Generative AI Consulting Services Built on Real Outcomes.

Engagements range from $10,000 for a 4-week strategy sprint to $100,000+ for a full multi-vertical roadmap with pilot. Cost drivers: industry depth, use-case count, pilot inclusion, and compliance scope. We provide a fixed-scope quote after a 30-minute discovery call.

An 8-week average covers Discovery, Strategy, Vendor Decision, Pilot Design & Build, and Handoff. Standalone strategy sprints run 4–6 weeks. Multi-vertical or heavily regulated engagements run 12–14 weeks.

Consulting is strategy, audit, vendor selection, and roadmap — as your AI consulting company, we plan, you decide. Development is engineering — we build the production system. Many clients run consulting first, then move to development with the same team.

No. All evaluations run with zero-retention API configurations or on-prem open-weight models. For regulated clients (Healthcare, Insurance), we recommend private deployments on AWS Bedrock, GCP Vertex AI, or Cloudflare Workers AI with full data-residency controls. Your data never trains a third-party model.

Every candidate use case gets scored on four dimensions: business ROI, technical feasibility, data readiness, and risk. We benchmark against industry comparables. Output: a prioritized list with confidence scores ready for executive sign-off.

Our LLM consulting benchmarks candidate models against your data using a curated eval harness covering accuracy, latency, cost, and data residency. Vendor scorecards rate product, support, lock-in risk, and pricing. Recommendation includes a portability plan so you can switch vendors later.

Every engagement includes a working pilot on your data, evaluated end-to-end with an eval harness and HITL controls. You own the code, the evals, and the documentation — no vendor lock-in to us.

Compliance is embedded in every engagement — not a separate add-on. We have industry advisors on staff for Healthcare, Retail, Insurance, and Real Estate. Output includes a compliance roadmap aligned with your auditor’s framework.

Three layers of defense. First, we ground every model in your real data using retrieval-augmented generation — so answers come from your documents, not the LLM’s training set. Second, every pilot ships with an evaluation framework that scores accuracy, groundedness, and refusal rate on every change. Third, high-stakes outputs route through human-review checkpoints before they reach an end user. For regulated workflows (clinical, financial, legal) we benchmark against a test set you sign off on — so accuracy becomes measurable, not anecdotal.

In ~95% of our engagements GenAI augments the team rather than replacing it — taking routine work (triage, drafting, lookup, summarization) off the plate so people focus on judgment work. Every engagement includes a change-management track: stakeholder mapping, internal communication plan, training sessions, and adoption metrics tracked monthly for 6 months post-launch. In our experience, the engagements that fail almost always fail on adoption, not technology — so we treat change management as a first-class deliverable.