Enterprise AI platform · Built in Jakarta

AI that works on what your enterprise actually knows.

LapisAI gives your agents three governed layers: Redroot for the numbers in your warehouses, Redwood for the knowledge in your documents, and Relai for the work your people do with AI. All of it runs in your environment.

lapis/ˈla.pɪs/Indonesian for layer.

LapisAI platform3 layers
Lapis 03AI platform
RELAI

Workspace, Code and Desktop. Where people and agents do the work.

Lapis 02Ontology
Redwood

Documents become linked, cited company knowledge.

Lapis 01Data platform
Redroot

Certified metrics from every warehouse, asked for by name.

Your cloud · Indonesian region · On-premises
01/04One question, answered through every layer.
Certified metricsCited knowledgeGoverned agentsRuns in your cloudIndonesian regionMCP for any AI toolBahasa Indonesia firstOpen formats, no lock-in

Why LapisAI

Your AI tools can write. They still cannot see your business.

01

Numbers without definitions

An agent writing SQL against a raw warehouse returns plausible numbers, and nobody can tell which ones are wrong.

Redroot answers from certified metrics, or refuses.

02

Documents without connections

Search finds passages, not who did what, with whom, and what was learned. Every question starts from zero.

Redwood compiles them into linked, cited knowledge.

03

Tools without governance

Five AI subscriptions per person, no audit trail, and company data sitting outside your security perimeter.

Relai puts every model and agent in one governed place.

The platform

Three layers. One governed context for every agent.

Each layer stands on its own. Together, an agent moves from a client's revenue to its contracts to a finished brief in one conversation, with a receipt for every fact.

Lapis 03 · AI platform RELAI

Where people and agents do the work: a governed AI workspace, a coding agent and a desktop agent, all grounded in the layers below.

relai.lapisai.id →
Lapis 02 · Ontology Redwood

What the company knows: documents compiled into linked, cited notes on people, companies, projects and domains, growing with every project.

redwood.lapisai.id →
Lapis 01 · Data platform Redroot

What the numbers are: a certified semantic model over every warehouse, so agents ask for metrics by name and get the same answer every time.

redroot.lapisai.id →
Foundation · Deployment Your environment

Every layer deploys where your data already lives: your cloud tenant, an Indonesian region, or your own data centre.

Deployment →
Redroot Lapis 01 · Data platform Early access

Certified numbers from every warehouse.

Redroot reads your warehouse's data model and drafts a semantic model your data owner certifies. Agents then ask for metrics by name. Redroot writes the SQL, applies the asker's access policies, and returns the number with its receipt.

  • AI drafts, people certify. Every metric carries a named owner and a certification stamp.
  • Refuses instead of guessing. Questions outside the certified model get the closest certified metric, not a plausible wrong number.
  • The warehouses you already run. SQL Server, Oracle, PostgreSQL, Microsoft Fabric and Synapse first; Snowflake, BigQuery, Databricks and MaxCompute next.
Visit redroot.lapisai.id

Why a semantic layer: in dbt Labs' April 2026 benchmark, questions answered through a modelled semantic layer were 98 to 100% correct, while raw text-to-SQL on the unmodelled schema reached 64.5%.

Answer · via RedrootSample data

Berapa NPL per cabang Q3 dibanding Q2?

2,41%▲ 0,18 pp vs Q2
NPL gross, all branches, Q3 2026
Jawa Barat3,12%
Sumatera Utara2,64%
Jawa Timur2,38%
DKI Jakarta2,05%
Q3 2026 vs Q2 2026 Filtered to your regions
CertifiedNPL gross · certified by Risk · data as of 08.00
Redroot console
Add connection, test step: network, authentication and read-only role checks for a SQL Server warehouse, with the T-SQL that creates the read-only role. Model Builder on BANKDW_PROD (SQL Server): Power BI measures and dbt metrics mapped to the model, with conflicts and AI drafts marked. Review and certify: the NPL gross change in plain language, the definition diff with AI-proposed lines marked, and the evaluation result. Access and policies, View as: the answer a branch manager would get beside the unrestricted view, with other branches filtered out and personal data masked. Audit log with a query open: user, AI tool, role, policies applied and the receipt the user saw in chat.
Connect a warehouse with a read-only service account, tested before anything is saved.
ClaudeRedroot connected

hanya Jawa Barat

NPL gross, Jawa Barat, Q3 2026 2,73% ▲ 0,28 pp Tersertifikasi
The certified number, asked for in chat
Redwood Lapis 02 · Ontology In production at Xquisite AI

A company brain that grows a ring every year.

Redwood turns your documents into an open wiki of linked people, companies, projects and domains, with a source cited for every fact. Agents browse it, search it and draft inside it under each user's permissions, and every project adds to what the company knows.

