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Inteligencia artificial

Soluciones LLM a medida

Sistemas de modelos de lenguaje adaptados a su dominio, construidos sobre modelos comerciales y de pesos abiertos, con ajuste fino, evaluación y despliegue privado cuando sus datos y requisitos lo exigen.

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Panorama general

General-purpose models are capable, but they do not know your terminology, your document formats or the boundaries your business must respect. Custom LLM solutions close that gap through a deliberate combination of prompt engineering, retrieval, fine-tuning and, where justified, private hosting of open-weight models.

We start with the least invasive technique that meets the requirement and escalate only when evaluation shows it is necessary. Every adaptation is measured against a task-specific test set, so improvements are demonstrated rather than assumed.

Aplicaciones de negocio

  1. 01

    Domain-specific language tasks

    Adapt models to the vocabulary and conventions of specialized fields, such as clinical documentation, insurance claims, engineering specifications or financial filings, where general models misread terms or produce output in the wrong format.

  2. 02

    Private and sovereign deployment

    Host open-weight models such as Llama or Mistral inside your own cloud account or data center when data residency, confidentiality or contractual obligations rule out sending content to a third-party API.

  3. 03

    Structured output at volume

    Fine-tune smaller models to produce consistent JSON, labels or extracted fields at high volume, which can lower inference cost and latency compared with prompting a much larger general-purpose model for the same task.

  4. 04

    Policy-aligned generation

    Adapt or instruct models to follow house style, approved terminology and policy constraints, with guardrails and evaluation that catch drift before generated content reaches customers, partners or regulators.

Enfoque de ingeniería

  1. 01

    Define the task and the test set

    Agree on exactly what the model must do and assemble a representative, labeled evaluation set with your subject matter experts before any training or prompt work begins.

  2. 02

    Establish a baseline

    Measure commercial and open-weight models with careful prompting and retrieval against the test set, so every later adaptation can be compared with a known starting point.

  3. 03

    Adapt selectively

    Apply supervised fine-tuning, parameter-efficient methods such as LoRA, or preference tuning only where the baseline falls short, using curated, rights-cleared training data with documented lineage.

  4. 04

    Optimize for serving

    Quantize, distill or batch the model as needed and deploy it on an inference stack sized for the expected load, latency target and cost envelope of the workload.

  5. 05

    Monitor and retrain

    Track output quality, drift and user feedback in production, maintain versioned model artifacts, and rerun the full evaluation suite before any updated model is promoted.

Tecnologías y plataformas

Foundation models

  • Meta Llama
  • Mistral
  • Qwen
  • Microsoft Phi
  • OpenAI
  • Anthropic Claude

Training and adaptation

  • PyTorch
  • Hugging Face Transformers
  • PEFT and LoRA
  • TRL
  • DeepSpeed
  • Axolotl

Serving and optimization

  • vLLM
  • NVIDIA Triton Inference Server
  • TensorRT-LLM
  • ONNX Runtime
  • llama.cpp
  • Kubernetes

Evaluation and tracking

  • MLflow
  • Weights & Biases
  • EleutherAI lm-evaluation-harness
  • promptfoo
  • OpenTelemetry

Los nombres de tecnologías son marcas comerciales de sus respectivos propietarios y no implican asociación ni respaldo.

Lo que recibirá

  • Task definition and curated evaluation dataset
  • Baseline comparison across candidate models
  • Adapted or fine-tuned model with versioned weights and a model card
  • Inference deployment on your cloud or on-premises infrastructure
  • Automated evaluation pipeline for future model updates
  • Training data lineage and adaptation documentation

Preguntas frecuentes de los líderes

Do we actually need to fine-tune a model?

Often not. Many requirements are met through prompt design and retrieval over your own content, which are cheaper to maintain. Fine-tuning helps when you need consistent formats, specialized vocabulary, lower inference cost at scale or a smaller self-hosted model. We recommend it only when evaluation shows a clear gap that simpler methods cannot close.

Can the model run entirely inside our environment?

Yes. Open-weight models can be deployed in your own AWS, Azure or Google Cloud account, or on on-premises GPU infrastructure, so prompts and outputs stay within your boundary. We size the hardware, configure the inference stack and document the operational responsibilities your team will carry afterward.

Who owns the adapted model?

Ownership is set in the engagement agreement. Adapted weights and training artifacts produced for you are generally delivered to you, subject to the license of the underlying base model, which can carry its own usage conditions. We review those license terms with your team before any training begins.

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