AI consulting services for mid-market teams

AI consulting is the work of helping a business figure out where AI fits, what to build, and how to get it into production without wasting time or money. A good AI consultant looks at your workflows, data, and team, then helps you pick the use cases that are worth pursuing. Some firms stop at strategy. Others stop at a prototype. The ones worth working with stay close through the full build and keep improving it once it’s live.

AI strategy consulting is about deciding what to do. AI implementation is the work of actually building it. Most firms do one or the other. Strategy firms hand you a plan and leave the build to someone else. Dev shops take a spec and build it without questioning whether it’s the right thing. LaunchPad Lab does both, with the same team from start to finish. No handoffs, no lost context, no finger-pointing when something doesn’t work.

A product engineering firm is the right choice when you need the thing built, not just planned. Big 4 consultancies make sense when you need a global firm with thousands of consultants and deep enterprise audit relationships. LaunchPad Lab is different. We’re a Chicago-based team you can meet in person, working in one timezone, with the engineers who will build the software sitting in the same room as the strategists. That closeness is how things actually get shipped.

An AI consulting engagement usually takes anywhere from two weeks to six months or more, depending on the scope. Discovery and roadmapping run about two weeks. A proof of concept takes two to four weeks. A production MVP usually lands between eight and sixteen weeks. Full platform builds with deeper integrations run three months or longer. Ongoing support and optimization carry on from there, since AI systems need care as your data and business change.

Your data stays yours. We build on your systems with proper access controls, and we don’t use your data to train public models. For regulated work, we follow the compliance your industry requires, including HIPAA where it applies, with audit-ready outputs. Security and guardrails are part of the production build, not an afterthought.

Look for a partner that can build, not just advise. Ask who writes the code, whether they have shipped AI into production for real clients, and whether the strategists stay on through launch. Ask for named case studies with real numbers, not anonymous logos. The firms worth hiring can point to systems that are live and used, not decks and pilots that stalled. At LaunchPad Lab, you get 14+ years of experience shipping 760+ projects behind every recommendation.

We do both. LaunchPad Lab is a product engineering firm first, so the team that shapes the strategy is the same team that writes the code. That matters because a strategy built by people who can’t build tends to miss the practical details: what your data can actually support, what’s realistic to integrate, and what will hold up once real users are on it. We stay involved from the first workshop through production launch and long-term support.

 A typical AI consulting engagement runs about four to six weeks and ends with a decision, not a half-built system. It opens with a two-week discovery sprint that maps your workflows and ranks the use cases worth pursuing. Then we build a proof of concept on your real data and test it against the success metrics we set up front. You finish with a clear roadmap and a scoped proposal. If the evidence says go, the production build is the next, separate engagement.

Cost depends on scope, since every AI project is different. We start most engagements with a short, fixed-scope discovery, so you get a clear number for your specific use case before you commit to a build. That beats a generic price tag that never fits the actual work, and it means you only invest in a build once the use case is proven.