HUGGING FACE AGENCY: INTEGRATE AI THAT ACTUALLY WORKS INTO YOUR PRODUCT
Hack'celeration is a Hugging Face agency that helps you leverage the power of open-source AI without getting lost in the technical complexity. We deploy pre-trained models, fine-tune them on your data, and integrate them into your stack so you get real business value from AI.
Concretely, we work with the Transformers library, configure Inference APIs, deploy Spaces with Gradio interfaces, fine-tune models with PEFT and LoRA, and connect everything to your existing tools (Make, n8n, your CRM, your app).
We work with startups building AI-powered products, scale-ups wanting to integrate ML features without hiring a full data science team, and established companies looking to leverage open-source models instead of expensive proprietary APIs.
Our approach is simple: we focus on what works, deploy fast, and make sure you actually understand what we built. No black-box magic, no overcomplicated architectures. Just AI that does what you need it to do.
Let's build your growth engine.
Why partner
with a Hugging Face agency?
Because a Hugging Face agency can transform AI from a buzzword into an actual working feature in your product. Instead of spending months figuring out which model to use, how to fine-tune it, and how to deploy it at scale, you get a team that's already done it dozens of times. Hugging Face has over 500,000 models and 100,000 datasets. The ecosystem is powerful but overwhelming. Without guidance, you'll waste weeks testing models that don't fit your use case, struggling with deployment, or burning money on inference costs. Here's what we bring you: Model selection that makes sense → We navigate the Model Hub to find the right model for your specific use case, considering performance, cost, and deployment constraints. Custom fine-tuning → We fine-tune models on your data using PEFT, LoRA, or full fine-tuning depending on what makes sense for your budget and requirements. Production-ready deployment → We deploy via Inference API, Spaces, or your own infrastructure with optimized inference using TGI (Text Generation Inference) or ONNX export. Stack integration → We connect your AI features to your existing tools via API, webhooks, or direct integration with Make, n8n, or custom code. Cost optimization → We help you choose between hosted inference, self-hosted solutions, or hybrid setups to control your AI costs. Whether you're starting from scratch or have already experimented with Hugging Face, we help you move from prototype to production without the headaches.
Our methodology
for Hugging Face Agency.
STEP 1: AUDIT YOUR AI NEEDS
— We start by understanding what you actually need AI to do. Not what sounds cool, but what creates real value for your business. We analyze your use case: text generation, classification, summarization, image recognition, audio transcription, or something else. We identify the inputs and outputs you need. We review your existing data. Do you have training data? Is it clean? Is there enough of it? We assess whether fine-tuning makes sense or if a pre-trained model will do the job. We map your technical constraints: latency requirements, volume, budget, existing infrastructure, and team capabilities. At the end of this step, you have a clear roadmap with model recommendations, deployment strategy, and realistic cost estimates.
STEP 2: MODEL SELECTION AND TESTING
— We dive into the Hugging Face Model Hub to find candidates that match your requirements. Not just the most popular models, but the ones that fit your specific constraints. We run benchmarks on your actual data. We test multiple models from the Transformers library and compare performance, speed, and resource usage. We evaluate trade-offs: a smaller model that’s fast and cheap vs a larger model that’s more accurate but expensive. We give you the data to make informed decisions. We prototype using Gradio interfaces in Spaces so you can test the models yourself before we commit to anything. At the end of this step, you have a validated model choice with real performance metrics on your data.
STEP 3: FINE-TUNING (IF NEEDED)
— If a pre-trained model doesn’t cut it, we fine-tune one on your data. We use parameter-efficient methods like LoRA and PEFT to reduce costs and training time. We prepare your dataset using the Datasets library: cleaning, formatting, tokenization with the right Tokenizers, and train/test splits. We configure training with the Accelerate library for efficient use of GPU resources. We monitor training to avoid overfitting and catch issues early. We version everything with model cards and proper documentation so you know exactly what we trained and how. At the end of this step, you have a custom model that performs significantly better on your specific use case.
STEP 4: DEPLOYMENT AND INTEGRATION
— We deploy your model where it makes sense for your use case. Hugging Face Inference API for quick setup, private Spaces for custom interfaces, or your own infrastructure for full control. We optimize inference using TGI (Text Generation Inference) for LLMs or ONNX export for faster CPU inference. We configure batching and caching to handle your expected volume. We build the API layer and connect it to your stack. Direct integration with your app, webhooks for async processing, or connections to Make and n8n for workflow automation. We set up monitoring to track inference latency, error rates, and costs so you can see exactly how your AI is performing. At the end of this step, you have a production-ready AI feature integrated into your product.
STEP 5: TRAINING AND HANDOFF
— We don’t just deliver and disappear. We make sure your team understands what we built and can maintain it. We document everything: model architecture, training parameters, deployment configuration, API endpoints, and integration points. We train your team on how to use the system, monitor performance, and handle common issues. We show you how to retrain if your data evolves. We provide templates and scripts for common operations: updating the model, scaling infrastructure, debugging issues. At the end of this step, you’re autonomous. You can run, maintain, and evolve your AI features without depending on us for every little thing. WHY WORK WITH US? — At Hack’celeration, we don’t just do Hugging Face agency work. We master the whole stack (Airtable, Make, n8n, HubSpot, Bubble, etc.) and know how to connect AI to any business system. That’s the difference between a cool demo and a feature that actually creates value. We work with startups building AI-first products, scale-ups adding ML features to existing platforms, and established companies exploring open-source alternatives to expensive APIs like OpenAI or Anthropic. We’ve deployed text classification systems processing 100k+ documents daily, built custom chatbots fine-tuned on company knowledge bases with RAG pipelines, integrated image recognition into e-commerce platforms for automatic tagging, and created summarization tools that save teams hours of manual work. We don’t just know the Transformers library. We understand business problems. We know when AI is the right solution and when a simple automation will do the job better. We won’t sell you a complex ML pipeline if a Make scenario does what you need. We don’t build black boxes. We give you systems you understand, with documentation, training, and templates you can use. You work with a team that has deployed dozens of Hugging Face models to production and knows exactly how to avoid the pitfalls of AI integration.
