Boutique AI Consultancy, Singapore

AI adoption,
agents & compliance
for engineering teams.

Get AI past the pilot stage: shipped into production, governed, and measurably moving the business.

Or request a 60-90 min hands-on agentic SDLC demo, no commitment.

Founded by the former Mercer AI Adoption & Engineering Enablement team for APAC. We led AI rollouts across engineering teams and software leadership across the region.

Est. 2025
Selected engagements
HoverBotAI-native chatbot platformPlatform architecture & agent workflows
LabCaddyScientific product platformConversational AI search
B2B SaaS · Series B15-person engineering org · APACGoverned coding-agent rollout
Global professional services5,000+ employees · multi-jurisdictionPrivate LLM platform & knowledge retrieval
OpenAIAnthropicAzure AIAWSCursorClaude CodeGitHub CopilotVercelLangChainRAG Pipelines
OpenAIAnthropicAzure AIAWSCursorClaude CodeGitHub CopilotVercelLangChainRAG Pipelines
What we do

Adoption, agents, compliance.
the three pieces that ship AI together.

01

AI Adoption & Team Enablement

Train teams to use AI tools, copilots, and coding agents effectively.

  • Readiness assessment
  • Tooling selection
  • Hands-on enablement
  • Pilot design
Situation

Teams with ad-hoc AI usage struggling to standardise engineer adoption.

Explore AI Adoption
02

Agentic Systems & AI Employees

Define your agentic system strategy and deploy AI agents that work inside your existing workflows.

  • Coding agents
  • AI employees
  • GenAI / ML feature rollout into production
  • Evaluation harness
Situation

Teams shipping first production agents or rolling out coding agents.

Explore Agentic Systems
03

Compliance-Ready AI Operations

Governance designed alongside the tooling, before the first audit.

  • Governance framework
  • SOC 2 readiness
  • Data controls
  • Audit trails
Situation

Teams putting AI in audit scope for enterprise and regulated buyers.

Explore Compliance & Governance
Why VG Tech Consulting

Built by practitioners who shipped AI
inside real engineering orgs.

Special focus on APAC entrepreneurs and regional SMEs. Deep familiarity with the regulatory landscape, market dynamics, and operational constraints of building and scaling technology companies across the Asia-Pacific region.

01

A model that survived real rollouts

The six-step arc isn't a theory. It's how AI got shipped inside 5,000+ employee enterprises and 15-engineer scaleups alike, adapted each time to the team's constraints.

02

Engineering-led delivery

Grounded in software architecture, delivery practices, and hands-on implementation. The work happens alongside your engineers, not above them.

03

Governance wired in from day one

Audit trails, access controls, and compliance documentation are designed alongside the tooling, so enterprise security questionnaires don't trigger a scramble.

04

Tool-agnostic, client-stack first

No vendor affiliations, no preferred stack to push. Existing tooling stays where it works; change is recommended only when a different tool meaningfully improves the outcome. Low learning curve for the team.

How we work

No slideware. Six concrete steps,
each with something shipped at the end.

01

Assess Readiness

Map the team's current state: tooling, skills, workflows, and risk tolerance.

02

Prioritise Use Cases

Pick the 2-3 AI bets most likely to deliver value without overwhelming the team.

03

Design Workflows & Controls

Agent workflows, guardrails, and integration patterns, designed together with your engineers.

04

Train Teams & Launch Pilots

Hands-on enablement inside the team's sprint cadence. Engineers ship AI-assisted work on their own backlog from week one.

05

Add Governance & Evidence

Audit trails, access controls, and documentation, wired in before enterprise buyers ask.

06

Scale What Works

Productionise the pilots that proved value. Retire the rest.

Selected work

From 15-engineer scaleups
to 5,000+ employee enterprises.

AI workflow transformationB2B SaaS · Series B · 15-person engineering team · APAC

Engineering AI Adoption

Challenge

A software company wanted to adopt AI across engineering in a practical way, but needed the right workflows, training, governance, and rollout model to make it useful and compliant.

