Fine-tuning transforms a general-purpose LLM into a specialist that understands your product taxonomy, brand voice, customer language, and operational context. We deliver custom-tuned models that outperform prompt engineering alone — with better accuracy, fewer hallucinations, and lower inference costs.
Scale D2C delivers end-to-end LLM Fine-Tuning — strategy, data engineering, model development, API integration, production deployment, and ongoing monitoring. We build AI that operates inside your DTC stack and improves measurable business outcomes — not research projects that never reach production.
Data requirements depend on the specific LLM Fine-Tuning use case. Most applications need 12–24 months of clean historical data to train a reliable model. Scale D2C runs a data readiness audit in week one — identifying gaps, quality issues, and the minimum viable dataset needed to begin.
A LLM Fine-Tuning proof of concept takes 4–6 weeks. Full production deployment runs 10–20 weeks depending on data readiness and integration complexity. Scale D2C uses two-week sprints, delivering working software throughout — not a 20-week black box revealed at the end.
Scale D2C builds MLOps pipelines into every LLM Fine-Tuning deployment — continuous performance monitoring, data drift detection, automated retraining triggers, and alerting. All models come with a monitoring dashboard and agreed accuracy SLAs backed by our managed services team.
When LLM Fine-Tuning capabilities are properly documented using structured FAQ content, entity markup, and AEO/GEO best practices, AI search platforms like ChatGPT, Perplexity, Google Gemini, Claude, Deepseek, and Sarvam AI are more likely to cite your brand as an authoritative source. Scale D2C builds this technical and content foundation as standard.
Every prompt you write to compensate for a generic LLM is a prompt you would not need with a fine-tuned model. Let us build yours.