A product manager at a Singapore fintech told me in April that her team spent three weeks trying to find a compliance decision her legal team had made in 2024. The answer was in a Confluence doc, buried under three layers of folders, written by someone who had since left the company. Her team of 60 people used eight different tools โ Slack, Notion, Confluence, Google Drive, Jira, GitHub, Salesforce, and a local SharePoint โ and nobody could find anything anymore.
This is the knowledge retrieval problem that every SEA company past 50 people is hitting in 2026. AI is finally offering a real answer โ one that doesn't require a data science team to set up.
## The problem is worse in SEA than it looks
Knowledge fragmentation hits SEA companies harder for specific reasons. Most regional teams operate across multiple countries, so the same question might be answered differently depending on whether you're in Bangkok, Singapore, or Jakarta. The answers live in whichever tool that office prefers.
English is the official working language at most Singapore-headquartered companies, but a meaningful chunk of institutional knowledge is documented in Thai, Bahasa Indonesia, or Vietnamese and never surfaces in English-language searches.
Employee turnover in SEA tech is also notably high. When someone leaves a Thai e-commerce team, they often take 18 months of undocumented decision context with them. A knowledge base built on top of existing tools can at least capture what was written down.
## Two approaches actually working in 2026
There are two practical paths SEA enterprise teams are taking, depending on budget and technical capability.
**The enterprise route: Glean**
[Glean](/tools/glean) is the tool you hear about at Singapore tech company offsite conversations right now. It connects to your existing tools โ Slack, Notion, Drive, Confluence, Jira, Salesforce โ and builds a unified AI search layer on top. You ask a question in plain language and it finds the relevant content from whichever tool it lives in, respecting your existing permissions.
The practical advantage: you don't need to reorganize or migrate anything. Knowledge stays where it is. Glean just makes it findable.
The catch is price. Glean starts at around $50 per user per month (roughly SGD 67) with a minimum of 100 seats, putting the entry price at roughly $60,000 (SGD 81,000) per year. Reasonable for a 200-person Singapore regional HQ. It prices out most Thai or Indonesian SMEs entirely.
My honest take on Glean: it's the right tool at the right company size. Under 100 people, or outside Singapore where budgets are tighter, you'll spend the money long before you feel the benefit.
**The build-your-own route: Cohere + Dify or n8n**
For teams with at least one technical person, building a RAG (retrieval-augmented generation) knowledge base on [Cohere](/tools/cohere) embeddings plus a document store is now genuinely accessible. [Dify](/tools/dify) has become the go-to builder for this in SEA. Its visual workflow editor handles the ingestion pipeline, chunking, embedding, and retrieval without writing a backend.
A Bangkok-based logistics company built their internal policy search tool in two weeks using Dify with Cohere Embed v3 for multilingual embeddings and Cohere Command R for generating answers. Total monthly running cost: under $200 (about THB 7,000 or IDR 3.2 million). Staff can now search in Thai or English and get answers grounded in actual company policy documents.
Cohere's multilingual embedding quality for Bahasa Indonesia and Malay is notably better than alternatives at this price point. For companies processing documents across English, Bahasa, and Thai, this matters a lot for search accuracy.
## What actually breaks in most AI knowledge base projects
The failure mode I see most often: teams upload their entire document library without curation and wonder why the AI returns generic, unhelpful answers. A few things that make or break these projects in practice:
**Document quality is everything.** If your source documents are poorly structured, contradictory, or outdated, the AI will faithfully surface poorly structured, contradictory, outdated information. Before building, do a basic curation pass โ remove docs older than 3 years, merge duplicates, flag superseded policies.
**Chunking strategy matters more than model choice.** How you split documents for embedding has a bigger effect on retrieval quality than which embedding model you use. Docs with clear headings and sections chunk better than walls of text. If your Confluence space is full of long unstructured pages, retrieval quality will suffer regardless of which AI you put on top.
**Multilingual queries need multilingual embeddings.** If you embed documents in English but your staff searches in Thai, you're losing significant retrieval quality. Cohere Embed v3 handles cross-lingual retrieval reasonably well, but you get better results if you also store Thai-language versions of key documents.
## Is this worth it for SEA SMEs?
For companies under 30 people: probably not yet. The organizational cost of keeping the knowledge base updated is real, and a well-maintained Notion works fine.
For companies between 50 and 200 people, especially those operating across multiple SEA countries: this is the right moment. The tools are good enough that you don't need dedicated data science resources to set up a functional system. The build-your-own path using Dify and Cohere keeps costs under THB 10,000 (about $280) per month for most team sizes. Glean makes sense if you're larger and don't want to maintain your own infrastructure.
For companies above 200 people in Singapore or regional HQs: Glean is likely worth the investment. One Singapore engineering team estimated it saved each engineer about 45 minutes per week โ at Singapore engineering salaries, that pays back the Glean cost in a few months.
## The honest reality check
Building a knowledge base doesn't solve organizational knowledge problems. It makes existing knowledge more accessible.
If your team documents decisions poorly, an AI search tool will help people find the poor documentation faster. The Singapore fintech PM from the start of this piece eventually got Glean approved. Three months in, her team found the missing compliance decision in about 90 seconds. The decision itself? They ended up not using it โ the context had changed enough that it was outdated. But she described it as still worth knowing.
That gap between "tools are ready" and "team habits are ready" is where most implementations stall. Document the decision-making process first. Get people to write rationale, not just outcomes. The search tool amplifies whatever discipline already exists in your writing culture โ nothing more.