Approach
  • Reviewed engineering processes and delivery patterns
  • Created an AI adoption plan for the organization
  • Trained engineers to use AI tools effectively
  • Supported compliance-oriented process improvements
  • Introduced AI-assisted UI workflows using Figma-connected tooling
  • Implemented first-pass AI PR reviews with human escalation
  • Deployed Claude Code skills for test generation and automation
Outcome

Within 8 weeks, moved AI usage from ad hoc experimentation to governed operational practice. PR review cycles shortened by ~60%, first compliance audit passed without findings.

AI adoption consultingWorkflow automationCoding agentsTeam enablementCompliance-aware rolloutDeveloper productivity
Read case study
AI product architectureAI-native chatbot platform

HoverBot

Challenge

The client needed a production-grade AI platform that could support configurable chatbots, knowledge-grounded responses, and safe enterprise-friendly workflows.

Approach
  • Designed the platform architecture
  • Shaped the AI delivery model and team structure
  • Defined chatbot and retrieval workflows
  • Supported guardrails, knowledge controls, and operational design
  • Helped turn the concept into an AI-native product foundation
Outcome

Production-ready AI platform shipped in 10 weeks. Team independently shipping features within first month post-engagement.

AI architectureRAGChatbot designAgent workflowsAI team design
Read case study
Enterprise AI architectureGlobal professional services firm · 5,000+ employees · multi-jurisdiction

Enterprise AI Platform

Challenge

A large enterprise needed a secure, governed way for employees to use LLMs internally without exposing sensitive information or relying on uncontrolled public tools.

Approach

Designed the architecture for an internal AI platform. Recommended Azure-based private access patterns for LLM usage. Created centralized, controllable employee access. Introduced guardrails to reduce corporate data leakage risk. Connected retrieval systems to SharePoint-based knowledge sources. Enabled AI search across millions of internal documents. Trained employees to use the platform effectively.

Outcome

Rolled out governed LLM access to 5,000+ employees in 12 weeks with zero data incidents. Internal knowledge retrieval latency dropped from minutes to seconds.

AI architecturePrivate LLM deploymentEnterprise guardrailsRAGKnowledge retrievalAI adoption training
Read case study
Domain-specific conversational AIScientific product platform

LabCaddy

Challenge

The client needed a more intelligent way for users to discover science-related products and interact with product information through conversation, not just keyword filtering.

Approach

Designed a custom AI conversation flow for scientific use cases. Built an AI-powered search layer for science-related products. Enabled chatbot-driven search and product discovery. Aligned the system around domain-specific language and workflows.

Outcome

Conversational AI search deployed across the full product catalog. Product discovery conversion improved measurably over keyword-only filtering.

Custom AI solutionsAI searchConversational AIDomain-specific workflows
Read case study
Team

Engineers, architects, enterprise leaders.
builders helping others ship at scale.

VG Tech Consulting combines delivery leadership with enterprise market, cloud, operations, and data expertise. Every engagement is shaped by people who have built, led, and scaled technology work in complex environments.

Portrait of Alexander Khomenko
01

Alexander Khomenko

Technical Lead

Leads architecture and delivery for robust, scalable software systems. Alexander brings experience across enterprise platforms, SaaS products, startups, and R&D, with a strong focus on mentoring engineers, improving delivery quality, and turning complex technical work into shippable product.

ArchitectureDeliveryEngineering leadership
Portrait of Eugene Zozulya
02

Eugene Zozulya

Strategic Advisor, APAC & North America Enterprise Markets

Advises on enterprise market strategy, CxO engagement, and expansion across APAC and North America. Eugene brings 20 years across Microsoft, PwC, IBM, and SAP, with deep experience in enterprise AI, cloud platforms, public sector transformation, and ASEAN Microsoft solution practices.

Enterprise AICloud platformsMarket strategy
Portrait of Dr. Vithyatheri Govindan
03

Dr. Vithyatheri Govindan

Strategic Advisor, Operations & Data Analytics

Advises on operational excellence, scalable SaaS support, and data-informed service delivery. Vithya brings 20+ years across SaaS, support, operations, and professional services, combining machine learning and data analytics depth with empathetic leadership across global APAC and EMEA teams.

SaaS operationsData analyticsGlobal teams
Frequently asked

Straight answers
to the questions clients actually ask.

01
What does VG Tech Consulting do?

VG Tech Consulting helps software companies adopt AI across engineering teams. The firm provides structured rollouts of AI tools (coding agents, LLM assistants), designs and deploys AI agent workflows, and implements compliance-ready AI governance frameworks. Based in Singapore, serving companies across APAC.

