Foundation models that are actually yours.
We design and train custom LLMs from the ground up, built on your data, tuned to your domain, and deployed on infrastructure you control. For organizations where off the shelf isn't enough, and sovereignty isn't optional.
Every layer
of the stack.
Training a real model takes more than a fine tuning script. We own dataset curation, infrastructure, training runs, and evaluation, and hand off a model your team can operate with confidence.
Dataset Curation
Sourcing, cleaning, deduplication, and quality filtering at scale, including synthetic data generation when the corpus is thin.
Pretraining at Scale
Distributed training across H100 / A100 clusters with mixed precision, gradient checkpointing, and efficient parallelism strategies.
Domain Adaptation
Continued pretraining on industry specific corpora, legal, medical, financial, scientific, without losing general capability.
Eval Harness Design
Custom benchmarks that test what actually matters for your use case, not just public leaderboards.
Distillation & Compression
Larger teacher models distilled into faster, cheaper students, with measured trade offs on capability.
Sovereign Deployment
Full ownership of weights, training data, and serving infrastructure, including air gapped on premise options.
From corpus to production model.
Custom training is a six month commitment, not a six week sprint. We sequence it carefully so you see capability gains before the final run.
Scoping & Eval Design
We define the model's job, the success metrics, and the eval harness, before any GPU spins up.
Data Pipeline
Sourcing, cleaning, tokenization, and quality filtering, building the corpus that defines what the model knows.
Training Runs
Pretraining on your cluster or ours, with intermediate evals and the ability to stop early if signals are off.
Alignment & Handoff
Instruction tuning, safety alignment, and operational handover with monitoring and retraining playbooks.
Questions about
Custom LLM Training
When you need true sovereignty over weights and data, when your domain or language is poorly represented in foundation models, or when your scale makes inference cost the bottleneck. Otherwise, fine tuning usually wins on cost and time.
From 1B to 70B parameters routinely. Beyond that, training cost climbs steeply, we'll walk you through the trade offs vs. distillation or fine tuning a frontier model.
Your cloud, your on premise cluster, or compute we provision. For sovereignty sensitive projects we deploy entirely in your jurisdiction with no data leaving your perimeter.
Six to nine months end to end for most engagements. The training run itself is weeks; the data work, eval design, and alignment phases are where the time actually goes.
Yes. Weights, training data, eval harness, and operational tooling all transfer to you. We're a development partner, not a hosted API vendor.
Stop experimenting.
Start deploying AI that works.
Book a free discovery call. We'll review your data, scope the right model size, and tell you honestly whether custom training is the right move.
info@croncore.com