We implement artificial intelligence and machine learning into business processes: AI assistants, ML models, Computer Vision, NLP, recommendation engines, and predictive analytics. Transparent pricing, fixed timelines.
7 services · from $2,400
AI implementation costs from $2,400 to $60,000. AI assistant for business — from $3,600 (1-2 months). ML model — from $4,800 (2-5 months). Computer Vision — from $6,000. NLP system — from $4,200. Recommendation system — from $4,800. AppStar has been implementing AI since 2018, 30+ ML projects in production.
We study your data, define success metrics, choose approach (ready-made model, fine-tuning, or custom development). Form the dataset.
2
Model Development & Training
Build the data processing pipeline, train the model, optimize hyperparameters. Iteratively improve accuracy.
3
Testing & Validation
Validate the model on test data, measure metrics (accuracy, precision, recall). A/B test against the current process.
4
Deployment & Monitoring
Deploy the model to production, set up drift and quality monitoring. Automatic retraining on degradation.
Return on Investment
Automate 70-90% of routine decisions
AI handles repetitive tasks: ticket classification, document processing, answering standard questions — freeing employees for complex work.
Prediction accuracy 85-95%
ML models predict demand, customer churn, manufacturing defects with accuracy unattainable by humans when processing large data volumes.
ROI in 4-8 months
Reduced manual processing costs, higher conversion through personalization, fewer errors — investment pays back within the first six months.
FAQ
How much does AI implementation cost for a business?
Cost depends on the task. Integration of a ready-made AI model (GPT, Claude) — from $2,400. Fine-tuning a model on your data — from $4,800. Custom ML model — from $9,600. Computer Vision — from $6,000. We calculate the exact cost after analyzing the task and data.
Which is better: GPT API or a custom ML model?
GPT/Claude API is suitable for: chatbots, text generation, summarization, Q&A over knowledge bases. Quick start (2-4 weeks), low cost. A custom ML model is needed when: accuracy >95% is critical, domain-specific task, data cannot be sent to the cloud, or edge-device operation is required. We help choose the optimal approach.
How much data is needed to train an ML model?
Depends on the task. For text classification — from 1,000 labeled examples. For Computer Vision — from 5,000 images per class. For recommendation systems — from 10,000 interactions. With insufficient data, we use transfer learning, data augmentation, and few-shot approaches. GPT-based solutions work with minimal data thanks to prompt engineering.
What accuracy does an ML model provide?
Typical accuracy: text classification — 90-97%, image recognition — 92-99%, demand forecasting — 85-93%, anomaly detection — 88-96%. Accuracy depends on data quality, task complexity, and sample size. At the PoC stage, we demonstrate real metrics on your data before full development begins.
How long does AI implementation take?
PoC (proof of concept) — 2-4 weeks. AI assistant on a ready-made model — 4-8 weeks. Custom ML model — 3-6 months. Computer Vision system — 2-6 months. We start with a PoC: in 2-4 weeks we show a working prototype on your data so you can evaluate the result before full implementation.
Is a GPU server required to run AI?
Not always. GPT/Claude API solutions work via cloud — you do not need your own server. For custom models there are options: cloud GPU (AWS, GCP) — from $600/month, own GPU server — from $3,600 one-time, model optimization for CPU (quantization, distillation) — cheaper but slower. We select infrastructure based on your budget and speed requirements.
What is RAG and why is it needed?
RAG (Retrieval-Augmented Generation) is a technology that lets AI answer questions from your knowledge base: documents, policies, FAQs. The model does not hallucinate but retrieves a relevant fragment and generates an answer based on it. Use cases: corporate chatbot, documentation search, legal AI assistant. RAG system cost — from $4,800.
How is data security ensured when working with AI?
Multiple levels of protection: 1) On-premise deployment — data never leaves your perimeter. 2) When using cloud APIs — anonymization and PII masking before sending. 3) Data encryption at rest and in transit (AES-256, TLS 1.3). 4) Role-based access control. 5) Audit logs of all AI requests. We sign an NDA before starting work.
Can AI be integrated into our existing product?
Yes, this is one of our core services. We integrate AI via REST API, WebSocket, gRPC, or SDK. Examples: smart search in e-commerce, automatic content moderation, feed personalization, AI hints in SaaS. Integration cost — from $2,400, timeline — from 3 weeks. We work with any stack: Python, Node.js, Java, Go, .NET.
What is MLOps and does my project need it?
MLOps is DevOps for machine learning: automating training, testing, and deploying models. Needed if: the model updates more than once a month, multiple models in production, a team of 2+ ML engineers. Not needed for a single model with rare updates. We set up MLOps on MLflow, Kubeflow, or custom solutions. Cost — from $6,000.
How do you guarantee AI model quality?
Multi-stage quality control: 1) Baseline metrics before development. 2) Cross-validation during training. 3) Testing on a hold-out dataset. 4) A/B testing in production. 5) Data drift and model drift monitoring. 6) Accuracy SLA — if metrics drop, we fix it for free. We provide a detailed report with metrics: accuracy, precision, recall, F1-score.
What is Computer Vision and where is it used?
Computer Vision is a technology for recognizing images and video using AI. Applications: quality control in manufacturing (defects), people/object counting, document recognition (OCR), medical diagnostics, warehouse logistics automation, security systems. Cost — from $6,000. Accuracy — 92-99% depending on the task.
Do you work with multimodal AI?
Yes. Multimodal AI processes multiple data types simultaneously: text + images, voice + text, video + metadata. Examples: AI assistant with voice interface, product analysis by photo and description, video surveillance monitoring with text reports. We use GPT-4o, Claude 3.5, and custom multimodal models.
Can we start with a pilot project (PoC)?
Yes, we recommend starting with a PoC. In 2-4 weeks and $1,800-$3,600 we will: 1) Analyze your data. 2) Train a prototype model. 3) Show real quality metrics. 4) Provide recommendations for full-scale implementation. PoC reduces risks: you see the result before investing in the full project.
What industries do you serve?
We implement AI in 15+ industries: fintech (scoring, anti-fraud), e-commerce (recommendations, search), manufacturing (quality control, predictive maintenance), healthcare (diagnostics), logistics (routing), HR (resume screening), legal (document analysis), marketing (personalization), real estate (valuation), education (adaptive learning). Experience since 2018, 30+ projects in production.