We build with Hugging face
We use Hugging Face to build and fine-tune natural language and generative AI models for your applications that support your business goals. From model selection and fine-tuning to inference optimization and deployment, we create products designed to evolve with your needs.
DISCUSS YOUR PROJECTPretrained Model Hub
Thousands of ready-to-use models for NLP, vision, and audio.
Rapid Fine-Tuning
We adapt pretrained models to your data and use case.
Production Inference
We deploy models as scalable, low-latency inference APIs.
BENEFITS OF HUGGING FACE technology
We use Hugging Face to fine-tune pretrained models, deploy inference APIs, and accelerate NLP and vision projects.
BUILD
[01]- Select pretrained models
- Fine-tune on custom data
- Configure tokenizers
- Evaluate model outputs
ENGAGE
[02]- Serve real-time inference
- Support chat and search
- Process text, image, audio
- Integrate with your apps
GROW
[03]- Scale inference throughput
- Add new fine-tuned models
- Optimize latency and cost
- Extend with RAG pipelines
Our Hugging Face Technology Stack
We combine Hugging Face with PyTorch, Datasets, Tokenizers, and Accelerate for training and fine-tuning, and with vector databases and LangChain for retrieval-augmented generation and production inference.
Custom Hugging Face development company
With our Hugging Face development services, we build custom NLP, computer vision, and generative AI features using pretrained and fine-tuned Transformer models, tailored to your boldest business goals. Having years of experience with the Hugging Face ecosystem, our engineers harness the Model Hub, Transformers library, and tools like Datasets and Accelerate to deliver accurate, production-ready AI solutions across industries and company sizes. Whether it's a new AI feature or integrating models into an existing product, we deliver solutions that are accurate, scalable, and easy to maintain. Our range of Hugging Face development services spans consulting, model selection, fine-tuning, and deployment, along with ongoing monitoring and optimization. We work as your technical partner and ensure complete transparency and comfortable communication throughout the process, bringing deep machine learning expertise and a pragmatic approach to problem-solving. All this to make sure your AI features stay accurate, cost-efficient, and reliable as usage grows, and moves your business forward.
OUR HUGGING FACE SERVICES
We build, modernize, and support Hugging Face models around your product goals.
Your roadmap is growing faster than your team. Add senior engineering capacity and deliver more without sacrificing quality.
Fine-tune pretrained models from the Hub on your data and use case.
Chatbots, search, summarization, and generative AI features built on Transformer models.
Scalable, low-latency inference APIs and RAG pipelines connected to your applications.
Migrate custom ML pipelines to Hugging Face or upgrade existing models with newer architectures.
Meet our Hugging face experts
A curated selection of senior specialists currently available for new engagements.
KD
We Turn Technology Into Results
Partner with a team that blends technical precision, creative design, and business insight. We’ll help you launch, scale, and dominate your digital niche.
Frequently Asked Questions
Common questions about how we use Hugging Face and what it can bring to your project. Have a specific requirement?
How does SoftDoes use Hugging Face?
We use Hugging Face to fine-tune pretrained language and vision models and deploy them as production inference APIs. We select models from the Hub and the supporting tools around your product's use case and data.
What types of applications do you build with Hugging Face?
We build chatbots, semantic search, document summarization, content generation, and computer vision features, from early prototypes to production AI systems integrated into existing products.
Can Hugging Face integrate with our existing systems?
Yes. Hugging Face models can be deployed as APIs alongside your existing backend, and integrate well with vector databases and orchestration frameworks like LangChain for retrieval-augmented generation.
Do you train models from scratch or use pretrained models?
We typically start with pretrained models from the Hugging Face Model Hub and fine-tune them on your data, which is faster and more cost-effective than training from scratch for most use cases.
Do you work with PyTorch and TensorFlow alongside Hugging Face?
Yes. The Transformers library supports both backends, and we choose PyTorch or TensorFlow based on your existing infrastructure and the model architectures your project requires.
Can you help us build a RAG application with Hugging Face?
Yes. We combine Hugging Face embedding and generation models with vector databases and LangChain to build retrieval-augmented generation systems for search and question answering.
How do you decide whether Hugging Face fits a project?
We look at your data, accuracy requirements, latency and cost constraints, and existing infrastructure, then confirm Hugging Face is the right fit before starting development.
