AI & Machine Learning
Custom LLMs, RAG Pipelines
& Computer Vision
Production-grade AI for enterprise — from LLM fine-tuning & RAG pipelines to computer vision and predictive analytics. We build AI that creates measurable business value for clients in 18+ countries.
AI services
Everything in the AI stack
Large Language Model Integration
GPT-4, Claude, Gemini API integration; custom fine-tuning on proprietary data using LoRA, QLoRA, and RLHF.
RAG Pipeline Development
Document ingestion, vector databases (Pinecone, Weaviate), semantic search, and hybrid retrieval systems.
Computer Vision
Object detection, OCR, medical imaging analysis, defect detection, and real-time video analytics.
Predictive Analytics
Time-series forecasting, churn prediction, demand forecasting, fraud detection, and recommendation engines.
NLP Systems
Sentiment analysis, entity extraction, document summarization, classification, and multilingual processing.
MLOps & AI Infrastructure
Model training pipelines, monitoring, A/B testing, drift detection, and scalable inference infrastructure.
AI Chatbots & Agents
Multi-turn conversational AI, tool-calling agents, customer support automation, and agentic workflows.
AI Consulting
Strategy, vendor evaluation, data readiness assessment, ROI modeling, and AI roadmap development.
Technologies
Our AI tech stack
Case studies
AI systems we've deployed
AI Recruitment Engine (Singapore)
ML-powered talent matching platform. 98% CV screening accuracy, 68% faster time-to-hire, and 50K+ active users.
AI Fraud Detection (UK Fintech)
Real-time transaction fraud detection with 99.3% detection rate and 0.001% false positive rate at sub-millisecond latency.
Computer Vision QC (Germany)
Visual quality control system for a German manufacturer, reducing defect escape rate by 40% and 3x faster inspection.
FAQ
Common AI questions
How long does it take to build a custom AI system?
Simple AI integrations (connecting OpenAI or Claude APIs to your product) can take 3–6 weeks. Custom fine-tuned models and RAG pipelines typically require 2–4 months. Full AI platforms with MLOps infrastructure take 4–8 months depending on data maturity.
How much does AI development cost?
AI projects range from $15K for a basic chatbot integration to $500K+ for enterprise ML platforms. Key cost drivers are data preparation, training compute, model complexity, and ongoing inference infrastructure. We provide transparent breakdowns after a discovery call.
Do you fine-tune existing models or build from scratch?
Almost always fine-tune — foundation models (LLaMA, Mistral, GPT) are incredibly powerful starting points. We use LoRA/QLoRA for efficient fine-tuning on your proprietary data. We only build custom architectures for highly specific domains like medical imaging where pre-trained models fall short.
How do you ensure AI accuracy and reliability?
We establish baseline metrics before any training, use holdout test sets, implement automated evaluation pipelines, and monitor production drift. For LLMs, we use RAGAS for RAG evaluation and custom rubrics for generation quality. All models are validated against your business KPIs before deployment.
Can you integrate AI into our existing software?
Yes — AI integration is one of our most common engagements. We connect AI capabilities via API to your existing stack, or build AI microservices that fit into your architecture. We work with any backend language or database.
What data do we need to start an AI project?
It depends on the approach. For RAG/retrieval systems, you need structured documents or databases — even 1,000 documents can be effective. For fine-tuning, 1K–100K labeled examples are typical. For predictive models, at least 12 months of historical transaction data is ideal. We assess your data in discovery at no cost.
Start your AI project
From data to intelligence
Tell us about your AI challenge and get a tailored proposal within 24 hours.