  • Compiled, not just indexed. PDFs, Word files, slide decks and scans become entity notes with links and citations.
  • Agents that work in context. Each project gets an agent that drafts deliverables from its plan, meetings and files.
  • Yours to keep. The whole vault is plain Markdown you can open in Obsidian or export at any time.
Visit redwood.lapisai.id
1,608linked notes
1,928graph links
368companies mapped

Redwood's Second Brain, built from Xquisite AI's own pipeline and delivery files.

Entity note · RedwoodSample note
Company Reviewed

PT Contoh Manufaktur

Manufacturing client since 2024.1 Data warehouse modernisation delivered with Microsoft in 2025.2 Lessons from the handover now guide two newer projects.3

Related
DWH Modernisation 2025 Microsoft Data warehouse Manufacturing
Sources
1Proposal_2024-03.pdf · page 2
2Handover_2025-11.pptx · slide 3
3Lessons learned · DWH Modernisation 2025
Redwood · Second Brain
A cited answer card with three claims, four numbered sources and a reviewed-by line. Entity note for Bank Sentosa Raya: engagements by year with citations, people, properties and related projects. The graph in focus mode around Bank Sentosa Raya, coloured by note type. A project agent's plan and tool steps, with a failed step offering Retry or Skip. Merge review comparing two company notes before they are combined.
A cited answer: every claim numbered, every source one tap away, and the name of the person who reviewed it.
A cited answer in Indonesian on a phone, with source [2] open in a bottom sheet.
On a phone, in Bahasa Indonesia
RELAI Lapis 03 · AI platform

AI you can rely on.

Where your people and agents do the work.

Relai brings the models, tools and agents your teams need into one governed platform, connected to Redwood and Redroot, so every answer starts from what your company knows.

Relai Workspace

Available

One AI workspace for the whole company: chat across models, deep research with citations, slides, image and video, and an organisation knowledge base. SSO, roles and audit built in.

Chat · Research · Slides · Image · Video · Knowledge

relai.lapisai.id →

Relai Code

New

An agent that reads, writes and runs code in your repositories, from the terminal or your editor, with your team's context and guardrails.

$ relai "add NPL by branch to the risk report"
Reading 14 files · plan ready for review
>
relai.lapisai.id/code →

Relai Desktop

New

The same agent in a desktop app for Windows and macOS. Start a task, approve each edit, and review every change before it lands.

Read src/components/SignupForm.tsx
Edited 1 file · +9 −0
Waiting for your approval
Download the app →
RELAI Code · Desktop
Relai Desktop showing a finished task beside a side-by-side diff of SignupForm.tsx with nine added lines. Relai Desktop asking for approval before applying an edit to SignupForm.tsx, with Reject, Allow all edits and Yes, allow once. Relai Desktop welcome screen with a task box, Ask approval mode and the qwen3-coder-next model selected.
Review the diff of every file the agent changed, line by line.
Relai Code in a terminal: it reads and edits SignupForm.tsx, asks to allow the edit, runs the tests and reports 14 passed.
Relai Code in the terminal

Deploys on Alibaba Cloud, Microsoft Azure or BytePlus, with the models each one offers.

How the layers work together

One question, three layers, one answer you can check.

A director asks Relai

“Kenapa NPL Jawa Barat naik di Q3, dan apa yang sudah dibahas dengan tim regional?”

Why did NPL in West Java rise in Q3, and what has already been discussed with the regional team?

1Redroot

Returns certified NPL by branch for Q2 and Q3, filtered to the regions the director may see.

2Redwood

Finds the regional review minutes, the accounts discussed and who owns them, each with a citation.

3RELAI

Drafts the brief for the board pack, with every number and claim linked back to its source.

Governance and deployment

Built for regulated Indonesian enterprise.

Runs where your data lives

Your cloud tenant, an Indonesian region, or your own data centre. Nothing leaves without your say.

Permissions travel with the data

Every answer respects the asker's access in the source system, and never hints at what they cannot see.

A receipt for every answer

Certified definitions, citations and data freshness on every number and claim, with a full audit log behind them.

Open to any AI tool

Redroot and Redwood speak MCP, so Claude, Microsoft Copilot, ChatGPT and your own agents can use them too.

Bahasa Indonesia first

Every surface works in Indonesian and English, with local number, date and currency formats.

Yours to keep

Open formats throughout: plain Markdown for knowledge, standard SQL for data. No lock-in.

Deploy on Alibaba CloudMicrosoft AzureAWSBytePlusOn-premises

Company

From the team that builds Indonesia's data warehouses.

LapisAI (PT Lapis Kecerdasan Buatan) is a member of Xquisite AI, the Jakarta data and AI consultancy behind data platforms for more than 90 enterprises.

90+enterprise clients served by Xquisite AI
5sectors: banking, BUMN, manufacturing, logistics, FMCG

See the platform on your own data.

A working session with our team, on your warehouse, your documents and the question your board keeps asking.