Training data for ideas
taking shape.
Zyka Foundry is a precision labeling studio for teams building computer vision, video intelligence, and speech AI. We handle the unglamorous part of the model — so your team can stay focused on the interesting part.
Models are only as honest as the data behind them. We treat labels like the product they are.
Specialists, not a crowd
Radiologists do medical imaging. Linguists do phoneme work. Drivers do LiDAR. Matching expertise to modality is not optional — it's the whole job.
Quality as a measured system
Every pool runs under a QA framework: gold tasks, consensus scoring, calibrated reviewers, and agreement deltas shipped with every batch.
Your taxonomy, not ours
We don't hand you a generic schema. We sit with your ML team, iterate on edge cases, and encode your judgment into the annotation guide.
Pixel-level honesty.
Motion, traced
frame by frame.
Sound with meaning attached.
thanksfor calling,how can I help?yeah uhmy orderit hasn't[0.4s hesitation]arrived yetand I'm kindalosing patience here
active: speaker · agent
intent: order.status.check
entity: order_id (missing)
sentiment: neutral
When the model is right, we stay out of the way. When it isn't,
we close the loop.
Your model makes predictions at full throughput.
~85% auto-accepted. ~15% routed by entropy + uncertainty.
Matched specialists label, correct, rationalize.
New checkpoint rolls forward. Confidence gate retightens.
Corrected labels join SFT / RLHF training pool.
Disagreement routed to senior review. Rationale logged.
- 01 /3–10× throughput
Pre-label & correct
Your model proposes boxes, masks, transcripts, or captions. A trained reviewer approves, fixes, or rejects. On mature schemas, throughput goes up by an order of magnitude and label cost drops just as hard.
- 02 /Uncertainty-routed
Active learning
We route only the samples your model is least sure about — low-confidence predictions, high-entropy distributions, near-decision-boundary cases. The labels you pay for are the ones that move the needle.
- 03 /Every Nth inference
Production review pool
A fixed sample rate of production traffic is mirrored into a review queue. Drift, regressions, and silent failures show up in the agreement delta the day they start — not in the quarterly model eval.
- 04 /Specialist pools on standby
Edge-case triage
When your model hits something weird — a rare medical phenotype, a code-switched utterance, a long-tail class — the task is auto-routed to a specialist pool. No generic-crowd guessing on data that deserves expertise.
- 05 /Pairwise · rubric · Likert
RLHF & preference
Pairwise A/B ranking, rubric-scored absolute rating, and multi-turn dialogue judgment for generative models — image, video, speech, text. We run the calibration pipeline that keeps subjective judgment internally consistent.
- 06 /Policy · jailbreak · adversarial
Safety & red-team
Jailbreak probing, policy-violation review, and adversarial prompt labeling for safety-tuned models. Trained reviewers who know the taxonomy cold and log rationale alongside the label.
- 07 /Signal, not noise
Disagreement routing
When three annotators disagree, the schema is under-specified — not the annotators. Disagreements are routed to a senior reviewer, resolved with written rationale, and become test cases for the next schema revision.
- 08 /Statistical + human review
Drift detection & reroute
Population-level statistics on the production inference stream surface distributional shift. Flagged slices are pulled into a re-labeling queue before the model's confidence catches up with its actual accuracy.
A loop implies something that runs. Most HITL pipelines we inherit from customers are not actually loops — they're a one-way conveyor from humans to training data, never back. The difference shows up in model performance twelve months later. We build them as real loops.
A quiet, careful process
that you never have to manage.
Good annotation looks boring from the outside. No heroic late-night pushes, no Slack fires, no "we'll fix it in the next batch."
The work shows up on time, the agreement numbers are where they should be, and the edge cases are the ones your guide already anticipated.
- 011–2 weeks
Kickoff & taxonomy
We sit with your ML team for 2–3 working sessions to understand the model's failure modes, the decision boundary you want to enforce, and the edge cases that keep your PMs up at night. Output: a versioned annotation guide, test set, and success metric.
- 023–5 days
Gold set & calibration
We hand-build 200–1,000 gold tasks with your team, then calibrate annotator pools against them. Annotators who don't hit the agreement bar don't see production data. Simple as that.
- 033–7 days
Pilot batch
A small production batch (1–5% of scope) runs end-to-end through the tool, pool, QA, and export pipeline. We surface schema ambiguities early, before they compound across millions of labels.
- 04Ongoing
Production at scale
Full throughput with layered QA — peer review, senior review, gold-task injection, and consensus scoring on disputed tasks. Rolling dashboards on agreement, throughput, and reviewer calibration.
- 05Per milestone
Retrospective & schema v2
Every batch ends with a retro. What did annotators argue about? What did the model get wrong after training on this data? That feedback becomes the next version of the guide.
Numbers you can audit,
not adjectives.
Every delivery ships with an agreement report, reviewer calibration curve, and a delta against your gold set. You don't have to trust us — you can check.
Disagreement is the most valuable signal in any annotation pipeline. We surface it, root-cause it, and feed it back into the next schema revision instead of averaging it away.
Inter-annotator agreement
Krippendorff's α, Cohen's κ, or F1 depending on task shape. Reported per batch and per reviewer.
