The difference between a model that performs in a demo and one that holds up in a hospital, a courtroom, or a trading desk is almost always the data. We focus on that gap.
The most consequential knowledge in the world — how to read an ambiguous scan, how to interpret a contract under stress, how to assess credit risk in a novel situation — lives in the minds of experienced practitioners. It does not appear in Wikipedia or web crawls.
CiForce AI was built to capture that knowledge systematically and deliver it in a form that AI systems can actually use. Not as scraped text, but as structured, expert-verified, purpose-built data.
We are not a commodity annotation platform. We are a precision data partner for teams that cannot afford to find out about failures after deployment.
Year founded
Incorporated in Singapore
Expert domains
Enterprise engagements
These are operational commitments, not wall decorations. They shape how we scope, staff, and deliver every engagement.
We would rather deliver 10,000 well-constructed expert examples than a million low-signal ones. Volume without quality trains the wrong behavior.
We do not call someone a medical expert because they tagged health data. Contributors must hold active licenses, certifications, or demonstrated professional records in their field.
Data governance is an engineering decision, not a compliance checkbox. Privacy controls are embedded in pipeline design from the start.
We engage at the model architecture level — not as a task farm. We need to understand what you are building to give you useful data.
When model training reveals a coverage gap, we treat that as signal. We design for feedback loops, not one-shot deliveries.
We document our methodology and report quality metrics without inflation. If there are edge cases or coverage limitations, we say so.
All contributors across all domains share three properties before they annotate a single item:
Degree, license, certification, or employment record — confirmed, not self-reported.
Currently practicing — not retired or primarily academic — so their knowledge reflects current standards.
Domain expertise is necessary but insufficient. Contributors receive task-specific training on annotation protocols and quality standards.
CiForce AI was founded by a team with backgrounds in NLP research, data engineering, and enterprise AI deployment. We have built annotation pipelines from the inside — and seen where they break.
Those failures are rarely compute problems. They are data problems: wrong distribution, wrong domain, wrong labeling protocol, wrong quality threshold. We built CiForce AI to address each one specifically.
Our operations are based in Singapore, which gives us access to APAC professional networks, a strong legal and contractual infrastructure for data work, and a natural hub position for clients operating across Asia-Pacific and globally.
Start a ConversationContact us with your data challenge. We respond within one business day with a specific recommendation.