Data Design Partner

Training data that actually improves your model

Expert RLHF, NLP annotation, GenAI evaluation and image annotation.Powered by an AI+human hybrid pipeline with published quality metrics you can verify.

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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
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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
Why Concave AI

Not another annotation vendor.
A quality system.

Every competitor claims "98% accuracy." We publish the actual numbers — Cohen's kappa, gold standard pass rates, batch error logs — on every single delivery.

01
Published, verifiable quality metrics
Every project ships with a QA report: inter-annotator kappa scores, gold standard accuracy per annotator, batch error rates, and a data card. You can verify our quality claim. No competitor does this.
02
RLAIF + human expert hybrid pipeline
AI pre-scores 70–90% of annotation tasks automatically (using Claude/GPT as AI evaluator). Your expert human annotators handle all uncertain cases, edge cases, and domain-sensitive content. Result: 60% faster, same accuracy as pure-human, 40% lower cost.
03
ML-engineer-led quality, not just operations
Our founder is an ML engineer who personally designs annotation rubrics, builds QA pipelines, and runs sycophancy audits. You get technical peer-level engagement, not a project manager reading a script.
04
Model feedback loop in every contract
We build a mandatory 2-week model performance follow-up into every engagement. You train on our data, measure your benchmark — we collect that result and use it to improve the next batch. No competitor does this as standard.
05
Completely vendor-neutral and DPDP compliant
We are independently owned — not backed by any AI lab, cloud provider, or model company. Your training data stays yours. All operations are DPDP Act 2023 compliant for Indian customers.
Data annotation quality pipeline
● IAA κ: 0.87 · STABLE
✓ GOLD PASS: 94.2%
⚙ RLAIF PRE-SCORE: 72%
● 3-TIER QA · ACTIVE
▼ BATCH QUALITY TREND
Batch 1 Batch 6 Batch 12 ▶
✓ DELIVERED · 2,400 PAIRS
Our Approach

Intelligence that
trains intelligence

Every annotation decision is made by humans who understand the domain — not crowdworkers ticking boxes. Our ML-engineer-led pipeline ensures your model learns from signal, not noise.

Start Free Audit →
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
Services

Every service your
AI model needs to align

From raw preference data to production model quality assurance — we cover the full data lifecycle for NLP, GenAI, and computer vision.

⚖
RLHF Preference Data
Expert-vetted pairwise response ranking for LLM training. Domain-specialist annotators evaluate helpfulness, safety, accuracy, and cultural appropriateness. Every pair includes structured reasoning.
Pairwise ranking DPO pairs Reward modeling
🔤
NLP Annotation
Named entity recognition, intent classification, sentiment analysis, relation extraction, and coreference resolution. AI pre-labeling cuts annotation time 40%. Human experts validate and correct.
NER Sentiment Intent tagging Relation extraction
📝
SFT Instruction Data
Expert-written prompt and ideal-response pairs for supervised fine-tuning. Domain specialists — doctors, lawyers, engineers, educators — write responses your model should emulate. The quality ceiling of your SFT model starts here.
Instruction tuning Domain-expert written Multi-domain
🔬
Sycophancy Detection Audit
We inject 50–100 sycophancy traps into your RLHF pipeline and measure how often annotators incorrectly reward agreeable-but-wrong responses. Delivers a susceptibility score, risk report, and corrective training pairs.
RLHF audit Bias detection Fixed-price report
🛡
Red-Teaming & Safety Eval
Structured adversarial probing across 8 attack categories: jailbreaks, prompt injections, factual hallucinations, bias elicitation, harmful content, privacy leakage, sycophancy, and instruction-following failures. Delivered as a graded report with corrective RLHF data.
Adversarial testing Safety report Corrective data
🧠
Hallucination Detection
Claim-by-claim verification of AI outputs against source documents or domain knowledge. Our ML pipeline auto-extracts all factual claims; expert annotators verify each. Delivered with a hallucination rate breakdown by category and severity tier.
Claim extraction Fact verification Severity scoring
💻
Code RLHF
Software engineers evaluate AI-generated code on correctness, security, readability, efficiency, and style. Specialist pools by language: Python, JavaScript, SQL, Java, Go. Includes automated unit test execution and security linting alongside human judgment.
Multi-language Security review Engineer annotators
🖼
Image Annotation
SAM2-powered pre-annotation reduces manual work 40–60%. Bounding boxes, polygon segmentation, semantic and instance segmentation, keypoint detection, and medical DICOM labeling. Human experts validate all AI-suggested labels.
Segmentation Bounding boxes DICOM / medical SAM2 pre-annotation
🎬
Video Annotation
Object tracking with consistent ID across frames, action recognition, temporal segmentation, and AV scenario annotation. Temporal interpolation reduces frame-by-frame work 70%. Multi-object tracking via ByteTrack. Supports surveillance, healthcare, and autonomous vehicle use cases.
Object tracking Action recognition AV scenarios
📊
Synthetic Data QA
AI-generated training data reviewed by expert humans for bias, hallucination, distribution drift, and edge-case coverage. We generate synthetic examples using LLMs then route through our expert verification layer. Gartner predicts 60%+ of training data will be synthetic by 2027 — this is the trust layer it needs.
Bias detection Quality verification Data provenance
📡
RAG System Evaluation
Human evaluation of retrieval + generation quality in RAG pipelines. Annotators assess: did the retrieval surface the right context? Did the generation faithfully use it? Detects faithfulness failures, citation errors, and hallucinations. Targeted at enterprise AI teams deploying knowledge-base assistants.
Faithfulness eval Citation accuracy Enterprise AI
🔄
Continuous Model Quality Retainer
A standing team of expert annotators permanently assigned to your model. Weekly: evaluate 500–2,000 live outputs. Monthly: quality report, drift alerts, and a curated retraining batch. Your model stays aligned as user behaviour evolves.
Monthly retainer Drift monitoring SLA-backed
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
How It Works

