HeyKiko
HeyKiko is our own private RAG platform, and the assistant in the corner of this page is running on it — answering from our own content, with citations. It is the least abstract demo we can offer: try to catch it out.
AI & business automation
AI and business automation in Singapore
Retrieval-augmented knowledge systems, AI agents and document intelligence — deployed where your data already lives, with every answer traceable to a source.
RAGAI agentsVector searchLLM integrationSelf-hosted modelsDocument extractionOCRPythonWorkflow automationOn-premisePrivate cloudPDPA-aware
0Deployment options
0Answers cited
0Pilot length
0Data leaves site
The problem
The blocker is rarely the model. It is trust
Staff already paste internal documents into public chat tools. Blocking that does not remove the risk — it moves it to a personal phone.
Two objections stop most enterprise AI projects, and neither is about accuracy in the abstract. The first: nobody can tell where an answer came from, so nobody will act on it for anything that matters. The second: nobody is confident the question did not leave the building.
Both are solvable, and solving them is most of the work. Retrieval-augmented generation grounds answers in your own documents and cites the passage used, so an error is visible and checkable rather than silent. Self-hosted or private-cloud deployment keeps the content inside your perimeter. Neither is a guarantee of correctness — which is why we design the workflow so a human stays accountable for consequential decisions.
Where we start: not with a model, but with the question of which decision in your business is currently made badly because nobody has the information in front of them. If there is no such decision, there is no AI project worth funding.
What we do
What we build
Practical systems rather than pilots that impress once and are never opened again.
Private RAG knowledge systems
An assistant that answers from your SOPs, policies and contracts, cites its source, and says nothing when it does not know.
AI agents & workflow automation
Multi-step processes — triage, routing, drafting, data entry — with defined boundaries and a human approval point where it matters.
Document intelligence
Extraction and classification from invoices, forms, contracts and scanned records, with confidence scoring and a review queue for the uncertain cases.
Predictive analytics
Demand forecasting, predictive maintenance and anomaly detection where you have enough history to make a model worth trusting.
Enterprise search
Semantic search across the file shares, wikis and ticket systems your people currently give up on.
Private deployment
On-premise, in your own cloud tenancy, or managed by us in Singapore, including self-hosted open-weight models for air-gapped sites.
Deliverables
What you actually receive
Everything below is yours to keep, and to take elsewhere if you ever decide to.
A use-case assessment that says plainly which ideas are worth funding
A working pilot on your own content within about three weeks
Citation enforcement, so every answer links to its source
Access controls inherited from your existing permissions
Query logs and an unanswered-question report to improve the corpus
Deployment documentation for your infrastructure team
Proof, not promises
We built this for ourselves first
The fastest way to judge whether we can build your system is to look at one we already run in production.
Signals
Where AI actually pays for itself
The same questions, endlessly
Policy, process and product questions interrupting the three people who know the answers.
Knowledge locked in documents
Hundreds of PDFs nobody reads, containing answers people need weekly.
Data that cannot leave
Regulated or sensitive material that rules out public AI tools entirely.
Common questions
What clients ask first
Retrieval-augmented generation looks up relevant passages from your documents and answers using them, citing what it used. An agent goes further and takes actions across multiple steps — querying a system, drafting a response, updating a record. RAG answers questions; agents do work. Most organisations should get RAG right first.
No. Your documents are indexed for retrieval, not used to fine-tune a shared model. In an on-premise deployment the content never leaves your infrastructure at all.
Ground answers in retrieved passages and enforce citation, so unsupported responses are suppressed rather than returned. That is a large improvement, not a guarantee — which is why we keep a human accountable for decisions with consequences.
Yes. Self-hosted open-weight models support air-gapped and highly sensitive deployments. There is a capability trade-off against the frontier hosted models and we will be straight with you about it.
About three weeks for a pilot: one to ingest and tune a defined set of documents, one for a small group to use it in anger, one to act on what that reveals.
It can, if personal data is in the corpus. Access controls, retention and purpose limitation all need thinking through — which is part of the engagement, and why our data protection practice sits alongside this one.
Related
Often needed alongside
Data services & PDPA
Governance and DPO cover for the data an AI system touches.
See data servicesHeyKiko platform
The private AI product this practice is built on.
See HeyKikoSystem integration
Connecting an assistant to the systems holding your answers.
See integration
Next step
Bring us the messy problem. We like those.
A 30-minute call, no deck, no obligation. Tell us what is breaking and we'll say honestly whether we're the right team to fix it.