01
Problem discovery
Where in the business is AI most likely to earn its keep? We work with leadership and the team doing the work to find the high-leverage problems worth solving with AI.
AiaaS – AI as a Service
AI as a service, built around how your business actually works.
AI agents, custom workflows, and bespoke integrations built around how your business actually works. From content engines to ops automation, we ship AI that pays for itself in week one.
Most businesses know AI matters. Few know what to do with it.
AI tools are everywhere. Pilots that never ship, prompts that never scale, and budgets spent on platforms nobody adopts. The gap between knowing AI matters and actually getting value from it is where most businesses get stuck. The cost of staying stuck compounds quarter by quarter.
We build AI systems that earn their keep for established businesses across Singapore, Indonesia, and Australia. Custom agents, content engines, and workflow automation scoped against real business problems, not generic use cases. Practical, owned, and built to pay for itself.
AI Content Creation
AI-assisted content production at scale, designed and supervised by humans. We use AI to compress timelines and expand output, not to replace the editorial judgement that makes content actually work.
What AI content covers
Ad creative
Static and video ad creative produced through AI generation pipelines, art-directed by humans. Used to test more variants more cheaply, then scale the winners.
Imagery & photography
AI-generated and AI-enhanced imagery for marketing, ecommerce, and brand work. Built to brand spec, used where stock photography fails and traditional shoots aren’t viable.
Video production
AI-assisted video production for short-form social, ads, and explainer content. From AI-generated B-roll to full AI video for specific use cases where it works.
Content marketing at scale
AI-supported editorial pipelines for content-heavy businesses. Briefs, drafts, optimisation, and review handled with the right human-AI workflow at each stage.
AI Tool Development
Custom internal tools built on top of large language models, integrated with your data, hosted on your infrastructure. The work that gives your team real leverage instead of another subscription.
What AI tools we build
Document & contract analysis
Tools that read, summarise, and analyse documents at scale. Useful for legal review, due diligence, RFP analysis, and any process that involves a lot of long-form reading.
Internal research assistants
Custom RAG systems trained on your company’s knowledge base, documentation, and historical decisions. The research assistant that actually knows what your team has done.
Workflow automation
AI-driven automation of the repetitive workflows that fill the day: triage, classification, drafting, summarisation. Not replacements, but force multipliers.
Customer-facing tools
AI features built into your product or website where they earn their place: search, recommendation, support, personalisation. Built to a specification, not as a buzzword.
AI Integration
The connective tissue between AI capability and your existing systems. We integrate AI into the tools your team already uses, on infrastructure you control, with the security and governance the business actually needs.
Integration scope
Claude Code & developer AI
Setting up developer teams with Claude Code, Cursor, and the AI coding tools that change how engineering work gets done. Tooling, training, and the workflow design.
MCP integrations
Model Context Protocol integrations connecting AI assistants to your tools: Slack, Asana, GitHub, internal systems. The plumbing that makes AI useful in real workflows.
AI content pipelines
Building the editorial and asset production pipelines that combine AI generation with human review. Designed to scale output without breaking quality control.
Custom API integration
Direct API integrations with Anthropic, OpenAI, and other model providers, on your infrastructure. Custom logic, fine-tuned prompts, and the engineering work that makes a working application.
Strategy & adoption
Most AI projects fail at adoption, not at the build. We work with leadership to identify where AI actually creates leverage, scope the right pilots, and run the change management that gets the team using the work.
How adoption gets done
AI strategy & roadmapping
Where AI fits in the business, what to build, what to buy, what to skip. A written roadmap mapped against business priorities, not vendor demos.
Pilot scoping
Identifying the right starting projects: small enough to ship, big enough to matter, structured to learn. Most AI failures start with bad pilot selection.
Team training & adoption
Workshops, documentation, and the change management work that gets the team using the tools. Without adoption, even the best build is wasted spend.
Governance & security
AI policies, data handling, model review, and the governance work that lets the business move fast safely. Especially relevant for regulated industries and customer data.
Our approach
AI is a process, not a product.
The companies getting real leverage from AI aren’t buying tools. They’re building applications that fit their specific workflows, on their infrastructure, with their data.
Our process is built to identify where AI actually creates value in your business, build the right thing, and get the team using it.
