Private AI Knowledge Systems for Singapore Teams
A RAG knowledge system is an AI assistant that answers from your own documents with cited, auditable sources instead of guessing. MicroPyramid builds private RAG-powered copilots, support assistants, and multilingual document search for Singapore finance, logistics, legal, govtech, and SaaS teams — query your institutional knowledge in natural language, with source citations, role-based access, and every byte staying within Singapore infrastructure (AWS ap-southeast-1) or on-premise.
Built to the Singapore PDPA and PDPC guidance, and — for financial-services clients — the MAS Technology Risk Management guidelines and FEAT principles. Hosted in AWS ap-southeast-1 (Singapore) by default, or on your own infrastructure where MAS or sector rules demand it.
Why Singapore Organisations Need Private RAG
Singapore financial institutions, growing SaaS companies, logistics and maritime operators, insurers, and public-sector bodies accumulate enormous volumes of knowledge — often across English, Mandarin, Malay, and Tamil — yet most of it sits inaccessible in shared drives, email threads, and PDF archives. The PDPA's accountability obligation makes each organisation responsible for how personal data is handled, including the moment it is indexed into an AI system.
Private RAG systems solve this cleanly. Documents are indexed and retrieved within your own environment; LLM inference runs on-premise or in AWS ap-southeast-1 (the Asia Pacific Singapore Region). Nothing has to leave Singapore. For MAS-regulated financial institutions — required to keep customer data within approved jurisdictions — that in-region or on-premise design is the difference between a usable tool and a licensing problem.
We've been building secure, production AI systems for 12+ years. We know that "compliant" for a MAS-regulated institution or a govtech team means more than a terms-of-service checkbox — it means auditable architecture, documented data flows, source-cited answers, and alignment with the IMDA and PDPC Model AI Governance Framework for Generative AI. That's how we build.
What We Build for Singapore Teams
Six types of private RAG-powered knowledge systems, each shaped around Singapore compliance, data-residency, and sectoral needs
Internal Knowledge Copilot
Give your Singapore team a private retrieval assistant over internal policies, SOPs, and compliance guides — with citations, role-based access, and audit trails that satisfy the PDPA accountability obligation enforced by the PDPC.
- Document ingestion pipeline
- Semantic retrieval with citations
- Role-based access control
AI Support Assistant
Turn your support docs, product FAQs, and ticket history into an intelligent first-line assistant — built for Singapore SaaS and fintech teams handling high support volumes across Singapore, ASEAN, and the wider APAC region.
- Knowledge ingestion & indexing
- Retrieval-backed answers
- Fallback & escalation logic
Enterprise Document Search
Replace keyword search with semantic retrieval across contracts, regulatory filings, tender documents, and technical specs — in English, Mandarin, Malay, and Tamil, built for the document-heavy realities of Singapore logistics, maritime, legal, and real-estate firms.
- Semantic search & ranking
- Multilingual support (English, Mandarin, Malay, Tamil)
- Filters & faceted navigation
Financial Services & MAS Compliance Q&A
Secure retrieval over MAS guidelines and notices, internal compliance manuals, and product disclosures — cited answers that reduce the risk of misinterpreting requirements and cut compliance research time for MAS-regulated firms.
- Regulatory document retrieval
- PDPA-aligned access controls
- Audit logging
Private Document Q&A
Access-controlled Q&A over sensitive documents — client contracts, board papers, PDPA processing records, and legal briefs — deployed in AWS ap-southeast-1 (Singapore) or entirely on your own infrastructure where MAS or sector rules require it.
- On-premise or private cloud
- Singapore data residency (ap-southeast-1)
- Audit logging
Secure RAG with Citations
Every answer is attributed to its source with page-level citations — auditable, trustworthy, and safe for Singapore regulated sectors from fintech and banking to insurance, healthcare, and govtech.
