AI Training Background
Agentic Model Engineering

Train Agents.
Not Just Models.

Generic LLMs aren't enough for enterprise execution. We fine-tune models on your data, align them with your business rules (RLHF), and optimize them for autonomous agency.

Why Off-the-Shelf Models Fail

GPT-4 is a generalist. Your business needs a specialist. Without fine-tuning, agents hallucinate, miss context, and fail to follow complex SOPs.

Domain Amnesia

General models don't know your SKUs, your compliance codes, or your legacy codebase. We inject that knowledge directly into the model weights.

Data Privacy Risks

Sending sensitive PII to public APIs is a non-starter. We train and deploy private models (Llama 3, Mistral) within your VPC.

Latency & Cost

Using a massive model for simple tasks burns cash. We distill large models into smaller, faster, task-specific agents that run 10x cheaper.

Our Training Pipeline

We use an enterprise-grade stack to take models from raw checkpoints to production-ready agents.

Data Curation

Snorkel, Labelbox for high-quality instruction datasets.

Fine-Tuning

LoRA/QLoRA on H100 clusters using Axolotl or Unsloth.

Alignment

DPO (Direct Preference Optimization) to enforce business rules.

Evaluation

Ragas & DeepEval for automated benchmarking against golden sets.

PyTorch
HuggingFace
W&B
vLLM

Training Infrastructure

Common Questions

Do we need to fine-tune a model or just use RAG?

For 80% of use cases, RAG (Retrieval Augmented Generation) is sufficient. Fine-tuning is necessary when you need to change the 'behavior' or 'style' of the model, or when you need to teach it a new language/syntax that isn't in its pre-training data. We often use a hybrid approach.

How much data do we need for fine-tuning?

Less than you think. With modern techniques like LoRA, we can achieve significant behavioral shifts with as few as 500-1,000 high-quality instruction pairs. Quality matters far more than quantity.

Can we host these models on-premise?

Absolutely. We specialize in training open-weights models (Llama 3, Mistral, Mixtral) that can be containerized and deployed in your own VPC or on-premise servers, ensuring total data sovereignty.

Ready to Build Your Custom Brain?

Stop renting intelligence. Start owning it. Let's train a model that understands your business perfectly.