02
Technical scoping
Architecture, data flows, model selection, infrastructure, and the security and governance considerations. Done before we write code, not after.
03
Build
Engineering the application end to end. Custom tools on your infrastructure, integrations into your existing stack, and the LLM logic that does the actual work.
04
Deploy
Rolling the work out to the team that needs it. Integration with existing tools, access controls, monitoring, and the production hardening that makes AI workloads reliable.
05
Train & hand off
Documentation, workshops, and the training that gets the team comfortable with the new tooling. Plus a support handover so the build doesn’t depend on us forever.
What we can do for you.
Engagements scope to where AI actually fits the business. Some clients want a single tool built and deployed. Others want a multi-quarter AI roadmap with content, tools, and integration work running in parallel. Here’s the full set of capabilities we bring to AI work.
AI strategy & roadmapping
Where AI fits in the business, what to build vs buy, and the prioritised roadmap that makes the path clear.
Custom LLM tool development
Custom AI applications, internal tools, RAG systems, and AI features integrated into your product or stack.
AI content production
Editorial and asset pipelines that combine AI generation with human review, designed to scale output without losing quality.
Tool selection & setup
Helping teams pick the right AI tools, set them up, and integrate them with existing workflows. Tooling decisions made on technical merit, not vendor relationship.
Team training & adoption
Workshops, documentation, and ongoing support to get teams comfortable with new AI tools and capable of getting real leverage from them.
Ongoing support
Retainers for AI ops, model updates, prompt iteration, and the maintenance work that keeps AI applications working as models and tools change.
Recent work
AI work that moved the numbers.
Get a free
AI audit.
Request a free AI audit ↗
The audit covers:
- Workflow opportunity mapping for AI leverage
- Current AI tool stack assessment and gap analysis
- Data readiness review for AI applications
- Integration and infrastructure assessment
- Team capability and adoption readiness
- A written brief on the highest-impact AI opportunities to address first
Frequently asked questions about AI.
AI as a Service. Three things: building custom AI tools that run on your infrastructure, producing content at scale using AI, and helping your team adopt the AI tools that already exist. Some clients work with us on one. Others combine all three over time as their AI practice matures.
No. Most clients come to us precisely because they don’t have one. Part of every engagement is making sure your team finishes capable of running what we built without us being the bottleneck. The goal is capability, not dependency.
Yes. For tool development, we deploy on hardware you control: local servers, your AWS, your Azure, or your GCP. The model runs on your infrastructure. Your data never leaves your perimeter unless you decide it should. For businesses in finance, healthcare, legal, or any industry where data sensitivity is real, that’s the only sensible setup.
Depends on the problem. Open-weight models like Gemma, Llama, and Mistral for local deployment. Frontier models like Claude, GPT, and Gemini where they’re appropriate and the data sensitivity allows. We pick the right tool for the job, not the one we know best.
Discovery and scoping is typically 2 to 4 weeks. Build phases run 6 to 16 weeks depending on complexity. Deployment and training adds another 2 to 4 weeks. Most engagements complete inside one quarter, with the option to keep iterating once the foundation is in place.
Yes. The model weights, the code, the infrastructure, all yours. We build, we hand over, you keep. If we ever stop working together, you keep what we built and the documentation to extend it. No platform lock-in.
Yes, where it makes sense. Sometimes a fine-tuned model is the right answer. Sometimes a well-designed retrieval system on a base model performs better at less cost. We test both before recommending either, and we’re upfront about which approach fits your data, your scale, and your budget.
Tool Development means building something new for a specific problem. AI Integration means setting up tools that already exist (Claude Code, MCPs, content pipelines, APIs) and configuring them around your team’s workflows. Most clients need both eventually, but they’re scoped separately so you can start where it makes sense.
Tool Development is fixed-fee, scoped after the discovery phase. Content Production runs on a monthly retainer. Integration is project-based with a defined scope. We disclose the model upfront and don’t charge by the hour for work where the hours aren’t the point.
No, and we don’t take projects pitched on that premise. AI is most useful when it changes what your team can do, not who’s in your team. We design around augmentation, not replacement. If a project comes to us framed as headcount reduction, we’ll usually decline it.