Production AI implementation
Fixed scope, a price agreed in writing before kickoff, and the evals, approval gates, and runbooks that get it through security review. Not a pilot — a system your team runs on Monday.
Delivery standards we build into every engagement — not client claims.
Engagement patterns
These are anonymized and representative engagement patterns. Named customer stories are published only after customers approve disclosure.
A B2B support workflow that answers repeat questions from approved knowledge, customer account state, and policy constraints while escalating low-confidence cases to humans.
Target result: faster first response with fewer tier-1 tickets reaching senior agents.
A document pipeline that extracts key clauses, compares them against a company playbook, and routes exceptions to legal with source-page provenance.
Target result: reduce manual review hours while preserving legal control over final decisions.
A post-call workflow that turns transcripts into buyer signals, CRM updates, and sales follow-up drafts while keeping reps in charge of what gets sent.
Target result: cleaner CRM data and faster follow-up without risky automated sending.
"The useful proof is not a flashy demo. It is a working workflow with evals, escalation, and a runbook."
Kevin Owens · Production launch principle"Governance belongs in the build, not in a cleanup meeting after the prototype already spread."
Kevin Owens · Governance principle"The model is one component. The operating system around it decides whether the AI work holds up in production."
Kevin Owens · Operating principleServices
4–8 weeks · Fixed scope
A single AI system, fully scoped, designed, built, and shipped on a fixed timeline and budget. We bring engineering, evals, and production discipline; you bring workflow knowledge.
Examples: support agents, document review, internal Q&A, sales call analysis, workflow automation.
Scope a sprintOngoing retainer · 20–80 hrs/mo
Dedicated senior AI engineering bandwidth on retainer. We become your AI team — building new systems, optimizing existing ones, running evals, and handling production incidents.
Includes: engineering hours, monthly evals, quarterly reviews, on-call coverage.
Discuss a retainer4–6 weeks · Fixed fee
AI governance, security, and operating-model readiness. We design policies, audits, and infrastructure so your AI work survives security review and scales without surprises.
Includes: governance design, security review, vendor policy, eval infrastructure, executive briefing.
Review your readinessOur Process
01
One call to map the use case, technical constraints, and success metrics.
02
A detailed scope document: architecture, milestones, price, team.
03
Architecture, eval plan, and production criteria locked in.
04
Weekly stakeholder checkpoints; live demo at every milestone.
05
System live with monitoring, runbook, and 30 days of post-launch support.
06
Knowledge transfer to your team, or transition to a fractional engagement.
Why Us
Through ProductExec, our sister practice for AI value creation in PE portfolios, a business case proven in one company moves straight into the build without re-briefing.
Every system ships with evals, monitoring, fallback paths, and runbooks. No magic prompts that break next week.
Designed for thousands of repeated runs, not impressive single demos.
Audit trails, security review, escalation, and compliance from day one.
Claude, OpenAI, open-weight; routed by task criticality, not vendor loyalty.
We've run the systems we ship. We know what production really means.
Security and data handling
How every engagement handles access and data, before anything is built.
Builds run in your own cloud accounts and under your access controls.
Your data stays in your environment, not ours.
We use model providers under enterprise or API terms that don't train on your data.
The studio gets only the access the work needs, and it is removed at handover.
Code, prompts, evals, and infrastructure-as-code are handed over and owned by you.
We sign your NDA before the first scoping conversation, and your DPA before any of your data is in scope.
About
Enterprise AI Studio is led by Kevin Owens, a four-time Chief Product Officer whose operating background spans B2B SaaS, productivity, healthcare workflow, and AI-enabled operating-model work, most recently as AI transformation lead inside a PE-owned software company. We work alongside ProductExec, our sister practice, and bring in specialist engineers for each engagement. Connect with Kevin on LinkedIn.
Scope honesty
If the workflow and the metric aren't named, there's nothing to ship against.
Anything touching customers or regulated work ships with evals, gates, and a runbook — or it doesn't ship.
Approvals, audit trail, and rollback criteria are designed in week one, not retrofitted under security review.
Work that doesn't move a real workflow metric isn't worth your budget or our quarter.
If you need forty people and two years, hire a systems integrator. We'll tell you so on the first call.
FAQ
ProductExec works with private equity firms to find the AI plays that pay back across a portfolio and prove the business case in one company. Enterprise AI Studio is the team that builds it, in PE-backed companies and in enterprises that already know what they need. Many engagements use both: ProductExec proves the case, the Studio ships it.
No. We augment your team. We typically build the AI core, integrate it with your systems, and hand off to your team or stay on retainer for continued AI work.
We're model-agnostic. We pick the right model for each task — frontier models for judgment-heavy work, cheaper long-context options for synthesis, open-weight where appropriate.
Implementation sprints are 4–8 weeks of fixed scope for one workflow. Retainers run 20, 40, 60, or 80 hours a month. Governance programs are 4–6 weeks at a fixed fee. Every engagement is quoted in writing after a free 30-minute discovery call and, for sprints, a free one-week scoping phase. See how pricing works.
Yes. We sign your NDA before any scoping conversation, and your DPA before any of your data is in scope.
Builds run in your own cloud accounts, under your access controls, and your data stays in your environment. We use model providers under enterprise or API terms that don't train on your data. The studio gets least-privilege access, removed at handover. The code, prompts, evals, and infrastructure-as-code are handed over and owned by you. We sign your NDA and DPA. More in security and data handling.
Governance is baked into everything we build. We include audit trails, security reviews, role-based access control, and escalation protocols from day one. If your organization has compliance requirements (SOC 2, HIPAA, etc.), we design the AI system to support them, not work around them.
Yes. We typically own the entire integration — APIs, webhooks, database connections, CRM/ERP linking, whatever your stack requires. We ensure the AI system fits seamlessly into your workflows without requiring your team to rewire their operations.
That's common. Our discovery call is free and specifically designed to map your constraints and uncover high-leverage use cases. We help you prioritize what to build first, then scope the work you want to move forward on.
Get in Touch
Tell us about your use case. We'll map the constraints and the fastest path to production — no sales deck, just a direct conversation.