02
What is an AI Adoption Diagnostic?

A 60-minute session to pressure-test your AI adoption approach. The review covers current state (tooling, workflows, governance) and identifies the two or three gaps most worth closing first. No salespeople, no slides. You leave with a short written summary of where things stand and what would happen first.

03
What does a typical engagement look like?

Engagements come in four shapes. A Diagnostic is a focused 60-minute review with a written follow-up. A Hands-on Demo is a 60-90 minute walk-through of an agentic SDLC setup in a real repository, with no commitment required. A Sprint is a 2-8 week hands-on engagement to design and ship specific workflows or governance. An Embedded Retainer puts a dedicated senior consultant alongside one or two of your engineers under your engineering leadership, on monthly time-and-materials, typically in a 12-month shape, with a standard baseline of around 35 hours per week.

04
How is your team structured?

Every engagement is led end-to-end by a senior partner. The person you scope with is the person you ship with, across the full contract. No delivery-management layer, no rotating bench. Delivery capacity is amplified the same way clients are advised to amplify theirs: with AI agents running inside a guardrailed SDLC (the same agentic patterns installed for clients), and a vetted specialist network selectively brought in when an engagement benefits from extra bandwidth on a specific problem. Clients always work with, and are accountable to, the senior partner.

05
Can we try a demo before committing?

Yes. A 60-90 minute hands-on demonstration of an agentic SDLC workflow is available, from branch protection through AI code review and autonomous agents, walked through in a real repository. It is the fastest way to see whether the approach fits your team. No commitment, no slides.

06
Who owns the intellectual property created during the engagement?

You do. All work product created during the engagement (tools, code, documentation, configurations, prompt libraries, and custom integrations) belongs to the client. The standard contract assigns IP to the client on delivery. No residual rights are retained over client-specific work.

07
How much of our engineering team’s time will this take?

During assessment, developer time is capped at around 1-2 hours per person per week. A single point of contact on the client side coordinates deliverables, and structured onboarding is prepared upfront so context is not repeated across sessions. All tool and process changes go through a small-group proof of concept before team-wide rollout.

08
Which AI tools and platforms do you work with?

Tool-agnostic, with no vendor affiliations. Engagements span the modern AI tooling landscape, including OpenAI, Anthropic (Claude), Azure AI / Azure OpenAI, AWS Bedrock, GitHub Copilot, Cursor, Claude Code, Codex, LangChain, RAG pipelines, Azure DevOps and GitHub Actions automation, and MCP integrations. The default is to work within your existing stack and reuse tools the team already knows, so the learning curve stays low. A different tool is recommended only where it meaningfully improves the outcome, with the team supported through the transition.

09
Can we talk to past clients, and how do you handle confidentiality?

Mutual NDAs are standard on every engagement, and enterprise engagements are under NDA, so client names are not disclosed publicly. Case studies on this site are either fully named (with written permission) or anonymised to a descriptor level that reveals nothing about the client beyond industry shape and scale. Client code, data, prompts, and configurations are never shared outside the engaged team without explicit written permission. During evaluation, four options are available: (1) anonymised reference calls with past clients under mutual NDA, (2) named reference conversations with the founders of HoverBot and LabCaddy who have publicly consented, (3) professional references for the partners via LinkedIn, and (4) a 60-90 minute hands-on demonstration of the practice in action.

10
Where are you based and who do you serve?

Based in Singapore, serving companies across APAC — from Series-A startups to global firms with 5,000+ employees. The founding team led AI adoption and engineering enablement programmes at Mercer across the region, with deep familiarity in the operational, regulatory, and market dynamics of scaling technology organisations in Asia-Pacific.

Going through security, legal, or procurement review? NDA posture, data handling, IP terms, and reference policy are documented in one place.

Trust & working model
Start here

Planning AI adoption, internal agents, or compliance-ready rollout?

Book a 60-minute AI Adoption Diagnostic. A direct conversation about your team, your constraints, and where to start.

No commitment, no salespeople. Or email alex@vgtc.io
Reference calls available under mutual NDA during evaluation
Not ready for a call?

Get the AI Adoption Checklist. 12 questions every CTO should answer before rolling out AI tools to engineering teams.