KENNY CHIEN / CONSULTING
Enterprise AI Consulting
From pilot purgatory to production — three ways to get AI shipped, measured, and owned by your own team.
What is enterprise AI consulting for the agentic era? It is the work of taking AI from a promising demo to software that runs part of your business — with the evaluation, security, permissions, and trained people that production demands. That is the work I do: not strategy decks about AI, but AI applications in production and teams that can build the next one without me.
I offer three ways to engage: launching one AI application end to end, designing the agentic architecture and ontology your enterprise will run on, and embedding with your engineers as a forward deployed engineer (FDE). Different entry points, one thesis: the companies that win the next decade are the ones whose people — not just whose tools — learn to think in agents.
“The deliverable is not a deck. It is a system in production and a team that can run it without me.”
Three ways to work with me
AI Application Launch
One high-value use case, taken from whiteboard to deployed application — with your team trained along the way.
6–12 WEEKS · OUTCOME-BASED→02Agentic Architecture & Ontology
Design the ontology your enterprise will run on: entities, actions, permissions — and the agents that traverse them.
ADVISORY · ARCHITECTURE→03Forward Deployed Engineering
I embed with your engineers as an FDE — pairing, reviewing, and shipping until agentic patterns become muscle memory.
EMBEDDED · HANDS-ON→How I run engagements
Every engagement, whatever its shape, runs on the same six principles. They are not aspirations; they are the operating rules that decide what gets built, in what order, and by whom.
Economics before architecture
We model the value of the use case first — minutes saved, revenue unlocked, risk retired — and let that number discipline every technical decision.
Evaluation is the product
Before the first prompt, we build the eval set: golden tasks, failure taxonomies, acceptance thresholds. A system you cannot score is a system you cannot ship.
Smallest deployable loop
We ship a thin, end-to-end slice to real users in weeks, then widen it — never a six-month build toward a big-bang reveal.
Your team writes the code
My job is leverage, not dependence. Your engineers pair on every component and own the system from day one of production.
Ontology-first integration
Integrate meaning, not just data. Model the business objects and actions once, and every later application inherits them.
Go-lives over slideware
Progress is measured in working deployments and trained operators — never in decks delivered.
Technologies and stack
I do not resell a platform, and no engagement is tied to a vendor. The work runs across whatever your stack requires — these are the layers every serious agentic system ends up needing, and where the engagements operate.
Claude, GPT, and open-weight models. Engagements are model-agnostic by design: the ontology and evaluation layer let you swap models without rebuilding the system.
Grounding agents in your documents, systems, and institutional knowledge — so answers cite sources instead of inventing them.
Agent loops that plan, act, recover, and report — including sub-agents, human-in-the-loop checkpoints, and long-horizon workflows.
Typed interfaces to the operations agents may invoke — each with preconditions, effects, and reversibility, so capability is granted, never improvised.
Eval harnesses with golden tasks, failure taxonomies, and acceptance thresholds — rerun on every model upgrade and every prompt change.
Tracing, cost envelopes, and rollback paths. The unglamorous instrumentation that separates demos from systems.
Who may an agent act as, on what, within which limits — with a decision trail for when audit, legal, or an angry customer asks why.
Where agentic AI pays off
The use cases that survive contact with production share a shape: measurable economics, real data, and operations an agent can be safely permitted to perform. These are the families of work where engagements typically land.
Document & knowledge workflows
Contract review, policy questions, research synthesis — agents that read at scale, ground every answer in sources, and route exceptions to people.
Customer operations
Triage, case summarization, drafting, and resolution — with permissioned actions and human checkpoints where the stakes demand them.
Engineering acceleration
AI coding workflows, migration and refactoring agents, and test generation — the same discipline I teach in the Vibe Coding Bootcamp, applied to your codebase.
Back-office automation
Reconciliation, reporting, compliance checks — operations with clear preconditions and audit trails, which is exactly what agents need to act safely.
Trends and field notes
The pattern across current engagements: models are becoming a commodity, while ontology and orchestration are becoming the durable assets. The teams pulling ahead treat evaluation as engineering, not an afterthought — and they learn fastest by shipping.
Frequently asked questions
How is this different from working with a big consultancy?
The deliverable is different. Large consultancies sell strategy and staff; I ship working software inside your team and measure the engagement by what runs in production after I leave. You get one senior practitioner, not a pyramid of juniors — and your engineers write the code with me, so the capability stays.
What does an engagement cost?
Every engagement is scoped individually against the value of the use case, so there is no rate card to publish. Email kenny.chien@gmail.com with a paragraph on what you are trying to ship and I will give you an honest read on scope and cost.
Do you work remote or on-site?
Both. Most engagements run remotely inside your existing cadence — your repos, standups, and channels. On-site time is scoped per engagement where the work genuinely benefits from it, typically kickoff workshops and go-lives.
Which service should we start with?
If you have one high-value use case, start with an AI Application Launch. If the open question is architecture — how agents should represent and act on your business — start with Agentic Architecture & Ontology. If the goal is team capability, start with Forward Deployed Engineering.
Are you tied to a specific vendor or platform?
No. I do not resell a platform, and engagements are deliberately model-agnostic. We build the ontology and evaluation layer so you can swap frontier models — Claude, GPT, or open-weight — without rebuilding the system.
Not sure where to start?
Send one paragraph on what you are trying to ship and what it costs you today. I will reply with an honest read on which engagement fits — or whether you need one at all.