01 / Automation
Make work
flow better.
Connect your people, processes, and applications. Put the repetitive work on a better path.
CAPABILITIES 01—03↗
AI ENGINEERING & INFRASTRUCTURE
Intelligence.
Engineered
for impact.
We build the agents, automation, and infrastructure that turn AI into a working part of your business.
Singapore based. Built for the real world.
AIMS / INTELLIGENCE ARCHITECTURE01—10
+
01 / CONNECTYour systems. In sync.
02 / ORCHESTRATEIntelligence in motion.
03 / OPERATEBuilt for production.
CONNECTED BY DESIGNAPI · MCP · LLM
FROM THE FIRST WORKFLOW TO THE FULL AI STACKDiscover what’s possible ↓
Open standards.
Connected possibilities.
↗APIs & MCP⌘Agent orchestration◈Knowledge & RAG⊞Model infrastructure
Connected capabilities
The whole AI stack.
One engineering partner.
From the workflow your team repeats every day to the infrastructure behind your next product. We connect the pieces that make AI useful.
Make work
02 / AI engineering
Build intelligence
into your business.
Create agents and knowledge systems designed around the way your organisation works.
04Training, fine-tuning & RAG05Agent harness engineering06Agentic environments07Coding agents & CI/CD
CAPABILITIES 04—07↗
03 / Infrastructure
Run AI on
your terms.
Take control of model access, inference, performance, and the cost of operating at scale.
CAPABILITIES 08—10↗
Intelligence in practice
Built around the work.
Connected to the outcome.
See how the right combination of tools, models, and human judgement can move a real business process forward.
Less manual work. More forward motion.
Keep work moving across your business.
Connect incoming requests, documents, and business systems into an accountable workflow. Let automation handle the handoffs while people focus on exceptions and decisions.
- Fewer manual handoffs
- Visible exception handling
- A traceable path from request to result
WORKFLOW / 01Illustrative architecture
01Incoming request
Email, form, or event
02Understand & route
Extract, validate, classify
03Review & approve
Your policy. Your control.
04Update your systems
CRM, finance, operations
MEASURE WHAT MATTERS
Cycle time · rework · cost per completed task
Useful answers. Meaningful action.
Give every conversation the right context.
Build an assistant that understands your products, checks live information, and helps customers take the next step. Make escalation to your team a first-class part of the experience.
- Answers grounded in your knowledge
- Controlled actions through your tools
- A clearer handover to human support
WORKFLOW / 02Illustrative architecture
01Customer question
Web chat or application
02Retrieve context
Approved knowledge & policy
03Use approved tools
Check status. Prepare action.
04Resolve or hand over
With the context attached
MEASURE WHAT MATTERS
Answer quality · resolution · response latency
Faster iteration. Engineering discipline.
Give your engineers a capable AI teammate.
Connect coding agents to repository context, isolated workspaces, and delivery pipelines. Turn a scoped task into a reviewable change with the checks your team expects.
- More focused engineering time
- Reviewable changes with useful context
- A repeatable path through your quality gates
WORKFLOW / 03Illustrative architecture
01A scoped task
Requirements & repository context
02Agent workspace
Implement in an isolated runtime
03Validate & review
Tests, checks, human judgement
04Release with control
Your CI/CD and approval gates
MEASURE WHAT MATTERS
Lead time · rework · release reliability
Your stack, connected.
Work with the systems and data you already have.
Control, built in.
Clear permissions, human approvals, and useful visibility.
A clear handover.
Documented systems your team can understand and operate.
The way we work
Ambitious thinking.
Disciplined delivery.
A clear path from the first conversation to a system your team can depend on.
01Discover
Understand the work.
Map your workflows, systems, and constraints. Choose a valuable problem and establish what a better outcome looks like.
What you leave withOpportunity map + success criteria
02Design
Prove the approach.
Select the architecture and test the critical assumptions with your data. Agree on scope, ownership, and how progress will be measured.
What you leave withArchitecture + evaluated prototype
03Engineer
Build for reality.
Connect your systems, implement the controls, and test the edge cases. Make reliability, permissions, and observability part of the build.
What you leave withIntegrated system + validation
04Operate
Keep getting better.
Deploy with a clear handover. Use real usage, evaluation results, and cost visibility to guide the next iteration and ongoing support.
What you leave withDeployment + operational playbook
Frontier model access
The right intelligence.
The right terms.
From capable everyday models to frontier reasoning. Find an access arrangement that fits your applications, expected usage, and operating requirements.
Workload-led model selectionVolume-based enquiriesIntegration and usage planning
Explore model accessAvailable models, regions, and commercial terms are confirmed during quotation.
MATCH THE MODEL TO THE WORK
01
ReasoningComplex analysis & multi-step problems
02
CodingDevelopment & engineering workflows
03
MultimodalText, images & richer context
04
EmbeddingsRetrieval, search & knowledge
A clearer perspective
Make your next AI decision
an informed one.
RAG or fine-tuning? Start with the problem.
Understand when to use retrieval-augmented generation, when to fine-tune a model, and how to evaluate both against your business requirements.
Read the guideWhat is MCP, and does your business need it?
A practical introduction to Model Context Protocol, how it relates to APIs, and what to consider when giving AI agents access to business systems.
Read the guideLLM caching: what actually gets reused?
Understand KV caching, provider prompt caching, and semantic response caching, including where they operate and how to measure their real value.
Read the guideA little more clarity
Good questions. Clear answers.
Have something more specific in mind?
Talk it through with usWhat does AIMS build?
AIMS designs and implements AI automation, tool-using copilots, API and Model Context Protocol integrations, retrieval-augmented generation, model fine-tuning, agent harnesses, and the infrastructure to run them. We also help teams set up coding agents, CI/CD, LLM gateways, private inference, and model access.
Can you work with the systems we already use?
Yes. We start with your existing applications, data, infrastructure, and access controls. Depending on what those systems support, we connect through APIs, MCP servers, events, or other approved integrations. Discovery identifies any gaps before implementation.
Do we need a technical team to get started?
No. We can begin with a business process and a clear objective. For teams with in-house engineers, we can work alongside them on architecture and implementation. Every engagement includes an agreed handover and documentation scope.
Can AI run in our own infrastructure?
Yes, where the selected models and workloads support it. We scope on-premise, private-cloud, and hybrid inference around your data requirements, hardware, model licences, and operational capacity. Hosting location, external dependencies, and telemetry flows are considered together.
How are projects and model access priced?
Implementation is quoted against an agreed scope, integrations, and support requirements. Model access is quoted separately based on the provider, expected usage, region, and applicable terms. We discuss delivery costs and ongoing operating costs before you commit.
Where is AIMS based?
AIMS is based in Singapore, at 29 Carpenter Street, Singapore 059923. We work with businesses across Singapore and APAC, and welcome international enquiries.
From ambition to action
Your next advantage
starts here.
Bring us a workflow, a challenge, or an ambitious idea. Let’s find the right way to build it.