WHO WE ARE · ANNOTIQ BASE
Built by people who love data.
A working lab — researchers, labelers, RL engineers, and systems folks in one room, shipping the data and the models that go with it.
ANNOTIQ BASE · TRAINING ROOM
Data labeling is what we do — across every modality and every industry, at a fraction of what legacy vendors charge.
Our flagship service. Text, image, audio, video, and multimodal labeling — AI-assisted and human-audited at a fraction of what legacy vendors charge. From a thousand samples to a billion, with the same quality.
Supervised fine-tuning, instruction-tuning, RLHF, and DPO. We collect the preference data, run the training, and evaluate the model — so your team ships, not orchestrates.
Embed AI capabilities into your product without hiring a research team. Classification, extraction, search, agents — scoped, built, and operated by us.
Cleared-personnel labeling pipelines for defense, intelligence, and civic AI — with the chain-of-custody and security posture those workloads require.
Multimodal sensor labeling — IMU, lidar, depth, IR, ToF — with synchronized timeline review for embodied agents and humanoid robots.
Reproducible reinforcement-learning environments and reward labeling — the substrate for tool-using, planning agents.
AI-assisted annotation with human audit gates. Text, image, audio, video, multimodal — at any scale, with inter-rater agreement reported in real time.
Supervised fine-tuning, RLHF, DPO. We collect the preference data, run the training, and report evaluation metrics your team can act on.
Ship a working model — hosted by us or handed off to your stack. Versioned datasets, signed checksums, and active-learning APIs for the next iteration.
Raw signal → annotation studio → consensus → training → evaluation → a model that ships.
Beyond labeling and training, Annotiq Base provides specialist teams across the disciplines that frontier AI work actually demands.
Specialists who keep model behavior tied to human intent. They design alignment techniques, build evaluation suites that surface harmful or deceptive behavior, and develop training methods — RLHF, constitutional approaches, preference modeling — that close the gap between what a model optimizes for and what you actually want.
Researchers who reverse-engineer what a model is actually computing. They probe activations, isolate circuits and features, and build tooling that makes internal representations legible — so model behavior can be predicted and audited at the level of mechanism, not just observed at the output.
Adversarial engineers who stress-test frontier systems before they ship. They hunt jailbreaks, prompt injection, training-data extraction, and misuse pathways, then work with your team to harden models and guardrails against the attacks they find.
Analysts who map AI's effect on markets, labor, and institutions. They model economic impact, advise on governance and regulation, and translate raw capability into policy-grade research that holds up in front of regulators, boards, and the public.
Engineers who train agents through reward. They design reward models, build reproducible RL environments, run RLHF and DPO pipelines, and tune agentic systems for planning, tool use, and reliable long-horizon tasks.
Systems engineers who make large-scale training and inference fast and affordable. They optimize distributed training, kernel-level performance, memory footprint, and serving throughput across GPU and TPU clusters — turning compute budgets into shipped capability.
A working lab — researchers, labelers, RL engineers, and systems folks in one room, shipping the data and the models that go with it.
Send us your dataset and use case — we’ll come back with a quote within a business day, and it’ll be the lowest one you’ve seen.