Question 1

What do you build?

Accepted Answer

AI agents, local models, and AI workflows. Agents with memory, tools, and skills that do real work; open models deployed on your own hardware so data stays in-house; and LLM pipelines and automations wired into the tools your team already runs. Shipped work includes the Codex Astra/Luna Orchestrator, Coworker, AI Backends, and Local Evals.

Question 2

How does an AI engineering project work?

Accepted Answer

A one-hour scoping call on the problem, your data, and what done looks like. Then a proposal with deliverables, timeline, and engagement type: fixed project or retainer. Then I build, with working software early, regular demos, and production at the end.

Question 3

What is a Fractional CAIO?

Accepted Answer

A Fractional CAIO (Chief AI Officer) is a senior AI leader you engage part-time, usually on retainer: set the AI agenda, decide what to build and skip, put guardrails in place, and stay close enough that pilots become real systems, without a full-time C-level seat.

Question 4

What does an AI adviser do for a company?

Accepted Answer

A senior sounding board when you mainly need counsel rather than an ongoing seat. I review strategy, vendors, and build vs. buy, and pressure-test plans before you commit budget, as office hours or a fixed number of calls. It can convert into a Fractional CAIO retainer later.

Question 5

Who is this for?

Accepted Answer

Founders, CEOs, COOs, and CTOs who have something to build, want a senior engineer-exec in the loop, and are not ready for a full-time hire. More than a workshop, less than a $400k CAIO.

Question 6

How is this different from AI consulting or a workshop?

Accepted Answer

A workshop trains your team. Typical consulting delivers a deck and leaves. I build the system and, if you want, stay on the agenda with your leadership team while it ships.

Question 7

How is the engagement customized?

Accepted Answer

We agree on scope (a single build, advisory, or a retainer that mixes both), cadence (weekly, biweekly, or monthly), how involved I am with your team, and duration: a 30-day trial, a quarterly retainer, or a project with a finish line.

Question 8

Where are you based, and which markets do you serve?

Accepted Answer

Singapore, Southeast Asia’s AI hub, working with clients worldwide. Singapore hours cover Asia-Pacific; calls with US and European teams are scheduled at overlap-friendly times. Remote-first, with in-person sessions available in Singapore.

Question 9

Why does being Singapore-based matter?

Accepted Answer

Singapore is where AI in Asia is funded, governed, and deployed first: a national AI strategy, partnerships with OpenAI and Google, and frameworks like IMDA’s Model AI Governance Framework, AI Verify, and MAS guidelines that set the enterprise baseline for the region. I bring that frontline exposure to your build decisions and guardrails.

Question 10

How do I get started?

Accepted Answer

Book a one-hour scoping call. Bring a short company overview and the problem or decision that is stuck. Prefer to ask questions first? Message me on LinkedIn.

Service	Good for	What I can help with
AI Agents & Workflow AutomationBuild AI agents that understand requests, use tools, work with company data, and execute real workflows.	Good forCustomer support, research, document processing, operations, lead qualification, internal tools, and business process automation.	I can help with Agent architecture and implementation Tool and API integrations Human-in-the-loop approvals Multi-agent and subagent workflows Scheduled and event-driven automation Agent tracing and evaluation
Private & Open Model AIRun AI models on your own infrastructure when privacy, control, or cost matters.	Good forEvaluate and deploy open models such as Qwen, Gemma, and other small or large language models on local GPUs or cloud infrastructure.	Services include Open-model feasibility studies Local and private AI deployments llama.cpp, vLLM, and Ollama infrastructure GPU inference optimization Quantization and model packaging Hybrid local + cloud model architectures
AI Backend EngineeringTurn an AI prototype into a production-ready system.	Good forThe backend infrastructure needed to reliably operate AI applications at scale.	This can include Multi-provider LLM integrations Model routing and fallbacks Structured outputs Embeddings and semantic search OCR, vision, and document processing Authentication and rate limiting Observability and tracing Cost and latency optimization
Fine-Tuning & Small ModelsNot every problem needs a large language model.	Good forFor classification, routing, moderation, intent detection, extraction, and similar high-volume tasks, smaller specialized models are often faster and significantly cheaper.	I can help with Dataset preparation LoRA / SFT fine-tuning Classifier training ModernBERT and embedding models Evaluation pipelines ONNX and INT8 optimization CPU-friendly inference
AI Proof of ConceptHave an AI use case but not sure what architecture or model to use?	Good forA focused POC on your real workflow and data to determine what actually works before you invest in a larger implementation. Validate the approach with working software and measurable results.	A typical POC evaluates Cloud vs open models Prompting vs fine-tuning Agent vs deterministic workflow Model quality and reliability Infrastructure requirements Latency and operating costs
AI Engineering DiscoveryFor teams still figuring out where AI fits.	Good forA focused technical discovery engagement. You leave with a clear technical direction instead of a generic AI strategy deck.	We work through Map the workflow Identify where AI provides value Select the right models and architecture Define a practical implementation plan

AI Engineering Services

What I can help with

What is a Fractional CAIO?

Who this is for

Founders and exec teams

Past experiments, no agenda yet

Not ready for a full-time CAIO

How to engage

AI Engineering project

Fractional CAIO retainer

AI Adviser

Customized. Not a package.

Cadence & calls

Scope

Depth

Duration

Engineer-led. Not slide-deck-led.

AI Engineering and Fractional CAIO FAQs

Have something to build?