From brief to verified delivery in 4 steps

Every project runs through the same rigorous pipeline. The RLAIF pre-scorer handles volume. Human experts handle judgment. Automated QA runs throughout.

1
📋
Scoping + Guidelines
We review your data samples, define evaluation criteria, write project-specific annotation guidelines with 20+ worked examples, and run an annotator calibration session. Target kappa ≥ 0.70 before any annotation begins.
2
🤖
AI Pre-Annotation
RLAIF pre-scorer (Claude API) evaluates each task and provides a first-pass suggestion with reasoning. Gold standard tasks are injected at 6% rate. For image tasks, SAM2 pre-draws segmentation masks. Humans see AI suggestions — they validate or override.
3
👥
Expert Human Review
Vetted domain-expert annotators handle uncertain cases, edge cases, and domain-sensitive content. Three-tier QA: automated anomaly detection → peer review (15% sample) → expert spot check (5% + all flagged tasks). Daily kappa tracking.
4
📦
Verified Delivery + Feedback
Delivery: dataset + QA report (kappa scores, gold accuracy, error log) + data card. Two weeks later: we follow up for your model benchmark result. That result improves the next batch. The loop that makes us better than any one-time vendor.
Human and AI collaboration
⚙ RLAIF PRE-SCORES: 72%
👤 EXPERT VALIDATES: 100%
Human + Machine

The future of training data is a collaboration, not a competition

AI handles volume and speed. Humans provide judgment, domain expertise, and accountability. Together they produce data no pipeline can replicate alone.

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
Industry Verticals

Deep expertise in six domains

Domain-specific annotation requires annotators who understand the subject matter, not just the task format. We maintain specialist pools for each vertical below.

