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Independent · Vendor-Neutral · Senior Practitioners

We build AI that actually ships to production

Most enterprise AI advisory is junior analysts writing slide decks about models they have never deployed. We are the practitioners who built production AI systems at Google, Microsoft, McKinsey, and Accenture before launching an independent practice focused entirely on measurable outcomes.

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Former Google · Microsoft · McKinsey · Accenture 200+ enterprises advised 500+ AI models in production 340% average client ROI 15+ years senior experience
200+
Enterprises Advised
500+
AI Models in Production
340%
Average Client ROI
15+yrs
Senior Experience
Our Story

Born from frustration with enterprise AI theater

The catalyst for this practice was simple: watching large firms send MBA analysts with six months of AI experience to advise Fortune 500 companies on technology decisions worth hundreds of millions of dollars. The decks were beautiful. The strategies were theoretical. Almost nothing shipped.

We started this practice with a contrarian premise: enterprise AI advisory should be delivered by people who have actually deployed AI systems at scale. Not people who have studied deployments. Not people certified in AI frameworks. People who have spent years writing production code, debugging inference pipelines at 3am, and explaining to a CFO why the model performance numbers look different in staging versus production.

Every advisor at this practice brings a minimum of 15 years of hands-on enterprise technology experience. Our backgrounds span machine learning engineering, data platform architecture, enterprise software strategy, and C-suite AI program leadership at organizations including Google, Microsoft, McKinsey, and Accenture.

We have advised 200+ enterprises across financial services, manufacturing, healthcare, retail, insurance, and government. We have deployed more than 500 AI models into production environments. We measure our success in production deployments, not strategy documents.

We remain completely independent and vendor-neutral. We evaluate Azure, AWS, GCP, OpenAI, Anthropic, Google, and every major AI platform on objective merit. We do not accept referral fees from technology vendors. We do not have preferred partnerships that create conflicts of interest. Our only interest is delivering the right outcome for your organization.

If you want transformation theater, we are not the right fit. If you want AI that ships, drives measurable ROI, and scales across your organization, let's talk.

Track Record
$4.2B+
Documented client value created
94%
Of engagements reach production deployment
14wks
Average time to first production model
0
Vendor referral fees accepted
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Our Differentiation

Why practitioners outperform consultants

Every major AI strategy firm will tell you they have senior advisors. Here is what that actually means in practice.

01
Senior practitioners, not senior consultants
At large advisory firms, a senior engagement manager typically has 8 to 10 years of consulting experience, most of it in frameworks and client management. At this practice, senior means 15+ years of hands-on engineering, architecture, or AI program delivery. Our advisors have written the production code, managed the data pipelines, and debugged the inference infrastructure. They know where enterprise AI fails because they have been in those situations.
02
We measure success in production deployments
Most advisory firms measure success in deliverables: strategy documents, roadmaps, assessment reports. We measure success in production deployments, model uptime, inference latency, and business outcomes. 94% of our engagements result in a model deployed to production. That number is the only one that matters to us.
03
Genuine vendor neutrality
We do not have preferred vendor partnerships. We do not accept referral fees from Microsoft, Google, AWS, OpenAI, Anthropic, or any AI platform. Our evaluation framework is objective: technical capability, enterprise fit, total cost of ownership, and vendor risk. When we recommend a platform, it is because it is the right choice for your organization, not because it improves our margin.
04
We tell you when AI is not the right answer
One of the most valuable things we do is advise clients NOT to build the AI system they are planning. In a third of our initial assessments, the right recommendation is to fix the data infrastructure, the governance model, or the organizational readiness before investing in AI. This advice costs us short-term revenue. We give it anyway because it is correct, and because clients who follow it come back with better projects that actually succeed.
05
Former leaders at organizations you know
Our advisory team has led AI programs at Google, Microsoft, McKinsey, and Accenture. We bring the institutional knowledge of how enterprise AI is built at scale, combined with the independence that lets us apply that knowledge honestly. When a Fortune 500 CIO asks whether they should build, buy, or partner on a particular AI capability, we have been on both sides of that decision.
How We Work

Our operating principles

Not a manifesto. The actual rules we follow on every engagement.

Outcomes over deliverables
We are evaluated on production deployments, not PowerPoint decks. Every engagement defines success in business outcomes before work begins. If we cannot measure it, we do not promise it.
Speed to production
Our average time from engagement start to first production model is 14 weeks. We do this by starting with the smallest viable AI component, validating in production, and scaling from proven results. We do not spend six months in discovery.
Independent counsel only
We refuse vendor referral arrangements. We refuse preferred platform relationships. When we evaluate your options, the only variable is what is best for your organization. That independence is worth more than any commission structure.
Honesty about limitations
We tell clients when AI is not ready, when their data is not good enough, or when the problem is better solved without machine learning. This costs us short-term work. It builds the long-term trust that defines this practice.
Knowledge transfer built in
We do not build AI systems your team cannot maintain. Every engagement includes structured knowledge transfer so your internal team can own the model, the infrastructure, and the governance. The goal is independence, not dependency on us.
Enterprise scale, not startup theory
We have deployed AI in organizations with 5,000 to 200,000 employees, legacy infrastructure, complex governance requirements, and global regulatory environments. We know the difference between what works in a demo and what survives contact with enterprise reality.
Our Background

Where we come from

The experience behind our advisory practice spans the full spectrum of enterprise AI, from research labs to boardrooms.

Meet the Full Team →
Former Google AI leadershipSenior engineering and product leadership roles in Google's enterprise AI and Cloud AI divisions. Hands-on experience with large language model deployment, ML infrastructure at petabyte scale, and enterprise Vertex AI programs.
Former Microsoft AI strategyProgram leadership across Microsoft Azure AI, Copilot enterprise rollouts, and Microsoft 365 AI integration programs. Deep knowledge of enterprise Microsoft licensing, AI governance within regulated industries, and Azure OpenAI deployment patterns.
Former McKinsey AI practiceSenior engagement management across McKinsey's Global AI practice, advising Fortune 100 financial services and manufacturing clients on AI transformation programs with budgets exceeding $500M. Led teams across 12 simultaneous engagements.
Former Accenture AI practiceGlobal AI practice leadership including development of enterprise AI assessment frameworks adopted across 40+ client engagements. Specialization in AI Center of Excellence design, AI governance frameworks, and regulated industry deployments.
Enterprise data architectureCombined 60+ years of enterprise data platform experience across the team. Data strategy, data governance, and data engineering for AI workloads at Fortune 500 scale. Experience with Databricks, Snowflake, dbt, and all major cloud data platforms.
MLOps and production AI infrastructureHands-on MLOps experience across Kubernetes-based inference infrastructure, model monitoring, A/B testing frameworks, and model drift detection at scale. Our advisors have managed production AI systems serving 40,000+ concurrent users.
Industries Served

Enterprise AI across every major sector

We do not have a single industry specialization because enterprise AI problems are fundamentally similar across sectors. The data challenges, governance requirements, and organizational change management patterns repeat.

Financial Services Manufacturing Healthcare Insurance Retail & CPG Energy & Utilities Technology Government Telecommunications Logistics
Case Study
Fortune 500 manufacturer: 94% defect detection
Computer vision AI deployed across 12 production lines. $4.2M annual savings in year one.
Case Study
Top 20 bank: credit risk AI in 16 weeks
ML model reducing false positive rates by 67%. Deployed across 3 business units.
Case Study
Healthcare system: GenAI clinical documentation
40,000 clinicians on AI-assisted documentation. 2.1 hours saved per clinician per day.
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