AI Development Services
Built for Production
Zyptr builds AI systems that actually work in production — not demos, not PoCs that never ship. Our AI team has deployed ML models handling millions of inferences daily, RAG systems serving enterprise knowledge bases, and computer vision pipelines for regulated industries.
Whether you need to fine-tune an LLM on your proprietary data, build a document Q&A system, automate a complex business process, or integrate AI capabilities into your existing product, we've done it before and we'll do it right.
What We Build
LLM Fine-tuning
Customise foundation models (GPT, Llama, Mistral) on your domain-specific data for dramatically better performance on your use case.
RAG Architectures
Connect LLMs to your knowledge base — documents, databases, APIs — for accurate, citation-backed answers. Built with LangChain, LlamaIndex, or custom pipelines.
Custom ML Models
Classification, regression, anomaly detection, recommendation systems — trained on your data, optimised for your constraints.
Natural Language Processing
Text classification, entity extraction, sentiment analysis, summarisation, and information retrieval at production scale.
Computer Vision
Object detection, image classification, OCR, medical imaging analysis, defect detection — production-grade CV pipelines on AWS, Azure, or on-premise.
Intelligent Process Automation
Automate complex workflows that previously required human judgment. Combine rule-based automation with ML models for robust, auditable systems.
Data Engineering & ETL
Build the data infrastructure your AI needs. ETL pipelines, feature stores, data quality monitoring, and real-time data processing with Kafka, Spark, and Airflow.
AI Strategy & Roadmap
Not sure where to start with AI? We help you identify high-impact use cases, assess feasibility, build a data strategy, and create a prioritised AI roadmap.
Our AI Development Process
Discovery & Feasibility
We assess your data, define success metrics, and evaluate feasibility before writing a single line of code. Many AI projects fail because they skip this step.
Data Strategy
We audit your existing data, identify gaps, and design the data pipeline architecture your AI system needs. Good AI is 80% good data.
Model Selection & Development
We select or build the right model for your use case — balancing accuracy, latency, cost, and interpretability requirements.
Integration & Deployment
We integrate the AI system with your existing infrastructure and deploy to production with monitoring, alerting, and rollback procedures.
Monitoring & Iteration
AI systems need ongoing care. We monitor model performance, detect drift, and continuously improve based on real-world feedback.
Related Industries
AI Services FAQ
Let's discuss your AI use case. Free consultation, honest feasibility assessment.