Machine Learning & AI Development Service
Custom machine learning models, OpenAI integrations, computer vision, and AI-powered features. From prototyping to production deployment.
Adding AI and ML to Your Product
AI and machine learning can transform your product — but building ML features requires specialized knowledge that most development teams lack. From data preprocessing to model training to production deployment, each step has its own challenges. A recommendation engine that works in a Jupyter notebook is very different from one that serves millions of predictions per day in production.
Hiring a full-time ML engineer is expensive and often unnecessary. Most products need AI features, not an AI team. You need someone who can integrate OpenAI, build a custom classification model, or set up a recommendation engine — and then move on to the next feature. Paying a six-figure salary for an ML engineer who spends half their time on non-ML work is not efficient.
The AI landscape changes rapidly. Six months ago, the best approach for a text processing feature might have been a fine-tuned BERT model. Today, it might be a GPT-4 API call with carefully designed prompts. Keeping up with the latest models, frameworks, and best practices requires continuous learning that most product teams cannot afford.
Many AI projects fail not because the model is wrong, but because the engineering around it is weak. Data pipelines break silently, model predictions are not monitored for drift, error handling is missing, and the system falls over when input data does not match the training distribution. Production ML is 90% engineering and 10% model selection.
Our team has built ML systems ranging from product recommendation engines to computer vision applications to LLM-powered features. Through AsyncForge, you get experienced ML engineers who handle the full pipeline: data preparation, model selection, training, evaluation, and production deployment — all at a fixed monthly rate.
What You Get
OpenAI & LLM Integration
GPT-4, embeddings, function calling, and RAG pipelines for AI-powered features. We build LLM integrations that go beyond simple chat — structured output parsing, embedding-based search, retrieval-augmented generation for company-specific knowledge, and multi-step agent workflows. Every integration includes proper token management, cost tracking, and fallback handling.
Custom ML Models
Classification, regression, recommendation systems using scikit-learn, XGBoost, and PyTorch. We build and train custom models when off-the-shelf APIs do not fit your needs. This includes data analysis, feature engineering, model selection, hyperparameter tuning, and validation. Every model comes with evaluation metrics so you know exactly how well it performs.
Computer Vision
Image classification, object detection, and segmentation with YOLO, SAM, and CLIP. Whether you need to detect defects in manufacturing images, classify product photos, or extract information from documents, we build computer vision systems that work reliably on real-world data with proper preprocessing and augmentation.
Data Pipelines
ETL pipelines with pandas, data validation, and automated model retraining. ML models are only as good as the data feeding them. We build robust data pipelines that extract, clean, validate, and transform your data — with monitoring and alerts when data quality degrades or distributions shift unexpectedly.
ML Ops
Model versioning, monitoring, A/B testing, and production deployment with Docker. We deploy models as containerized services with proper versioning, health checks, and performance monitoring. A/B testing infrastructure lets you compare model versions safely, and automated retraining keeps your models current as new data arrives.
NLP & Text Processing
Sentiment analysis, text classification, embedding search, and language processing. We build text processing features that understand your domain — from customer support ticket routing to content moderation to semantic search. Each implementation is tuned for your specific data and vocabulary, not just generic pretrained models.
Technologies We Use
How It Works With AsyncForge
Define the AI feature
Describe what you want the AI to do in plain language — we translate it into technical requirements. You do not need to know the difference between classification and regression, or whether you need an LLM or a custom model. Tell us the problem, and we recommend the right approach with clear tradeoffs.
Prototype and validate
We build a working prototype to validate the approach before investing in production code. This prototype uses your real data to demonstrate accuracy, speed, and feasibility. If the approach does not work, we find out in days instead of months, and we pivot to an alternative solution.
Build for production
We productionize the model with proper error handling, monitoring, and scaling. This means containerized deployment, input validation, graceful degradation when the model fails, performance monitoring, and cost tracking. Production ML code is significantly different from a Jupyter notebook, and we treat it with the same rigor as any other backend service.
Integrate and deploy
We integrate the AI feature into your existing application and deploy it to your infrastructure. Your frontend team gets clean API endpoints to call, and your ops team gets dashboards to monitor model performance and costs over time.
Learn More
Subscription vs Freelancers
See why startups are switching from freelancers to dev subscriptions.
Subscription vs Traditional Agency
How a development subscription compares to hiring a traditional agency.
Complete Guide to Productized Development
Everything you need to know about the productized development model.
How AsyncForge Works
From signup to shipped code in four simple steps.