AI
Implementation

Most companies treat AI as aspirational. They run pilots, build sandboxes, and announce initiatives, but the work rarely reaches production. The gap between pilot and production is where the value lives, and most companies never close it.

The result is a team that reads about AI on LinkedIn but operates the same way it did three years ago. AI as marketing positioning rather than AI as operating leverage.

Production workflows the team runs every day, not pilots waiting for approval to scale.

The work is grounded in shipped production workflows, not theoretical possibility. Each engagement starts with one question: where is the team spending hours on work that AI can compound or eliminate? The usual answers are reporting automation, content production, competitive intelligence, analytical synthesis, and creative iteration.

Implementation uses Claude, Cursor, n8n, Zapier, and custom internal applications. The pattern is workflows that team members run daily, not pilots that wait for executive sign-off to scale.

Process redesign matters as much as tooling. AI changes which steps in a process are the bottleneck, so the work includes reshaping the workflow to take advantage of the added capacity.

Anthesia operates AI-native from day zero. Daily workflows in Claude, Cursor, and n8n cover analytics, reporting, competitive intelligence, content production, infrastructure, and product development. The B2B APIs in active development, MedData and Shopify Intelligence, are built with an AI-native development workflow.

AI has also been implemented across entire marketing functions in production, including custom internal applications for analytics, reporting automation, and competitive intelligence, plus AI-augmented creative production through Midjourney, Veo 3, and Krea.ai. The work draws on senior in-house and agency experience across 250+ consumer brands.

• 01

  AI Audit and Roadmap

  Two weeks. Assess the current state, identify high-leverage implementation opportunities, and deliver a prioritized roadmap with effort and impact estimates. The outcome is a clear plan for what to ship and in what order.

• 02

  Implementation Project

  Four to eight weeks. Build production workflows, train the team, and drive adoption. Scoped to two to four specific workflows per engagement. The outcome is production workflows in daily use, with documented internal training.

• 03

  Fractional AI Advisor

  Monthly retainer. Ongoing optimization, new use case identification, and tool evaluation as AI capabilities evolve. The outcome is continuous improvement of the AI operating layer.

• 04

  Team Workshop

  A half-day to one-day intensive. Hands-on training for marketing and operations teams. The outcome is practical AI literacy for eight to twenty people.

Pricing is scoped to each engagement rather than sold in fixed tiers. Every engagement begins with a free initial consultation, where we define the work and the cost together.

Marketing and operations leaders at mid-market consumer companies. Teams stuck in AI pilots that never moved to production. Founders who want AI as leverage without hiring an in-house AI team. Leaders who see AI as compounding capacity rather than a threat.

The following are representative scenarios. They illustrate typical scope and outcome shape, not actual client work. Real case studies will replace them as engagements progress.

• A mid-market consumer brand with a marketing team of eight, spending hours each week on manual reporting and competitive intelligence. Scope: a six-week implementation of Claude-powered reporting automation, competitive intelligence agents, and custom internal applications for analytical synthesis. Outcome shape: fifteen to twenty hours per week of team capacity returned to higher-leverage work.

• A consumer brand whose leadership is interested in AI but has no production workflows live. Scope: a two-week AI Audit and Roadmap identifying six to eight high-leverage opportunities, prioritized by impact and effort. Outcome shape: a written roadmap with a sequenced plan suitable for an in-house build or a follow-on implementation engagement.

• A larger company seeking fractional AI advisory. Scope: a monthly retainer covering ongoing workflow optimization, new use case identification as model providers ship new capabilities, and quarterly team workshops. Outcome shape: an AI operating layer that compounds as the team grows.

What AI tools do you primarily use?

Claude as the primary reasoning model, Cursor for development, n8n and Zapier for workflow automation, and tools chosen per use case, such as Midjourney and Veo 3 for creative or custom internal apps for domain-specific work.

Do we need a technical team to maintain the workflows?

Most implementations are operable by non-technical team members after handoff. Some custom application work needs technical maintenance, and that is flagged before scope is set.

How do you measure success?

Time saved per workflow, capacity expansion measured as output per team member, and quality of output. Specific metrics are defined per engagement during the audit phase.

Will this replace anyone on the team?

No. The goal is to return hours spent on repetitive work back to the people doing it, so the team produces more rather than shrinks. Adoption depends on the team trusting and using the workflows, which is why training and handoff are part of every engagement.

How do you handle data privacy and security?

Workflows are scoped to the data each task actually needs, run within your own accounts and tools where possible, and documented so you can audit what touches what. Specific handling is agreed before any build begins.