Free AI Business Assessment
Life Tap Labs guides businesses to the first useful AI build: Booking Desk for lost demand, Workflows for internal bottlenecks, or Foundations when the public path needs cleanup.
Build Model
The first implementation should connect the parts that already matter: demand, intake, documents, staff review, scheduling, reporting, follow-up, and the systems the business already uses.
Who This Serves
Some businesses are losing calls. Some are buried in paperwork. Some need a stronger website before automation can help. The assessment guides the first move so the build matches the real problem.
Business condition
A plumber, handyman, cleaner, HVAC company, detailer, or repair business needs faster response, better intake, and cleaner scheduling.
Likely first move
Likely first move: AI Booking Desk. If the website or quote path is weak, Foundations cleans that up before the desk gets heavier.
AI Booking Desk
AI Booking Desk responds fast, captures the right details, and hands staff a clean next step.
01 - Booking Desk lane
Best fit
Best for service businesses where a booking fee, diagnostic fee, deposit, or paid scheduling flow may already be normal.
02 - Booking Desk lane
Best fit
Best when partners should not pay to ask questions or request appointments and the business pays for always-on response coverage.
03 - Booking Desk lane
Best fit
Best when some requests can be handled by AI and others need staff review, custom quoting, or manual scheduling.
04 - Booking Desk lane
Best fit
Best for higher-trust or regulated teams that need stricter review rules, escalation, documentation, and human ownership.
Lane selection
Some teams need simple response coverage. Others need hybrid review or a managed front-office layer. The assessment identifies the right lane before pricing.
Workflows
Workflows are the family for internal AI systems. They are for teams dealing with documents, reports, research, approvals, spreadsheets, or repeated admin work. The build can use AI agents, LLMs, machine learning, automation, dashboards, and integrations, but the point is simple: reduce manual drag without losing human judgment.
AI gathers context, compares sources, drafts briefs, and leaves a reviewable trail. Best when staff repeats source review, comparison, briefing, or evidence gathering every week.
Staff can search SOPs, documents, transcripts, notes, and files without guessing where answers live. Best when the answer exists somewhere, but people keep asking around because knowledge is scattered.
LLMs help turn notes, spreadsheets, calls, or source documents into structured reports for human review. Best when reports are rebuilt from the same inputs and judgment still needs to stay human.
Teams can approve, correct, replay, and monitor AI-assisted work instead of trusting a black box. Best when the workflow needs visibility, approvals, replay, correction, and operational control.
Voice or chat agents collect details, answer approved questions, and escalate anything outside the rules. Best when callers or visitors need fast help, but the business needs strict boundaries around answers.
Foundations
If the website is unclear, the service pages are thin, or the quote path is messy, automation will amplify the mess. Foundations clean up the surface partners actually see before AI agents are added. Business Website Sprint is the first public package in this family.
What gets fixed
Business Website Sprint starts at $1,500.
Implementation Signal
Public pages should show how Life Tap Labs thinks through real implementation work without pretending a concept is a live deployment. Review the scope labels, service fit, and human-review boundaries before the first build.
A public-source implementation simulation for a bounded orthopedic scheduling and routing voice agent with deterministic policy gates and staff review.
What it demonstrates
Real-time voice runtime choices, policy-gated scheduling, escalation discipline, and rollout design for higher-trust intake operations.
Supports
AI Booking Desk / WorkflowsImplementation work around reconstructing product journeys from order history through data engineering, sequential modeling, and privacy-preserving AI-assisted delivery.
What it demonstrates
Journey reconstruction, Markov-style recommendation design, analytics system framing, and disciplined AI acceleration without raw-data exposure.
Supports
WorkflowsStart Here
The assessment identifies whether the first useful move is demand capture, internal workflow automation, public-path cleanup, or a scoped prototype so implementation starts where the business is actually getting stuck.