Singapore · RAG accuracy · Evidence you can audit

Know how often your RAG is wrong — and prove where every answer comes from.

QCRCE measures retrieval accuracy for RAG and internal search at Singapore and Southeast Asia teams. We test on documents you can share and on your real questions, and report recall@k and citation accuracy with the evidence behind every number.

The problem

Wrong answers cost more than slow ones

A RAG system that answers confidently from the wrong passage erodes trust fast. Most teams cannot say what share of answers are grounded in the right source.

“Show me the source” is becoming a requirement

Reviewers, risk teams and — for financial institutions — the MAS Guidelines on AI Risk Management (effective 7 October 2027) expect AI to be evaluated against measurable thresholds and documented.

English-only testing misses real users

Staff and customers in the region ask in Chinese, Malay and Bahasa. Retrieval that works in English can fail silently in other languages.

How we work

  1. 1

    Snapshot

    Free: about 10 questions on your public docs, scored for correct passage and citation.

  2. 2

    Measure

    Health Check: a gold set of 50 of your real questions on documents you share (NDA available), baseline recall@k and citation accuracy.

  3. 3

    Explain

    Failure analysis and a prioritised fix plan, with run instructions so your team can re-test after changes.

What we offer

Two fixed offers, delivered by us directly. Prices in SGD; invoiced via our Singapore business account.

Free

RAG Accuracy Snapshot

SGD 0 a few days

  • About 10 questions run over your public documentation or website
  • Score: correct passage in the top 5, and correct citation at rank 1
  • Short report showing where and why retrieval missed
  • Public pages only; no access to your systems needed

No cost, no obligation.

Paid

RAG Accuracy Health Check

SGD 3,800 about 1 week

  • Works on documents you can share with us (exports, PDFs, wiki pages); mutual NDA available
  • Gold set of 50 of your real questions, with source passages agreed with your team
  • Baseline recall@k and citation accuracy on your current RAG or search (or a reference pipeline on the shared docs)
  • Failure analysis: which questions fail, and why
  • Prioritised fix plan your team can act on
  • Optional: the same questions in Chinese / Malay / Bahasa

Full prepayment before work starts.

Evaluation method

What we measure

recall@k
Share of questions where a correct source passage appears in the top k results. Ground truth is labelled with your team.
Citation accuracy
Share of questions where the passage the system cites first is the right document and section — what an auditor would check.
Multilingual retrieval
The same gold questions asked in Chinese, Malay or Bahasa, scored the same way, to show whether non-English users get the same quality.
Latency p50 / p95
Supporting detail: median and tail query latency of the tested setup.

How targets are set

Thresholds are your call; we report the measured numbers. As an example only, a team might aim for recall@10 ≥ 0.85 and citation accuracy ≥ 0.80.

These are example targets, not results from a customer project.

See it on public documents

Our public demo runs this method on MAS AI risk documents with a 32-question gold set and publishes every measured number, including the misses.

Compliance readiness

For financial institutions and their technology vendors, the MAS Guidelines on AI Risk Management (finalised 7 October 2026, effective 7 October 2027) call for evaluation against measurable thresholds, documentation that lets an independent reviewer reproduce the work, and ongoing monitoring.

Our deliverables are designed to support your evidence pack: the gold set, test results, failure analysis and run instructions can be filed with your AI inventory and review records.

QCRCE provides software and technical evaluation only. We do not provide legal, regulatory or compliance advice or certification; your compliance and legal teams decide what meets your obligations.

Funding

Some Singapore companies may be eligible for Enterprise Singapore's EDGE Grant (which replaced EDG and PSG from 30 September 2026). Eligibility and approval are decided by Enterprise Singapore, and the grant generally requires that no work or payment happens before you apply.

We are glad to provide a quotation and scope for your own application, but we cannot promise any subsidy. If you plan to apply, tell us first and we will not start or invoice until you have.

Data handling

Your data is used only to deliver the agreed work and is never used to train public models. Third-party APIs are disclosed in the SOW, with SG/SEA regions preferred. Corpora are deleted or returned within 30 days of project close.

About

QCRCE is a small Singapore software and technical consulting company focused on RAG accuracy, retrieval evaluation and vector retrieval. It is founded and run by Mark (Ma Weijia), who works directly on every engagement.

We are software and consulting only. We do not provide financial or regulated advisory services.

Company details

Legal nameQCRCE Pte. Ltd.
UEN202630091K
Address68 Circular Road #02-01
Singapore 049422
FounderMark (Ma Weijia)

Talk to us

Tell us what your RAG or search system needs to answer, and where the knowledge lives. We will reply with a free snapshot offer or Health Check scope.