- Source-attributed answers
- Confidence scoring
- Hallucination mitigation
Where Singapore Teams Get ROI
The strongest use cases share one trait: large, growing bodies of knowledge that people need to query — without waiting for a colleague
Financial Services & MAS
Secure retrieval over MAS guidelines and notices, internal compliance manuals, and product disclosures for firms governed by the MAS Technology Risk Management guidelines and FEAT principles — cited answers reduce the risk of misreading a requirement.
Logistics, Maritime & Supply Chain
Help operations and trade teams search across shipping documents, customs filings, PSA and port workflows, and project specs — without emailing colleagues or digging through shared drives across multiple languages.
Legal & Contract Intelligence
Let Singapore legal teams and in-house counsel query contracts, matter files, and statutes in natural language. Answers are cited and auditable — important for PDPA data-handling and confidentiality obligations.
Singapore SaaS & Fintech Support
Reduce ticket volume for support-heavy products serving Singapore and APAC customers — answers drawn directly from your docs, with escalation when confidence is low and multilingual handling where you need it.
Govtech & Smart Nation
Knowledge systems for Singapore public-sector and Smart Nation teams — policy retrieval, citizen-service knowledge bases, and internal knowledge management with full in-country data residency in ap-southeast-1.
Sales Enablement
Fast retrieval of case studies, competitive battlecards, and proposal templates during active deals — grounded in your own data, not a generic chatbot guessing from public training data.
Custom RAG, Microsoft 365 Copilot, or Glean? How to Choose
Now that Microsoft 365 Copilot runs on Azure's Singapore region and Glean is landing in enterprise stacks, the real question isn't "AI or not" — it's which approach fits your data, your residency obligations, and how much you want to own. Here's the honest breakdown.
Custom RAG (what we build)
Own it outright
A private retrieval system grounded in your own documents, with page-level citations, your own access rules, and deployment in AWS ap-southeast-1 (Singapore) or on-premise. You own the source code and IP outright — no per-seat licence, and your data never leaves Singapore.
Choose it when
your knowledge lives outside Microsoft 365, you need Singapore data residency or on-premise for PDPA or MAS reasons, you want answers embedded in your own product, or you need auditable citations and access control you govern.
Microsoft 365 Copilot
Productivity layer
Generative AI woven through Word, Outlook, Teams, and SharePoint. Strong when your knowledge already lives inside Microsoft 365 and generic, conversational answers are good enough for the task.
Choose it when
your content is already in M365, you accept per-seat licensing, and you don’t need custom citations, bespoke access rules, multilingual retrieval, or residency guarantees beyond what the tenant gives you.
Glean
Horizontal search
A SaaS enterprise-search platform with prebuilt connectors across many tools. Useful for large organisations wanting cross-app search out of the box, accepting a third-party platform in the data path.
Choose it when
you’re a large org that wants connector-based search across many SaaS tools immediately and you’re comfortable with a vendor platform processing your index.
In practice many Singapore teams run both: Microsoft 365 Copilot for everyday productivity inside the Office suite, and a custom RAG system for the regulated, multilingual, or product-embedded knowledge Copilot can't reach. We'll tell you when off-the-shelf is the right call — including when not to hire us.
Best Fit For
- you have policies, contracts, MAS or compliance docs, or product knowledge Singapore teams need to query quickly
- answers need citations and audit trails — essential under the PDPA accountability obligation and MAS expectations
- you require data residency — all data stays in AWS ap-southeast-1 or your own Singapore infrastructure
- you need retrieval-backed answers grounded in your own data, often across English, Mandarin, Malay, or Tamil
Not the Right Fit When
- you mainly need AI embedded inside an existing product workflow rather than a standalone knowledge system
- your source content is thin, inconsistent, or not yet ready to index
- you expect autonomous answers without guardrails or human review in regulated financial or public-sector workflows
- the goal is a public-facing generic chatbot with no grounding in your own documents
If you need AI embedded inside an existing product workflow, start with AI Feature Development instead.
Related proof from our portfolio: Refactored.ai shows AI-assisted retrieval in a production learning platform, while Bough Digital demonstrates AI-powered search and recommendation at scale. See the full global service page at ai-rag-knowledge-systems, or explore product engineering in Singapore.
