We build the operational systems, governance, and integration architecture that make AI investments work as a coherent operation, not a collection of tools.
Founded by Chris McCarthy. A two-principal partnership based in Hudson, Ohio. We work with consumer brands and mid-market organizations navigating AI adoption at the operational layer, not the experimental one.
OB.1 is methodology-driven, vendor-neutral, and diagnostic-first. We do not sell tools. We do not sell time. We work in fixed-phase engagements that produce systems durable enough to hold up under operational pressure.
Our work sits one altitude above the AI vendors. We design the architecture that connects them, the governance that disciplines them, and the workflows that absorb their output without breaking the teams using them.
Most consumer brand creative organizations now run multiple AI vendors in parallel. Image generation. Content automation. Productivity assistance. Regulatory review. Each delivers value in isolation. None of them connect to the asset library, the project management layer, or each other. The result is a stack that produces faster outputs but slower operations.
A five-phase methodology that moves an organization from current-state assessment to executable architecture. Each phase has a defined output, a clear handoff, and a measurable timeline.
Begin with the free AI Transformation Score to identify your highest-leverage AI opportunity. The full AI Readiness Score delivers a comprehensive diagnostic report with quantified ROI projections.
We validate the score and authorize comprehensive current-state analysis. Define scope, identify key stakeholders, and map existing workflow architectures end-to-end.
We ingest documentation, data flows, and stakeholder interviews through our proprietary AI stack to surface inefficiencies and technical opportunities traditional consulting overlooks.
A premium, board-ready engineering plan that defines the future-state with 99% implementation accuracy. Technical requirements, resource allocation, and risk mitigation included.
Phase 1 Functional Design Documentation with development specifications, implementation timelines, and measurable outcomes. Build internally or with our preferred-vendor network.
Five practice areas that anchor most engagements. Scope is shaped by diagnostic findings, not pre-packaged offerings.
Structured assessment of current AI tool maturity, workflow integration depth, and operational friction across the creative pipeline. We map where AI outputs stall, where manual workarounds have ossified, and where governance has not yet caught up to deployment. Output: AI Readiness Score and Operational Pain Map.
Design the connective tissue between AI vendors, design platforms, project management, and digital asset systems so outputs flow as living objects, not flat files. Reduce manual handoffs. Eliminate version drift between source assets and downstream usage.
Policy frameworks, vendor accountability structures, and ethical oversight protocols. For organizations with multiple AI vendor relationships forming in parallel, we build the governance layer that keeps adoption fast and disciplined at the same time.
Purpose-built AI skills and agents tailored to specific operational workflows. Asset versioning at scale, retailer-specific output generation, regulatory pre-screening, alt-text automation, brief intake parsing. Designed to integrate with existing systems, not isolate from them.
Structured rollout, team enablement, and adoption measurement frameworks. Because the most precise system fails if the people using it don't trust it, and the most expensive AI investment depreciates if adoption stalls.
Rules-before-tools enforcement built into the audit pipeline. Each checkpoint produces an artifact that withstands a board review, a regulator, or your own future audit trail.
An 82-point assessment scored 0–100 by our AI engine. No engagement starts without a documented data hygiene and AI maturity baseline.
Two to four accountable owners required by role: executive, operations, customer-facing, and finance. You cannot govern what no one owns.
Stakeholder interviews logged as 1,500 to 2,000 character transcripts covering pain, ideal state, constraints, and success metrics. Implicit assumptions made explicit and contestable.
Three to five documents required, auto-extracted into ten tagged excerpts categorized as Process, Technology, or Strategy. No recommendation without documented evidence.
Four-stage AI-driven audit: merge sources, validate findings, identify gaps, build strategy. Twelve recommendations produced with confidence scores attached.
Six confidence dimensions on every audit layer, typical range 82 to 92 percent. Investor-grade transparency baked into every output.
Five contradictions surfaced per audit, structured as Documented vs. Reality with priority and supporting evidence. The gap between belief and evidence named explicitly.
Built-in benchmark library: blended labor rates, implementation cost ranges, maintenance percentages. Year-1 ROI, payback period, and 5-year net value on every recommendation.
Four frameworks auto-generate per audit: BCG AI Maturity, Deloitte 2x2 Impact Matrix, Know-Do-Build Roadmap, BCG 10-20-70 Resource Allocation. External standards, not internal opinion.
Three role-personalized memos plus a 10-page PDF report. Every stakeholder leaves with a signed, dated artifact tied to their accountability scope. Audit trail closed.
Every relationship begins with a calibration step. Scope expands only when the diagnostic supports it.
Our signature diagnostic instrument. A structured assessment that produces a quantified AI maturity score and surfaces the highest-friction patterns in your current operational stack. We make it complimentary as a starting point for engagement conversations, not as a deliverable in itself.
Formal scoped assessment that builds on the AI Readiness Score. Stakeholder interviews, workflow mapping, tool audit. Output: Operational Pain Map (visualized friction inventory across the AI tool stack), a prioritized recommendation set with effort and impact scoring, and an executable engagement plan.
Multi-phase architecture engagement. Output: governance framework, integration architecture, implementation roadmap, and acceptance criteria for each phase of execution.
Ongoing advisory and governance. Built for organizations executing on a blueprint and needing operational oversight as AI maturity scales across the organization.