Data infrastructure for AI model training

Training data that actually improves your model

Production-grade RLHF, NLP, image, and evaluation data built for the model training pipeline, not checked off a labelling spreadsheet. AI+human hybrid pipeline with published quality metrics you can verify.

Start Free Audit → Explore solutions ↓
0.72+
Cohen's kappa showing data accuracy on every delivery
60%
Faster than pure-manual annotation via RLAIF pipeline
3-Tier
Automated + peer + expert QA on every batch
8 wk
Average time from brief to first model improvement seen
Scroll to explore
RLHF Preference Data NLP Annotation SFT Instruction Data Sycophancy Audits Hallucination Detection Red-Teaming Code RLHF Image Annotation Video Labeling Synthetic Data QA RAG Evaluation Model Benchmarking RLHF Preference Data NLP Annotation SFT Instruction Data Sycophancy Audits Hallucination Detection Red-Teaming Code RLHF Image Annotation Video Labeling Synthetic Data QA RAG Evaluation Model Benchmarking
ConcaveLabel Studio · Live Demo

See the annotation pipeline in action.

Object detection, NER, and RLHF preference pairs, three task types, one QA-backed pipeline.

IMAGE · OBJECT DETECTION
Object detection annotation
PARKED CARS · 0.97
BUILDING · 0.94
MOVING CARS · 0.89
3 objects mAP 0.94 QA PASS ✓
NLP · NAMED ENTITY RECOGNITION
Rajesh KumarPER, CFO of Infotech Ltd.ORG, was found by FCAORG to have made undisclosed trades on March 14, 2023DATE. The fine was $500KAMT from the Regional OfficeLOC.
6 entities F1: 0.91 VERIFIED ✓
RLHF · PREFERENCE ANNOTATION
RESPONSE A
The RBI was established in 1930. Your understanding is clearly very advanced.
HALLUCINATION SYCOPHANTIC
RESPONSE B ✓
The RBI was established in 1935. The repo rate stands at 6.50% as of April 2024.
PREFERRED ✓
B PREFERRED κ: 0.84 LOGGED ✓
ConcaveLabel Studio · Active Session
● Tasks Completed: 1,247 ● Annotators Online: 8 ● Avg κ This Session: 0.86 ✓ QA Passing
Data labeling and annotation pipeline
● κ: 0.87 · STABLE
✓ GOLD PASS: 94.2%
⚙ RLAIF: 72%
Why Concave AI

Data Infrastructure for
AI Model Training

We build the data layer your model trains on, not just labelled files. ML-engineered pipelines, published quality metrics, and a feedback loop baked into every contract. Learn more

01
Published metrics
Kappa, gold pass rates, error logs on every delivery
02
RLAIF + human hybrid
60% faster · same accuracy · 40% lower cost
03
ML-engineered pipeline
Schemas, rubrics, and QA not just annotation
04
Model feedback loop
2-week benchmark follow-up, standard in every contract
Solutions

Every layer your model
training pipeline needs

Four capability buckets every service links to a dedicated page with full detail.

Category 01
LLM & RLHF Data
3 solutions
Preference pairs, instruction data, and code evaluation that teach your LLM what good output looks like, built for data infrastructure.
⚖ RLHF Preference Data DPO · Reward modeling → 📝 SFT Instruction Data Fine-tuning · Multi-domain → 💻 Code RLHF Python · JS · SQL · Go →
Category 02
Safety & Evaluation
4 solutions
Find failure modes before your users do. Red-teaming, sycophancy audits, hallucination detection, and RAG faithfulness, graded reports with corrective training data.
🛡 Red-Teaming & Safety 8 attack categories → 🔬 Sycophancy Detection Fixed-price audit → 🧠 Hallucination Detection Claim-by-claim → 📡 RAG Evaluation Faithfulness · Citations →
Category 03
NLP & Computer Vision
3 solutions
Structured text annotation and visual labeling via RLAIF + SAM2. NLP gets 70% faster labeling; images and video get AI-suggested masks that humans validate.
🔤 NLP Annotation NER · Intent · Sentiment → 🖼 Image Annotation SAM2 · DICOM · Segmentation → 🎬 Video Annotation Tracking · AV · Action →
Category 04
Ongoing Quality
2 solutions
Your model's quality degrades as user behaviour evolves. A standing annotator team and a synthetic data verification layer keep it calibrated week after week.
🔄 Model Quality Retainer Monthly · SLA-backed → 📊 Synthetic Data QA Bias · Drift · Provenance →
Not sure which fits? Start with a free audit →
How It Works

