agent answers the phone 24/7, qualifies the caller, and books the job into ServiceTitan, Jobber, or Housecall Pro as the call happens, live in 3 to 4 weeks. At a regional concrete contractor, monthly lead capture rose 23% within 60 days of go-live, and standard-job quotes went from 2 to 3 days to under 4 hours once quote automation followed.

Chat, SMS, and email agents. An AI chatbot answers from your own content, cites the page it came from, and hands off to a booking, a call, or a text; the bot on this site is the demo. Review reactivation asks for the review 2 hours after the job and rebooks past customers by SMS: a 2.4x rebooking rate and 340+ dormant customers back within 90 days at a 3-location cleaning and restoration company. The RFQ email agent for manufacturers drafts a quote in minutes from historical job data, and every quote is reviewed before it is sent while the agent runs alongside your estimator.

Retrieval and AI inside the app you already run. Adding AI to an existing app means a question-answering layer over your own data, agents that act through the APIs your app already exposes, and workflow steps such as classification, extraction, and drafting dropped into existing screens. The six patterns guide is the engineering opinion behind it, and the first agent guide is the loop every agent here runs: work out the intent, call a tool or two, say something back, hand off when unsure.

Copilots. The dispatcher copilot on McLeod and Samsara drafts the next-day board and re-optimizes the remaining routes within minutes when a driver calls out. At a regional logistics agency, route planning per dispatcher fell from 2 to 3 hours to under 1 hour, and manual check-calls from 60 to 80 a day to 10 to 15.

Each agent has a scripted demo you can run in the browser with no sign-in, on the demos page. The software side is the portfolio: the apps and websites Plainbuilt designed and built full stack, the customer booking, owner assistant, patient intake, and real estate categories among them.

What generative AI development does Plainbuilt do?

The plain kind. The agents run on language models that write the reply on the call, the quote draft, and the follow-up text, so every agent above is generative in that sense. On this site, the bot and the demo agents run on models from OpenAI and Anthropic: Anthropic models answer the bot and run the SMS and email demo agents, OpenAI runs the bot's voice mode, and the typed demo chat runs on your own OpenAI or Anthropic key, held in memory for the session and never stored (privacy, demo terms). Each model sits behind an adapter, so a swap is a configuration change, not a rebuild; your data stays in your systems, and nothing is copied into a third-party training set.

What it is not: the output we measure is a booked job, a quote out the door, or a route on the board, never a page of marketing copy. An agent hands off when it is unsure: a call it cannot handle is transferred to your team with a summary of the caller, the issue, and the qualifying details, and in the scripted demo the manufacturing RFQ agent drafts the quote for the estimator to approve.

Does Plainbuilt do machine learning development?

Applied machine learning, yes. The AI/ML engineers on a founder-led team of 20 build, tune, and evaluate the models behind the agents. The voice agent is a custom voice model trained on your services, pricing tiers, service areas, and FAQs, and it keeps improving from call recordings and booking outcomes after go-live. The dispatcher copilot models your best dispatcher's decision patterns from historical data, every override trains it, and routine assignments run on their own once the approval rate passes 90%. Quote automation validates against your historical job data during setup and keeps improving from outcome data. Every deployment is measured against a baseline (how we work).

Details to follow

What don't we build?

Pilots. We ship into production or we don't take the engagement, with a kill switch on every deployment and nothing that works only in a demo. Software licenses and generic chatbots: we are implementers, not advisors, and every system is wired into your scheduling software, CRM, and workflows, then measured against a baseline. Anything the numbers argue against: if the diagnosis projects year-one ROI under 3x, we tell you not to hire us, and we tell you when AI isn't the answer.

How do you check an AI vendor before you sign?

The 20 questions guide says the first five separate implementers from talkers. Plainbuilt's answers, in the guide's order:

1. Case studies in your industry, with a date: three dated, anonymized deployments on the work page, a concrete contractor, a logistics agency, and a cleaning and restoration company, each with its numbers and their methods, and behind them the portfolio of apps and websites designed and built full stack.
2. Your platform, and what moves in each direction: jobs and appointments are created in ServiceTitan, Jobber, or Housecall Pro as the call happens, HubSpot carries the pipeline for teams that run it there, and the dispatcher copilot reads McLeod and Samsara and drafts the next-day board there.
3. ROI method: a baseline taken before go-live, the same number measured at 30, 60, and 90 days, and a redesign on our dime if a workflow underperforms its projection by 20% or more (the commitment, annotated).
4. Timeline in writing, checked against references: 3 to 8 weeks depending on scope, written into the build plan; no client reference is published yet, because the clients are anonymized until they approve naming.
5. When things go wrong: a kill switch on every deployment, a call the agent cannot handle transferred to your team with a summary of the caller, the issue, and the qualifying details, 30 days of post-launch support, then optional ongoing support on a flat monthly fee with SLAs (the FAQ).