Gold-standard evaluation datasets. Hallucination detection. RLHF annotation. 15 years of NLP heritage — now powering the most critical layer in your LLM stack.
Most AI data companies were born in the LLM era. BizKonnect wasn't. We've been solving the core problem — extracting reliable intelligence from unstructured text — since 2009.
"We built NLP systems when there were no foundation models to lean on. Every extraction was engineered by hand. Every entity disambiguation was a decision tree. We learned, the hard way, that the quality of the human judgment layer determines everything."
Enterprise teams invest heavily in model selection, compute, and engineering. The training data layer — annotation, evaluation, ground truth — gets a fraction of the attention. It is, without exception, the layer that determines whether everything else works.
"We spent seven months fine-tuning an LLM for document processing. The model sounded brilliant — confident, fluent. Then we ran a hallucination audit. Fourteen percent of responses contained information that was plausible but factually wrong."
The most consequential layer in your system is the one most teams underinvest in.
Every service delivered by trained, domain-aware analysts. Not generic crowdsourced labour.
Structured human review of LLM outputs classified by error type — fabrication, omission, and context drift — with root cause analysis your ML team can act on.
Domain-aware human raters compare model response pairs. Consensus scoring ensures individual bias doesn't contaminate your reward model training signal.
Domain-specific, adversarially-tested, versioned benchmark sets. Built to give you honest answers about your model — not comfortable ones.
Semantic annotation at 20M+ record scale using a hybrid pipeline. Automated pre-labeling at speed, human review for accuracy at every critical edge.
Complex polygon and polyline annotation via CVAT for retail AI. Quality inspection annotation via RoboFlow at sub-millimeter precision for food and manufacturing.
Building Information Repositories created manually from satellite imagery — floor counts, surface areas, and property metadata for property-tech and urban AI models.
Every capability we offer is live in a product we built ourselves. LLM classification grounded by human verification — exactly the methodology we bring to every client engagement.
Dedicated, trained analysts who work as an extension of your team — not a ticket queue, not a crowdsourcing pool.
One scoping call. No generic proposal. A direct assessment of which service, team profile, and timeline fits your use case.