Why Singapore Teams Work With Us
12+ years of senior-led delivery, shaped to fit Singapore working hours, data law, and SGD commercial terms
Near Full-Day SGT Overlap
Singapore Time (SGT, UTC+8) is just 2.5 hours ahead of IST, so our India team overlaps almost your entire working day. Morning standups, same-day decisions, sprint reviews, and urgent escalations happen in real time on a Mon–Fri rhythm — not on an overnight delay.
PDPA, MAS & AI-Governance Ready
We build with the Singapore PDPA and the PDPC accountability obligation in mind from day one — data minimisation, access controls, audit logging, and residency scoped to AWS ap-southeast-1. For financial-services clients we align to the MAS Technology Risk Management guidelines, FEAT principles, and the IMDA / PDPC Model AI Governance Framework for Generative AI.
SGD Billing, GST-Compliant
Invoiced in Singapore dollars via Stripe with GST-compliant invoicing on request. No US-dollar conversion overhead or FX surprises — straightforward, transparent commercial terms for Singapore businesses.
Senior Engineers, Direct Access
You work with the senior engineers building your system — not a junior ticket-mill or an account-manager relay. The same team that runs discovery writes the code and answers questions directly on Slack.
How We Deliver
A focused, low-risk process designed to get Singapore teams from problem to working system fast
Discovery & Scoping
Map Singapore use cases, identify data sources, define PDPA / MAS requirements, and set success metrics
Data Preparation
Document ingestion, chunking strategy, embedding pipeline, and vector index — hosted in ap-southeast-1 by default
RAG Architecture
Retrieval system design, LLM selection (private in-region or API), prompt engineering, and context management
Build & Deploy
UI integration, accuracy testing, staged deployment, and monitoring — with full handover documentation
RAG & AI Technology Stack
We select models and infrastructure based on your Singapore data-residency, privacy, and performance requirements — not on defaults
AI & Retrieval
Data & Storage
Infrastructure
How to Get Started
We recommend a Discovery Sprint — low risk, clear output, a PDPA data-residency review, and a foundation for everything that follows
RAG Discovery Sprint
Map your use case, assess data sources, and get an architecture and PDPA-aligned implementation roadmap
- Use-case mapping & data review
- Architecture recommendation
- PDPA / MAS data-residency assessment
- Implementation roadmap
Knowledge Copilot MVP
Full build of a retrieval-based assistant with UI, source citations, and Singapore data residency
- Document ingestion pipeline
- Retrieval + LLM integration
- Web interface with access control
Ongoing RAG Expansion
Continued iteration on your AI knowledge system as your data, regulations, and use cases evolve
- Additional data sources
- Quality & accuracy improvements
- Analytics & monitoring
Client Projects & Case Studies
Products and platforms we've built for clients in Singapore, the UK, Australia, and worldwide over 12+ years
Refactored
AI-assisted Python learning platform with adaptive exercises and retrieval-backed tutoring at scale
See AI proof
CREDITABLE
Employee financial wellness platform for savings, loans, and workplace financial services
Read case study
Bough Digital
Digital marketing agency — AI-powered search visibility and recommendation workflows
See portfolio
PRO Music Tutor
Premium online learning platform connecting students with world-class instructors
See more workFrequently Asked Questions
Straight answers to what Singapore founders, CTOs, and compliance leads ask before building a RAG knowledge system.
What is a RAG knowledge system?
A RAG (retrieval-augmented generation) knowledge system is an AI assistant that retrieves the most relevant passages from your own documents and uses them to generate an answer with cited sources, instead of relying on what a language model memorised from the public internet. Because every answer is grounded in your content and attributed to its source, it stays accurate, auditable, and current as your data changes — which is what makes it safe for Singapore financial-services, public-sector, and regulated enterprise work.
Can you build a RAG system with full Singapore data residency?
