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AI consulting services are expert-led engagements that help an organization plan, build, and scale AI systems. A typical engagement covers four kinds of work, sometimes in a single phase and sometimes in separate ones. Discovery and use-case mapping — figuring out where AI fits in the business, what the data looks like today, and which workflows are realistic targets. Technical architecture and build-vs-buy — recommending the right model, the right framework, and whether to write code or buy a platform. Pilot implementation — actually building the first production system, not just documenting what one would look like. Iteration and governance — ongoing evaluation runs, model swaps as the landscape evolves, new integrations, and the periodic eval-set growth that keeps the system reliable. The phrase often conflates two very different products: strategy-only consulting that hands off to a separate build team, and practitioner consulting where the same firm scopes and ships. The second model is what we do; we will tell you when the first model is actually the right call for your situation.

Different products for different buyers. Big 4 AI consulting is staffed primarily by smart analysts and recent business-school graduates, supervised by a partner you meet at kickoff and at close. The deliverable is usually a strategy deck and a recommendation; the build is a separate engagement with a different team. The cost is high six to seven figures for strategy alone. The value is the firm name on the document — useful for board-level decisions, regulated industries, and politically complex transformations where the consultant's brand carries weight. Boutique practitioner consulting (what we do) is the opposite shape. Smaller team, all senior, the person scoping the work is the person doing the work. The deliverable is a working architecture and a scoped pilot, often built in the same engagement. The cost is mid-five to mid-six figures per pilot. The trade-off is real: you do not get the McKinsey name on the document. You get the system in production faster, cheaper, and with the people who built it staying close enough to fix it. Most companies need one of these two, not both. We will tell you which one your situation wants on the strategy call — including telling you to hire Accenture if that is the right answer.

It depends entirely on the shape and scope of the work, and we will not quote a number on this page because doing so would be useless to you. The dimensions that actually drive the cost: how much of the engagement is strategy versus build, how clean and accessible your data is today, how many systems you need integrated with, what the cost of being wrong looks like in your context (which sets the eval and governance budget), and whether you want us to stay on for iteration after launch. Industry benchmarks for boutique AI consulting are mid-five to mid-six figures for a scoped pilot, and Big 4 engagements run higher by a meaningful multiple. We give a written proposal at the end of a free 30-minute strategy call — scope, deliverables, timeline, and total cost in one document — so you can compare apples to apples against whatever else you are weighing.

Most of our consulting clients are non-technical founders, ops leaders, and executives. The job is translating between AI and engineering reality and the business outcomes the leader is responsible for. You will see plain-English trade-off conversations, concrete timeline and cost estimates, and architecture diagrams that come with a narrated explanation — not diagrams that need a translator. We deliberately do not require you to learn the jargon to make a good decision. We do require you to be available enough during the engagement to make the decisions only you can make: which workflows are highest priority, which trade-offs you are willing to accept, and how aggressive you want the rollout pace to be.

Yes — the deliverable is technology- and team-neutral by design. The architecture, build-vs-buy recommendation, vendor short-list, and pilot scope all read as a document any competent AI engineering team could pick up and execute. Most clients do choose to build with us because the context handoff is free and the strategy team is already in the weeds; some bring it to their existing engineering org or to a different vendor for procurement reasons, and that is fine. The deliverables are written so the strategy holds even if the people change. We do not put gotchas in the architecture that force you to come back to us.

Frontier hosted models (Claude family, GPT family, Gemini), open-weight models (Llama, Mistral, Qwen) when self-hosting matters for cost or privacy, and domain-fine-tuned variants when the task benefits from it. Frameworks: LangGraph for stateful multi-step orchestration, CrewAI when role-based multi-agent design earns its keep, and pure-code orchestration when either framework adds latency or debugging overhead without earning it back. Clouds: AWS, GCP, Azure, and on-premise — we pick based on your existing footprint, not based on which provider gives the best consulting partner rate. Vector stores and retrieval: Pinecone, Weaviate, pgvector, and Postgres full-text when that is actually enough. The architecture is loose enough that a model or framework swap is a configuration change, not a rewrite — so you are never locked into a vendor's pricing roadmap.

The free 30-minute strategy call can usually be booked inside the week. The 1-2 week strategy sprint can start within 5-7 business days of the call if the engagement is a fit, depending on calendars. The pilot build follows directly from the strategy sprint with no handoff gap. End-to-end, you can have a written architecture in hand inside two weeks of first contact and a working pilot in production inside eight. The bottleneck is almost never on our side — it is usually whether your team can free up the decision-makers to be available during discovery.