⚖
Legal
Annotators include qualified lawyers and CS graduates with legal training. Contract clause extraction, legal NER, judgment quality evaluation, compliance document annotation.
Contract clause tagging + risk flagging
Legal NER (statutes, precedents, entities)
Legal AI RLHF (lawyer annotators)
Red-teaming legal AI outputs
 💰
Finance & BFSI
Annotators include Chartered Accountants, finance graduates, and banking professionals. KYC document processing, fraud detection data, loan document NLP, financial AI RLHF.
KYC/loan document NLP annotation
Financial AI hallucination detection
RLHF for finance LLMs (CA annotators)
GST/ITR document processing
 🚗
Automotive & AV
Specialist annotators for Indian road conditions — auto-rickshaws, cattle crossings, unstructured intersections, monsoon visibility. Data that Western providers cannot supply authentically.
Object detection + tracking (Indian traffic)
Lane detection in unstructured roads
LiDAR point cloud annotation
ADAS sensor fusion labeling
 🌾
Agriculture
Annotators understand Indian crops, soil types, and farming conditions. Satellite imagery, drone footage, and field photo annotation for crop disease detection, yield estimation, and PMFBY insurance claims.
Satellite imagery crop classification
Crop disease detection annotation
PMFBY insurance claim photo labeling
Yield estimation model training data
 🏢
Enterprise GenAI
For enterprises deploying internal AI assistants, RAG-based knowledge systems, and domain-specific copilots. Continuous quality assurance, hallucination monitoring, and red-teaming to keep production models safe and aligned.
RAG system faithfulness evaluation
Continuous hallucination monitoring
Custom benchmark creation + tracking
Production AI red-teaming
ConcaveLabel Studio · Live Demo

Three annotation types. One pipeline.

From bounding boxes to preference pairs to NER spans — every task type runs through the same QA-backed pipeline.

IMAGE · OBJECT DETECTION
Object detection annotation
CAR · 0.97
PERSON · 0.94
MOTO · 0.89
3 objects mAP 0.94 QA PASS ✓
NLP · NAMED ENTITY RECOGNITION
Rajesh KumarPER, CFO of Infotech Ltd.ORG, was found by SEBIORG to have made undisclosed trades on March 14, 2023DATE. The fine was ₹4.2 croreAMT from the Mumbai 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
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
Quality Standards

Numbers, not claims

Every competitor says "98% accuracy." We say: here is our Cohen's kappa score, our gold standard pass rate, and your model's benchmark improvement after using our data. Verify it yourself.

Inter-Annotator Agreement (kappa)
≥ 0.72
Gold Standard Pass Rate
≥ 88%
RLAIF Pre-annotation Speed Gain
40–60%
Image annotation speed gain (SAM2)
40–60%
QA tiers per batch
3
Live project pipeline
📥
Customer data ingested to encrypted S3
Secure
↓
⚙
RLAIF pre-scorer evaluates each task
AI
↓
🥇
Gold tasks injected at 6% rate
QA
↓
👤
Expert annotators validate + correct
Human
↓
📊
Kappa + anomaly + gold accuracy checks
Auto-QA
↓
🔍
Senior expert review — 5% sample + flags
Expert
↓
📦
Dataset + QA report + data card delivered
Delivered
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
Pricing

Simple, transparent pricing

No opaque enterprise quotes. Pricing is per-unit, per-project, or monthly retainer. All engagements start with a free audit — no commitment required.

Project
One-off annotation, evaluation, or audit project with a defined scope and deliverable.
₹3L – ₹20L
per project · varies by type and volume
RLHF: ₹250–1,500 per preference pair
NLP: ₹100–400 per document
Image: ₹5–50 per image (complexity-based)
Sycophancy audit: ₹4–12L fixed scope
Red-team assessment: ₹6–20L fixed scope
Full QA report + data card on every delivery
MOST POPULAR
Retainer
Standing annotator team for your model. Continuous quality evaluation, drift monitoring, and monthly retraining batches.
₹4L – ₹15L
per month · SLA-backed
500–2,000 live outputs evaluated per week
Weekly quality report + drift alerts
Monthly curated retraining batch
Dedicated annotator team assigned
Priority turnaround (48–72 hours)
Quarterly model benchmark review included
Free Audit
We evaluate 50 of your model outputs or RLHF pairs and deliver a 1-page finding. No cost, no commitment.
₹0
zero cost · delivered in 5 working days
Sycophancy susceptibility check on 50 pairs
Or: hallucination detection on 50 outputs
Or: inter-annotator agreement baseline
1-page finding report delivered
No sales call required to start
Converts to paid project only if useful
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
Compliance & Standards
DPDP Act 2023 Compliant
GDPR Ready
AWS Encrypted Storage
Signed NDA — All Projects
ISO 27001 (In Progress)
HIPAA-Aligned Workflows

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