Solution
Synthetic Data for AI Training
Real data has compliance problems. Synthetic data doesn't.
The biggest bottleneck in enterprise AI is not the model — it's the training data. Real datasets are full of PII, subject to DPDP/GDPR restrictions, expensively labeled, and statistically biased in ways you don't discover until production. Qapitol's synthetic data practice generates privacy-safe, domain-accurate, bias-audited datasets that accelerate model development without the compliance overhead of real data.
01
Zero
PII in training data
02
DPDP & GDPR
compliant by design
03
GenRocket
powered generation
04
10×
faster dataset production
The challenge
What makes this hard
- Privacy Compliance Barrier: Real customer data contains PII that can't legally be used for model training without complex anonymisation pipelines. DPDP, GDPR, and sector regulations block fast access.
- Manual Labeling Bottleneck: Human annotation is expensive, slow, and inconsistently quality-controlled. 6–18 months to label a production-grade dataset destroys AI project timelines.
- Bias Hidden in Real Data: Real-world datasets reflect historical biases that become model biases. You don't discover them until post-deployment audits — by which point the damage is done.
What we deliver
The Qapitol approach
01
Data Generation — Synthetic dataset generation at scale
Domain-accurate synthetic datasets generated using GenRocket's statistical modelling combined with Qapitol's domain expertise in BFSI, healthcare, retail, and logistics. Statistically representative, relationship-preserving, and privacy-safe by architecture.
02
Annotation — AI-assisted annotation pipelines
Human-in-the-loop annotation workflows with AI pre-labelling to reduce annotation time by 80%. Domain expert annotators for BFSI and healthcare datasets. Quality-controlled ground truth with inter-annotator agreement metrics.
03
Bias Auditing — Statistical bias detection & correction
Systematic bias analysis across demographic attributes, class distributions, and domain-specific fairness metrics. Bias detection before model training — not after deployment audit — with correction recommendations embedded in the dataset generation pipeline.
04
Eval Datasets — Adversarial eval set construction
Construction of adversarial evaluation datasets specifically designed to stress-test your AI model's failure modes. Edge case generation, out-of-distribution examples, and adversarial prompts that expose weaknesses before production deployment.
05
Privacy & Compliance — DPDP / GDPR compliant data pipelines
Synthetic data that is provably privacy-safe — no re-identification risk, no PII in the output. Data generation and management pipelines designed for DPDP (India), GDPR (EU), HIPAA (US healthcare), and RBI data residency requirements.
06
Domain Specialisation — BFSI, healthcare & retail data factories
Sector-specific synthetic data generation that preserves the statistical properties of your domain — insurance claim data, banking transaction patterns, clinical records, retail clickstream, logistics events — without exposing actual customer data.
Next step
Bring Synthetic Data for AI Training to your stack
Scope it in one call — outcomes defined upfront, free assessment included.