Singapore · AI strategy, built and run

We set your AI strategy — and build the systems that deliver it.

Qlabs is a Singapore AI consultancy for teams who want results, not a slide deck. We move you from AI-absent to AI-native at startup speed — setting the strategy, building the working systems, and proving the return in your own numbers. Built by builders, trusted by leaders.

Strategy you can ship. Return you can measure.

Certified by Anthropic

Claude Certified Architect

Professional

Claude Certified Architect – Professional, issued by Anthropic

Verify on Credly →

Why Qlabs

A consultancy that ships, not one that presents.

Most AI consulting ends where the real work begins — a maturity assessment, a vendor shortlist, a six-month strategy deck and a phase-two invoice. We do it in the opposite order: set the strategy, build the working system, and prove the return. Four principles keep us out of the 95% of AI projects that never change a number that matters.

01

Strategy that ships

We set the strategy and then execute it. The deliverable runs in your operation, not in a document — no maturity assessment, no vendor shortlist, no phase-two you have to staff and fund separately. Strategy and working software in the same engagement.

02

Measured on return

We baseline the process before we build — cycle time, cost per case, error rate — then prove the delta in your own numbers. Not "it feels faster." A before and after you can put side by side, and a return you can defend to your board.

03

Singapore-native, PDPA-first

WhatsApp threads, call transcripts, emails, records — we record a lawful basis for every source before we touch it, minimise what we use, and can process it locally on our own hardware so personal data never leaves for a third party. Compliance built in, not bolted on.

04

You own it when we leave

We design your team's self-sufficiency from the first week — your people learn the system, own the documentation, and run it without us. A good consultancy makes itself unnecessary; a good engagement ends with you needing us less, not more.

From AI-absent to AI-native

Strategy and execution, in weeks not quarters.

A full engagement moves through seven stages — from setting strategy to shipping and owning the system. The early ones are fast and inexpensive on purpose: they exist so we both find out quickly whether this is worth doing, before anyone commits to a build.

  1. 0

    Fit check

    A short conversation: is there a sponsor with P&L authority, a real painful process, and a budget? If not, we say so.

  2. 1

    Discovery

    We map how your business makes money and where time and money leak, then list a handful of candidate processes worth fixing.

  3. 2

    Choose the one

    Together we score the candidates and pick a single process to focus on — one decision, one owner, one number to move.

  4. 3

    Baseline

    We measure the current state and inventory the data it needs, with a lawful basis recorded for every source. No baseline, no delta.

  5. 4

    Build

    We build the working system inside your operation, on your real, messy data — in weeks, not quarters.

  6. 5

    Prove

    We run it live, compare against the baseline, and show the delta — then close the loop so the decision sharpens each cycle instead of going stale.

  7. 6

    Hand over

    Your team takes the controls. We leave documentation and reusable parts behind — and one reusable artifact enters our own library.

Ways to engage

Start small. Scale once it's proven.

Four ways to work with us, built to be taken in sequence — the output of one is the entry point of the next. But the ladder is front-loaded: commit to a small, fast first step, and only scale once the value is visible. WDG-anchored where you're eligible.

Scoping Sprint

1–2 weeks

A decision census and a wedge scorecard. One decision, one owner, one number named, with a signed baseline plan. The diagnosis, not the build.

Wedge Build

2–4 weeks

A working prototype on your real, messy data, run by the real owner — with the current-state baseline measured and signed first.

Loop & Handover

3–6 weeks

The prototype becomes a loop that sharpens each cycle. Adoption driven with your staff, delta measured against baseline, handover pack so you run it without us.

Loop Care

Monthly, capped

Light custody of the live loop — tuning, drift-watch, next-cycle improvement, next-wedge scouting. Capped by design. Not managed services, not a way to become indispensable.

Not sure where to start? The first step is a 30-minute fit check. If there's no fit, we'll tell you plainly — we only take on work we believe will pay for itself.

Built by builders

We ship the systems we advise on.

This isn't theory. These are Qlabs-owned platforms running live in Singapore, each with agentic AI at the core — proof that we practise what we advise, in production, not in a deck. The method that proves itself here is the method we bring to your engagement.

Tutor Marketplace Live · Singapore

AI-powered two-sided marketplace for private tutoring.

Two-sided marketplace matching parents with verified tutors. Claude ranks candidates in real time, an autonomous Telegram agent fields subject questions inside a 400+ member community, and an orchestrated email-relay workflow takes a shortlist all the way to a paid lesson — without a human coordinator in the loop.

Web SPA · Telegram bot · email-relay · Stripe payments

Agentic capabilities

  • Claude-driven matching: ranks 20+ candidate tutors against parent requirements in under a second, returning top 3 with personalised explanations; rule-based fallback when the LLM is unavailable
  • Autonomous Telegram agent monitors a 400+ member group for subject and level keywords, waits 15 minutes for human responses, then auto-posts tutor cards
  • Email-relay agent (Claude + Gmail API) drafts and mediates tutor ↔ parent conversations — replies via signed links, no auth, no direct contact until payment clears
  • Time-triggered nurture orchestration: shortlist nudges (+24h / +72h / +7d), commission retries, lesson-confirmation prompts, review-unlock check-ins
  • Review-gated quality system: lessons must be confirmed as occurred before reviews unlock; tutor quality score auto-recomputes from base + review bonus

Stack

pythonfastapianthropicreactstripegmail-apiapschedulerdocker
Curriculum Platform Live · Singapore

AI content engine for international curriculum specialists.

Internal platform for curriculum specialists producing study materials for major international syllabuses. AI-powered worksheet generation produces structured, syllabus-aligned resources on demand. A catalogue of 18,000+ study resources — tagged, searchable, and organised by subject area — backs a growing international curriculum business.

Internal web platform · AI generation pipeline · React + FastAPI

Capabilities

  • AI worksheet generation pipeline: takes curriculum topic and level inputs, outputs structured study resources aligned to major international syllabuses
  • Content catalogue of 18,000+ resources with subject-area filtering, syllabus-level tagging, and full-text search
  • Automated S3 backup pipeline on six-hourly schedules with restore verification — no content loss on failure
  • React SPA with FastAPI backend: resource management, AI generation, and subject-area filtering in a single interface built for non-technical curriculum authors

Stack

pythonfastapireactpostgresqls3anthropicdocker
Learning Ops Platform Confidential — under NDA

Agentic operations platform for a Singapore education business.

One platform replacing an incumbent scheduling system and 15 spreadsheets across enrolments, scheduling, finance, and communications for a Singapore tutoring centre. Non-technical staff drive operations through a permissioned chat agent that books trials, records attendance, and processes invoices under explicit human confirmation.

Internal web app · scheduled Celery workflows · staff chat agent

Agentic capabilities

  • Tool-calling agent with 40+ tools across 10 domains (students, scheduling, finance, attendance, tutors, marketing)
  • Role-based tool access — admin, finance, marketing, tutor, readonly — enforced per call
  • Confirmation-first design: agent clarifies ambiguity and asks before any write operation
  • Persistent agent memory — staff teach the system standing rules, stored in Postgres and injected per session
  • Multi-backend LLM (Ollama / Anthropic / DashScope) behind a single interface; conversation history persisted for continuity

Stack

djangopostgrespgvectorredisceleryhtmxanthropicdocker

Insights

Thinking on the AI-native business

Get in touch

Ready to go from AI-absent to AI-native? Let's set the strategy and build.

Based in Singapore. The first step is a 30-minute fit check.

hello@qlabshq.com →