AI for business

ByteWave deploys artificial intelligence in companies: automating repetitive processes, document classification, sales predictions, OCR/NLP, AI agents with RAG. Pragmatic approach - we start with process audit and identify where AI delivers the highest ROI in 3-6 months. POC in 2-3 weeks, production deployment in 2-4 months.

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Prognoza Q1 2026 na podstawie modelu predykcyjnego:

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What we build

Solutions tailored to the business

Document automation (OCR + NLP)

Extracting data from invoices, contracts, orders, CVs. OCR (Tesseract, AWS Textract, Google Vision) plus language models for classification and structuring. Eliminating manual re-entry, reducing errors by 70-90%, saving hours of administrative work.

Predictions and analytics

Sales forecasting, anomaly detection, customer segmentation, churn prediction, price optimization. Classical models (XGBoost, Random Forest) and deep learning (TensorFlow, PyTorch). Dashboards with prediction visualization and confidence scores.

AI agents with RAG

Intelligent agents combining LLM with company knowledge base and external tools. Legal assistant analyzing contracts, sales assistant generating offers, HR assistant screening CVs. LangChain, LlamaIndex, function calling, multi-agent workflows.

Computer Vision and image analysis

Product classification, defect detection on production lines, quality control, surveillance image analysis, OCR of commercial documents. YOLO, OpenCV, TensorFlow Object Detection. Integration with existing cameras and warehouse systems.

Technologies

Tech stack that scales

ML models and frameworks

OpenAI / Anthropic / Google

LLM models available via API: GPT-4o, Claude Sonnet, Gemini. Best choice when deployment speed is key, no need for fine-tuning.

TensorFlow / PyTorch

Deep learning libraries for training custom models. Classification, NLP, computer vision, predictions. Full control over architecture and training data.

XGBoost / scikit-learn

Classical ML for tabular data: sales predictions, churn, customer segmentation. Often faster and cheaper than deep learning for structured problems.

Hugging Face Transformers

Pretrained NLP models for classification, NER, summarization, translation. Multilingual support. Fine-tuning on company data for specialized applications.

AI infrastructure

AWS SageMaker / Azure ML

Managed services for training, deployment and monitoring of models. Auto-scaling, A/B testing, model registry, ML CI/CD pipeline.

Vector databases

Pinecone, Weaviate, pgvector, Qdrant. Storing embeddings for RAG and semantic search. Scaling to millions of documents.

GPU / inference acceleration

NVIDIA A100, AWS Inferentia, Google TPU. Docker + GPU containerization. Cost optimization for high-performance model inference.

MLOps (MLflow / Weights & Biases)

Experiment versioning, metric tracking, model drift monitoring in production. Reproducible training pipelines.

Data and integrations

Python / Pandas / Spark

ETL, cleaning, transformations, feature engineering. Pandas for smaller datasets, Spark for large volumes (terabytes).

Data warehousing

BigQuery, Snowflake, Redshift, ClickHouse. Central data warehouses for analytics and ML. Integration with company source systems.

OCR and Computer Vision

AWS Textract, Google Vision, Tesseract, OpenCV. Document data extraction, image classification, object detection.

AI APIs and webhooks

Integration of models with existing systems: CRM, ERP, helpdesk, e-commerce. Synchronous (REST) or asynchronous (queue) prediction calls.

How we work

From idea to deployment

Discovery

Analysis of needs, business and technical requirements. We define goals and project scope.

Design

UX/UI design, system architecture, prototyping. We visualize the solution before coding.

Development

Iterative product building with regular demos. Agile, transparent process, constant contact.

Deployment

Testing, deployment, user training. Technical support and growth after launch.

Back to Artificial intelligence — part of

Frequently asked questions

Questions? We have answers

Where do we start with AI deployment in a company?

With a process audit (1-2 weeks, optionally free). We map company operations, identify where AI delivers the highest ROI in 3-6 months (typically: documents, support, sales, predictions). Define MVP with measurable success metrics. Then POC (2-3 weeks) validating assumptions on real company data.

How much does AI deployment cost?

POC validating assumptions: 1,800-3,500 EUR. Production deployment of document automation or chatbot with RAG: 4,500-11,000 EUR. Complex predictive systems or multi-agent AI: 11,000-35,000 EUR. Plus monthly API and hosting costs (typically 100-1200 EUR depending on volume). Free consultation and quote within 48h.

Do we need our own training data?

Depends on the use case. Chatbots and agents with RAG work with existing knowledge base (docs, FAQ, products) - training data not required. Document classification or sales predictions require historical data - we help collect, clean, label it. Often 500-2000 examples are enough for a good start.

How do we measure ROI from AI deployment?

Before the project we define KPIs: time savings, error reduction, conversion increase, support cost reduction. We measure baseline before deployment and after 1, 3, 6 months. Typical ROI: document automation 200-400% in first year, support chatbot 150-300%, sales predictions 100-250%.

Will AI replace our employees?

No - it automates repetitive, mechanical tasks. Employees gain time for creativity, empathy, decision-making. Typical effect: customer service handles 3x more inquiries, sales team closes 30-50% more deals, admin team processes invoices without manual entry. AI is a multiplier, not a replacement.

Have a project idea?

Let's talk about how we can make it happen.

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