Gold-task injection
Annotators see hidden gold tasks at a 3–5% rate. Performance below the bar triggers recalibration.
N-way consensus
Critical or ambiguous tasks routed to 3+ annotators. Conflict resolution by senior reviewer with written rationale.
Reviewer drift monitoring
Weekly calibration checks for reviewers themselves. Humans get lax over time — we control for it explicitly.
Teams whose data earns its keep.
- AV · drones · robotics
Autonomous systems
LiDAR cuboids, sensor-fusion tracking, lane and drivable-surface segmentation, behavior prediction labels.
14 AV programs shipped - radiology · pathology · ophthalmology
Healthcare & life sciences
DICOM-native workflows with clinician annotators. HIPAA environment, IRB-aware de-identification, audit-ready trails.
4 FDA-cleared customers - SFT · RLHF · safety red-team
Generative AI
Preference ranking, rubric-driven aesthetic scoring, and multi-turn dialogue judgment for text-to-image, video, and voice models.
1.2M pairwise judgments - CCTV · perimeter · behavior
Security & surveillance
Anomaly detection, re-identification across cameras, loitering and intrusion events, crowd density estimation at scale.
24/7 monitored pool - catalog · shelf · fit
Retail & e-commerce
Product attribute tagging, shelf compliance audits, try-on pose and garment segmentation, aesthetic search preference data.
6M SKUs processed - EO · SAR · aerial
Geospatial & satellite
Building footprints, land-use segmentation, change detection, vessel and vehicle detection from overhead imagery.
EO + SAR trained pools - ASR · intent · QA
Contact centers & voice
Transcription, diarization, intent and entity schema work for voice agents, IVR, and conversational analytics.
7 languages live - QA · predictive maintenance
Industrial & manufacturing
Surface defect segmentation, machine anomaly audio, thermal imaging classification, assembly compliance.
Line-speed throughput
A team of specialists. Not a crowd.
We're a hybrid model. A full-time annotation team of 140+ domain specialists handles the hard work. A vetted extension pool of 800+ trained contributors scales us up when volume spikes.
Annotators are paid above local living wage, receive health coverage, and are employed under local labor law — not gig-work contracts. We believe the quality of a label reflects the conditions it was made in.
We meet you where your stack already is.
- CVAT (self-hosted)
- Label Studio
- Scale Nucleus
- Labelbox
- Supervisely
- Segments.ai
- Deepen AI
- Custom PCD viewer
- Praat
- ELAN
- Audino
- Custom MUSHRA rig
- Argilla
- SurgeHQ schema
- Custom pairwise UI
- S3 / GCS / Azure Blob
- SFTP
- Signed-URL handoff
- COCO
- YOLO
- Pascal VOC
- CVAT XML
- KITTI
- nuScenes
- DICOM-SR
- WebVTT
- TextGrid
- custom schemas
Your data stays your data.
We operate on a principle of least access. Annotators see only the fields they need, from workstations inside our secure VPN — no downloads, no screenshots, no phone cameras allowed in sensitive pools.
PII redaction, watermarking, and full audit trails on every task are available by default on healthcare, financial, and government engagements.
Clean-room workstations
Locked-down VDI, no USB, no external network egress, recorded sessions on flagged pools.
End-to-end encryption
AES-256 at rest, TLS 1.3 in transit. Customer-managed keys available on enterprise plans.
Signed BAAs & DPAs
HIPAA BAA and GDPR DPA on every engagement. Sub-processor list published and versioned.
Full audit trail
Every annotation is attributed, timestamped, and reviewable. Deletion certificates on project close-out.
The nicest thing a customer has said is that they forgot we existed.
We switched from a crowd platform after six months of arguing with the data. Agreement numbers on our mammography schema went from 0.71 to 0.94 in the first pilot batch. That was the day the CTO stopped asking me about label quality.
Their LiDAR team knew the sensor stack. Nobody had to explain what a moving ground return was. We lost a quarter of engineering time on a previous vendor just trying to communicate basic things. That time came back.
Annotation is supposed to be invisible when it's working. For two years now I've been able to forget it's even part of our process — which is the highest compliment I can give a vendor.
We ran the same 5,000-sample evaluation batch with three other vendors first. Zyka Foundry was the only one whose disagreement pattern was consistent with our own internal review. They were not labeling a different model than us.
Priced for the real shape of the work.
Pilot
- Up to 20,000 simple labels
- 1 modality · 1 taxonomy
- Shared annotation guide authoring
- Gold set + calibration included
- Agreement report per batch
- 2-week standard turnaround
Program
- Dedicated annotator pool (20–80 people)
- Dedicated program manager + QA lead
- SLA on throughput and agreement
- Weekly reviewer calibration
- Live dashboards on agreement & throughput
- Multi-modality supported
- Direct Slack line to the pool PM
Enterprise
- Clinician / licensed specialist pools
- On-premise or customer VPC deployment
- Customer-managed encryption keys
- Custom BAA / DPA / DPIA
- Dedicated legal + security review
- 24/7 program staffing
- Data residency guarantees (US / EU / APAC)
Some things worth knowing before we start.
Let's get your data in shape.
Tell us what you're building. We'll reply within one working day with a scope sketch, a rough timeline, and a short list of the sharpest questions we'd ask in a kickoff session.