Reduce fine-tuning data costs by 10x while improving model reasoning depth.
Expert-level instruction-tuning datasets across eight specialist domains. Practitioner personas under institutional pressure. Three-stage certification. Built for enterprise AI teams that cannot afford generic training data.
Enterprise AI teams building specialist models spend more on data than on compute. That calculus is broken.
Scale AI charges $100,000–$150,000 per domain expert per year. Building in-house takes six months of ML engineering and produces static output that degrades. Generic synthetic data vendors produce tabular privacy data — not expert-depth instruction tuning. BondFoundry delivers practitioner-grade records continuously, at a fraction of the cost, with compounding quality.
The three existing options for domain-specific training data are all fundamentally inadequate.
Every dataset is generated by practitioner personas operating under institutional constraints — not theoretical analysis. Purpose-built for fine-tuning domain-specific models that need to reason like genuine specialists.
Every record passes through a sequential three-stage certification pipeline before entering the master catalogue. Records that fail any stage are permanently quarantined. Approval rates are published on every dataset.
Rejection criteria feed directly back into the next generation cycle. Every failure is a training signal. The catalogue does not just grow in volume — it compounds in certified quality.
These are not feature differentiators. They are architectural properties of the generation system that compound over time and cannot be replicated by prompt engineering, vendor switching, or additional headcount.
Custom domain pipelines. Bespoke expert personas built around your specific terminology and model architecture. Continuous generation with weekly QA reports. Enterprise DPA available. Suitable for production AI systems at scale.
All datasets ship as JSONL files with three fields per record: system, instruction, and response. This format is compatible with all major fine-tuning frameworks without preprocessing.
Every record passes three independent quality gates. The third gate is a structured peer review by a domain expert persona — the CISO for cybersecurity records, the Magic Circle senior partner for legal records, the principal quant researcher for finance records. Approval rates and quality dimension scores are published on every dataset. You can audit the quality framework before purchasing.
All datasets ship under MIT licence. Commercial use permitted without restriction. No royalty obligations. No attribution requirements. Enterprise clients receive a standard data processing agreement on request.
The Accelerated Sprint is a high-volume, time-bound delivery product. We generate 2,500 to 10,000 certified records in 7 to 21 days at flagship generation quality with full three-stage QA. Pricing starts at $4,999 for 2,500 records and scales to $14,999 for 10,000 records. Suitable for teams with an imminent training run or launch deadline.
The Custom Domain Pipeline is our highest-tier enterprise product. We build a bespoke generation pipeline around your specific terminology, regulatory jurisdiction, and model architecture, with 12 custom expert personas. Onboarding is $2,999. Ongoing generation is $3,500 to $15,000 per month depending on volume and domain complexity. DPA included.
Generation runs continuously. New records are certified and appended to the master catalogue every cycle. Enterprise retainer clients receive weekly update packages. One-time purchases include the dataset as it exists at time of purchase.