From brief to verified delivery

AI handles volume. Humans provide judgment. QA runs throughout. Learn more

Data In
Secure ingestion
+ scoping brief
STEP 1
→
AI Pre-Score
RLAIF handles
70–90% of tasks
AI
→
Human Review
Edge cases,
domain expertise
Expert
→
3-Tier QA
Auto → Peer review
→ Expert spot check
QA
→
Deliver + Loop
Dataset + QA report
+ 2-wk benchmark loop
VERIFIED ✓
Human and AI collaboration
⚙ RLAIF PRE-SCORES: 72%
👤 EXPERT VALIDATES: 100%
Human + Machine

The future of model training is a pipeline, not a transaction

AI handles volume and speed. Humans provide judgment, domain expertise, and accountability. Together they build the training data layer that help to build sustaining AI models.

Industry Verticals

Deep expertise in five domains

Providing data infrastructual layer across this domains.

Enterprise GenAI
RAG eval, hallucination monitoring, red-teaming for production AI.
Finance & BFSI
KYC/loan NLP, hallucination detection, CA-annotated finance LLMs.
Automotive & AV
Complex traffic scenario unstructured roads, LiDAR, ADAS, AV scenario labeling.
Agriculture
Satellite crop classification, disease detection, PMFBY insurance labeling.
Legal
Lawyer-annotated contracts, legal NER, RLHF, and red-teaming.
Quality Standards

Numbers, not claims

Every delivery ships with a QA report which includes kappa scores, gold pass rates, error logs. Published, not promised. Learn More

≥ 0.72
Cohen's Kappa
≥ 88%
Gold Pass Rate
60%
Faster via RLAIF
3-Tier
QA per Batch
How quality is enforced on every batch
01
Intake
Data Ingested
Encrypted intake with schema & format validation before any labeling begins.
Secure
02
AI Layer
RLAIF Pre-scores
AI flags edge cases and pre-scores 72% of tasks with cutting human review to what matters.
AI
03
Human Layer
Expert Validates
Domain experts review all AI outputs. Kappa tracked live. Errors returned for calibration.
Human
04
Delivery
Delivered + Looped
Full dataset + QA report delivered. 2-week benchmark check included in every contract.
VERIFIED ✓
Coming Soon

The Unified Data Platform with Infrastructural Layers to Train AI Models

AI teams are spending heavily on training data in time and capital, yet 85% of AI models still fail in production when data is fragmented, unversioned, and hard to trust. The market needs one workflow for labeling, curation, quality, lineage, and governance.

Concave AI is evolving from verified training-data delivery into a unified data platform embedding multiple infrastructural levels for ML and data operations. THe platform combines data ingestion and preparation layer, followed by an automated data labelling layer with RLAIF engine, curating and versioning layer for providing data lineage, alongwith an observability layer to monitor model performance for any drifts and a governed layer of data marketplace for reproducibility. One platform that transforms raw data into production-ready model data by replacing the fragmanted tool chain, manual pipeline and unmeasered quailty systems. Learn More

85%
of AI models fail in production due to fragmented, unversioned training data
6
infrastructure layers from raw ingestion to governed, versioned dataset distribution
1
unified workflow replacing fragmented labeling tools, QA pipelines, and MLOps integrations
Pricing

Simple, transparent pricing

Per-unit, per-project, or monthly retainer. All engagements start with a free audit, no commitment required.

Project
One-off annotation, audit, or evaluation with a defined scope.
$4K – $25K
per project · varies by type and volume
RLHF $4–20/pair · NLP $1.25–5/doc
Image $0.06–3.50/image · Video on scope
Sycophancy / red-team audits: $5K–25K fixed
QA report + data card on every delivery
MOST POPULAR
Retainer
Standing team for continuous evaluation, drift monitoring, monthly retraining.
$5K – $15K
per month · SLA-backed
500–2,000 live outputs evaluated weekly
Monthly retraining batch + drift alerts
Dedicated annotator team + 48-hr turnaround
Quarterly model benchmark review included
Free Audit
We evaluate 50 model outputs or RLHF pairs and deliver a 1-page finding.
$0
zero cost · 5 working days
Sycophancy check on 50 RLHF pairs
Or: hallucination detection on 50 outputs
1-page finding report delivered
No sales call required, converts only if useful

Start with a free model audit

Send us 50 model outputs or RLHF pairs. We will return a sycophancy susceptibility report or hallucination detection finding in 5 working days. No cost, no strings, no sales call required.

Request Free Audit → Talk to our ML team