Do we need a lot of data to get started with ML?

It depends on the problem. For LLM-powered features (summarization, extraction, Q&A), you can start with zero training data. For custom ML models, we need enough labeled examples — usually hundreds, not millions. We'll tell you honestly if your data is ready.

How do you prevent AI models from degrading over time?

Every model we ship includes monitoring for drift, scheduled retraining pipelines, and alerting when accuracy drops below thresholds. Models are living systems — we build the infrastructure to keep them healthy.

Service pillar

AI & Machine Learning

Intelligent solutions that learn and adapt.

From predictive analytics to AI agents, we build production-grade AI systems that deliver measurable business impact — not just buzzwords.

Book a 20-min scoping call →Try our AI on your problem ↑

What you get

AI agents & automation — custom outcome-targeted agents that watch, decide, and act, with humans in the loop where it matters
Custom AI apps & chatbots — RAG-powered knowledge bots with hybrid search, reranking, and cited answers
Predictive analytics & ML models for churn prediction, demand forecasting, anomaly detection, and quality scoring
Computer vision — image classification, object detection, OCR, and document understanding pipelines
Generative AI & content pipelines, plus AI strategy, governance, and team training workshops

When this is the right fit

Production-Grade Models — Battle-tested ML models with monitoring, drift detection, and automated retraining — not just notebooks.
Responsible AI — Built-in bias detection, explainability, and governance aligned with NIST AI RMF and EU AI Act compliance.
Full-Stack AI Engineering — From data pipeline to model serving to frontend — we build the entire stack, not just the model.
Multi-Model Architecture — Orchestrate Claude, OpenAI, open-source models, and custom fine-tunes — pick the right model for each task.
Measurable ROI — Every project starts with a business outcome metric. We measure what matters, not just model accuracy.
Rapid Prototyping — Working proof-of-concept in 2-3 weeks. Validate ideas fast before committing to full production builds.
Human-in-the-Loop — Smart escalation logic and confidence thresholds ensure humans stay in control where it matters.
Observability Built In — Full telemetry — logged decisions, cost tracking, latency monitoring, and eval suites from day one.

Sample builds

Intelligent document processing
Extract, classify, and route information from invoices, contracts, and forms — reducing manual data entry by 85% with human review for edge cases.
Product recommendation engine
Collaborative filtering + content-based hybrid model serving personalized recommendations — 23% increase in average order value.
Predictive demand forecasting
Time-series ML model trained on sales history, seasonality, and external signals — reducing overstock by 30% and stockouts by 45%.

Tech we reach for

Python / PyTorch / scikit-learn
Anthropic Claude / OpenAI
LangChain / LangGraph
Hugging Face
pgvector / Pinecone
MLflow / Weights & Biases
AWS SageMaker / Modal

FAQ

It depends on the problem. For LLM-powered features (summarization, extraction, Q&A), you can start with zero training data. For custom ML models, we need enough labeled examples — usually hundreds, not millions. We'll tell you honestly if your data is ready.