Questions we get on every call.
Straight answers, no "book a call to find out." If your question isn't here, it's one free intro call away.
What does an AI consultant do?
Audits your workflows and systems, identifies where AI agents or automation will actually pay off (and where they won't), then designs, builds, and helps operate the solution in production. The good ones write code and ship working systems, not just slide decks.
Do I need an AI consultant, or can I just use ChatGPT?
ChatGPT is fine for one-off answers or drafts. A workflow that needs to run reliably every day — pulling data, routing leads, calling internal systems without a human checking each step — needs architecture, error handling, guardrails, and monitoring that a chat window doesn't provide.
How is Supergood different from a big consulting firm?
Big firms sell strategy decks and staff junior consultants to execute them. Supergood is one senior practitioner — 14 years of product experience — who builds the thing himself, using tools you already pay for. No committee, no army of analysts, no six-figure floor.
What industries does Supergood work with?
Marketing and ops teams at mid-market companies are the core focus, drawing on experience from travel/hospitality, live entertainment, and consumer hardware. The audit-build-govern approach applies to any team drowning in manual work or trying to get AI agents past the demo stage.
How much does AI consulting cost?
Independent AI consultants typically charge $150-$350/hour, with custom agent builds running $15,000-$75,000 for mid-market scope. Supergood works at a leaner scale: a $500 Quick Scan (credited in full toward any engagement booked within 30 days), a $2,500-$6,000 readiness assessment, $8,000-$40,000 project builds, and $3,000-$8,000/month retainers — flat fees, not hourly billing. Full breakdown on the AI Consulting page.
What is an AI readiness assessment, and how much does it cost?
A structured audit that scores which of your workflows are worth automating or turning into an AI agent, which aren't yet, and what order to tackle them in. Supergood offers a $500 Quick Scan — a live working session with 3 prioritized recommendations on the call, a one-page written summary the next business day, and the $500 credited toward any engagement within 30 days — or a full assessment at $2,500-$6,000 (1-2 weeks, written roadmap). Details on the AI Readiness Assessment page.
What's the difference between the free intro call and the $500 Quick Scan?
The intro call is a fit conversation: do we click, can we actually help, is AI even the right tool — no deliverables, no pressure. The Quick Scan is a working session on your actual workflows: 3 prioritized recommendations on the call, a one-page written summary after, and the $500 credited toward any engagement within 30 days.
How long does an AI consulting engagement take?
A Quick Scan is 30 minutes. A full AI Readiness Assessment takes 1-2 weeks. A workflow or agent build typically runs 3-8 weeks depending on scope. Ongoing agent ops or automation maintenance is a monthly retainer with no fixed end date.
Do I have to hire Supergood to build after a readiness assessment?
No. The assessment is priced as a standalone deliverable — you own the findings and can take them to any team, including your own, to build. Most clients continue with Supergood because the person who did the audit already understands the systems, but it's not a requirement.
How do I get AI agents into production?
Start narrow: one workflow, a clear success definition, a limited tool set. Add evals before you add features. Put guardrails around anything that writes data or spends money. Most agents fail to reach production because nobody defined what "working" means or built the observability to catch it when it isn't. More in AI Agent Consulting.
What are AI agent guardrails?
Constraints that keep an agent's autonomy inside a safe boundary: tool allowlists, input/output validation, spend and rate limits, human-approval checkpoints for high-risk actions, and logging that lets you reconstruct what the agent did and why. See AI Agent Governance.
What's the difference between an AI agent and a workflow?
A workflow follows a fixed sequence of steps you define in advance. An agent decides its own sequence of steps based on the task and what it observes along the way. Agents are more flexible but harder to test and secure — many production systems are best built as a workflow with one or two AI-powered steps.
What is agent observability, and how is it different from evals?
Observability is the ability to see what an agent is doing while it's doing it — tool calls, reasoning, token spend. Evals measure whether the agent's outputs are actually correct. Observability tells you the agent ran; evals tell you whether it ran correctly. Production systems need both.
What's the difference between AI automation and RPA?
Traditional RPA replays fixed clicks and keystrokes and breaks when a screen layout changes. AI-assisted automation uses APIs and, where it helps, language models to handle the parts that used to require human judgment, while keeping reliable, rule-based steps as plain automation. See AI Automation Consulting.
Do you build with our existing tools, or sell us new software?
Existing tools, by default. Most teams already have enough automation horsepower sitting unused in Microsoft 365, their CRM, and their existing AI model subscriptions. A new platform means a new vendor and a new thing to maintain — recommended only when the existing stack genuinely can't do the job.
What if the assessment says I don't need AI?
Then that's what gets said. Half the value of an honest readiness assessment is being told "automate this with a spreadsheet formula, not an agent" — it saves you from building something you didn't need.
Ask it on a call instead of a form.
A free intro call, no pitch. We'll answer it straight, even if the answer isn't the one you were hoping for.
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