A language model trained on your data.
End-to-end custom LLM development — from data pipeline design to fine-tuning, RLHF, evaluation, and production deployment. Models that outperform generic APIs on your domain's hardest tasks.
Generic LLMs know a little about everything. We build models that know everything about your domain. Whether you need a model trained on proprietary research, internal documentation, medical records, or financial filings, we handle the full pipeline: data curation, model selection, fine-tuning, alignment, safety testing, and scalable inference deployment. The result: faster, cheaper, and more accurate AI than calling GPT-4 for every request.
Cost reduction vs. API calls
Avg. time to deployment
Domain accuracy
100% on-prem or VPC
Data pipeline & curation
We build ingestion, cleaning, deduplication, and annotation pipelines from your raw documents, databases, and APIs. Quality data beats bigger models.
Model selection & architecture
Llama, Mistral, DeepSeek, or custom transformer architecture — we pick and size the model for your latency, accuracy, and cost constraints.
Fine-tuning & domain adaptation
LoRA, QLoRA, full-parameter fine-tuning, or continued pretraining on your corpus. We measure everything and iterate until accuracy targets are hit.
RLHF & alignment
Reinforcement learning from human feedback, DPO, and constitutional AI to align outputs with your brand voice, safety policies, and user expectations.
Safety & evaluation
Red-teaming, bias audits, truthfulness benchmarks, and a custom evaluation suite that measures what actually matters for your use case.
Production deployment
vLLM, TensorRT-LLM, or custom inference servers. Optimized batching, quantization, and autoscaling so your model serves millions of requests cost-effectively.
Data audit & strategy
Map your data assets, identify gaps, and design a curation pipeline. You get a data quality report and a model architecture recommendation.
Data pipeline build
Ingest, clean, deduplicate, tokenize, and split your dataset. We version everything and build reproducible preprocessing so retraining is a single command.
Training & tuning
Fine-tune on your domain data, run RLHF with your team's feedback, and iterate on prompts and model weights until accuracy targets are locked.
Deploy & monitor
Production inference cluster with autoscaling, A/B testing, and drift detection. We monitor accuracy, latency, and cost per token in real time.
Every engagement ships these.
No upsell games. The full system is in the base scope so you can measure honest ROI from month one.
- Data quality audit + curation pipeline
- Fine-tuned model weights (your IP)
- RLHF-trained aligned variant
- Evaluation suite + benchmark reports
- Safety audit + red-team findings
- Production inference server (vLLM / TRT-LLM)
- API + SDK for your team
- 30-day post-launch tuning sprint
Do we need our own GPU cluster?
No. We train on cloud clusters (AWS, GCP, Lambda, or CoreWeaver) and migrate the final model to your infrastructure. If you want on-prem training, we design for that from day one.
How does a custom model compare to GPT-4?
On narrow-domain tasks, a 7B–13B fine-tuned model often beats GPT-4 at 1/20th the inference cost. On general knowledge, GPT-4 still wins — we design hybrid systems that route queries intelligently.
What about data privacy?
Your training data never leaves your environment unless you explicitly allow it. We can train entirely in your VPC, and the model weights are your intellectual property.
How long does it take?
8–12 weeks for most domain models. The first 2 weeks are data pipeline and architecture; weeks 3–6 are training and evaluation; weeks 7–10 are alignment, safety testing, and deployment.
Ship in 6–10 weeks. If we don't pay for ourselves, we work free.
Get a free product architecture review and a custom roadmap. We build web apps, mobile apps, and AI-powered products end-to-end — shipped by a senior team.
Book your intro callZero technical debt. Full IP ownership. Fixed-timeline delivery.