Yes. We deploy by default in AWS ap-southeast-1 (the AWS Asia Pacific Singapore Region), so your documents and embeddings stay in Singapore, and we can run the entire system on-premise or in your own private cloud where MAS, a sector regulator, or your DPO requires it. We build with the Singapore PDPA and the PDPC accountability obligation in mind from day one — data minimisation, role-based access, and full audit logging. For MAS-regulated financial institutions, who must keep customer data within approved jurisdictions, in-region or on-premise retrieval is designed in rather than retrofitted.
How is a custom RAG system different from Microsoft 365 Copilot or Glean?
Microsoft 365 Copilot and Glean work well when your knowledge already lives inside their ecosystem and generic answers are acceptable. A custom RAG system is the better choice when you need answers grounded in data they don’t reach, page-level citations, your own access rules, Singapore data residency in ap-southeast-1 or on-premise deployment, multilingual retrieval, or a copilot embedded in your own product — and when you want to own the system outright rather than rent per-seat licences indefinitely. Many Singapore teams run Copilot for everyday productivity and a custom RAG system for the regulated or product-embedded knowledge it can’t touch.
How do you stop the AI from hallucinating or inventing answers?
Every answer is grounded in retrieved passages and attributed with page-level citations, so a user can verify the source before trusting it. We add confidence scoring, fallback and escalation logic when retrieval is weak, and an evaluation pass on your real questions before launch — so the system says “I don’t know” or escalates to a human rather than making something up. This maps directly to the IMDA and PDPC Model AI Governance Framework for Generative AI, which calls for hallucination control, content provenance, and safety testing — and we can validate against tooling like Singapore’s AI Verify before you go live.
Can it work for MAS-regulated, govtech, or PDPA-sensitive teams?
Yes. Cited, access-controlled retrieval is a strong fit for MAS-regulated banks, insurers, and fintechs, Singapore public-sector and Smart Nation teams, and PDPA-sensitive enterprises — secure Q&A over compliance manuals, MAS guidelines and notices, policy libraries, and contracts, with source attribution so nothing gets misquoted. For financial institutions we align to the MAS Technology Risk Management guidelines, the Notice on Cyber Hygiene, the FEAT principles, and the emerging MAS Guidelines on AI Risk Management; for any personal-data use we follow the PDPC’s Advisory Guidelines on the Use of Personal Data in AI systems. Singapore data residency, audit logging, and per-team access controls are designed in, not bolted on.
Can it handle Singapore’s multilingual documents (English, Mandarin, Malay, Tamil)?
Yes. Singapore organisations hold documents across English, Mandarin, Malay, and Tamil, and our retrieval pipeline handles multilingual and mixed-language corpora — embedding and retrieving across languages so a query in English can surface the right passage from a Mandarin or Malay source, with the answer cited back to the original document. This matters for govtech citizen services, regional support desks, and APAC-wide knowledge bases where the source material was never written in a single language.
You’re an offshore team — how do you handle Singapore time zones?
Singapore Time (SGT, UTC+8) is just 2.5 hours ahead of India Standard Time, so our team overlaps almost your entire working day — morning standups, same-day decisions, sprint reviews, and urgent escalations happen in real time on a Mon–Fri rhythm, not on an overnight delay. You work directly with the senior engineers who scoped your system on Slack, not an account-manager relay, we invoice in Singapore dollars via Stripe with GST-compliant invoicing, and you own all the source code and IP outright — committed to your repositories as we go, with no per-seat platform lock-in.
What drives the cost of an AI knowledge system?
Cost is driven by the number and messiness of your data sources, how much cleaning and chunking the documents need, your access-control and audit requirements, whether you deploy in AWS ap-southeast-1 or fully on-premise, whether you need multilingual (English, Mandarin, Malay, Tamil) retrieval, and how deeply the copilot integrates with your existing systems. We scope the smallest valuable version first in a discovery sprint and give you a fixed estimate in Singapore dollars before any build begins, so there are no surprises — pricing is handled directly in conversation, not published as a one-size band.
Ready to Build Your Singapore Knowledge System?
Start with a free discovery call. We'll assess your use case, your PDPA and MAS requirements, and your data sources — and propose a concrete first step with no obligation.