LLM Development

Custom Language Models Trained on Your DTC Brand & Domain.

Public LLMs know everything but specialise in nothing. A custom LLM trained on your product catalogue, brand voice, customer service history, and domain expertise knows your business at a level no public model can match — delivering more accurate, more consistent, and more brand-appropriate outputs.

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LLM Development Services

Language Models That Know Your DTC Business Inside Out

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LLM Architecture Design

Selection and design of the right LLM architecture — choosing model size, training approach, and domain adaptation strategy based on your data, latency, and accuracy requirements.

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Training Data Engineering

Collection, cleaning, and preparation of training datasets — curating the highest-quality domain-specific data that will shape your model's language understanding and generation.

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Fine-Tuning & Domain Adaptation

Supervised fine-tuning and instruction tuning of base LLMs on your proprietary data — adapting model behaviour to your brand voice, product domain, and operational context.

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RLHF & Alignment

Reinforcement Learning from Human Feedback to align model outputs with your brand guidelines, safety requirements, and desired response characteristics.

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Inference Optimisation

Model quantisation, pruning, and distillation to reduce inference cost and latency — making your custom LLM commercially viable at DTC production serving volumes.

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Safety Evaluation & Guardrails

Safety evaluation, red-teaming, and output filtering ensuring your custom LLM behaves appropriately across all input types and adversarial conditions.

More brand-accurate outputs vs generic LLMs

60%

Reduction in prompt engineering overhead with fine-tuned models

40%

Lower inference cost vs GPT-4 for equivalent quality tasks

100%

Data privacy — your training data never leaves your environment

Frequently Asked Questions

Scale D2C delivers end-to-end Large Language Model (LLM) Development — 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 Large Language Model (LLM) Development 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 Large Language Model (LLM) Development 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 Large Language Model (LLM) Development 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 Large Language Model (LLM) Development 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.

Custom Language Models Trained on Your DTC Brand & Domain.

Language Models That Know Your DTC Business Inside Out

Frequently Asked Questions

Build a Language Model That Knows Your Brand