Machine Learning ML models that ship to production — not notebooks that never deploy.
Develop predictive models and intelligent systems that learn and adapt. Delivered by our AI & Machine Learning team in 4 to 10 weeks. USD pricing.
Part of our AI & Machine Learning practice — see related capabilities below.
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Who we've built for.
How we work
- Focus
- Machine Learning
- Stack
- OpenAI GPT-4o · Anthropic Claude · LangChain · LangGraph · Pinecone · Weaviate · pgvector · Python · FastAPI
- Integrations
- Slack · Microsoft Teams · Salesforce · HubSpot · Zendesk · custom APIs · vector databases
- Typical timeline
- 4 to 10 weeks
- Compliance
- EU AI Act · GDPR · SOC 2 · zero-retention API options · EU data residency
We build and deploy custom machine learning models: churn prediction, demand forecasting, fraud detection, and recommendation engines. Full pipeline from data prep to monitoring in production.
Recent AI builds — named clients
Built a complete Legal AI Contract Review & Drafting platform from scratch, with LLM fine-tuning, MS Word add-in, and multi-dashboard ecosystem
Read case study →
All-in-one hospital platform with AI medical history in seconds, staff, patients, inventory, CRM & finance
Read case study →
Unified company knowledge graph, graph RAG, SOC/ISO PR scans & LLM implementation.md from every source
Read case study →
1.6B EAN product API, Next.js dashboard, Amazon/Walmart Chrome extension with Keepa charts
Read case study →What we deliver — Machine Learning
Predictive analytics models
Churn, LTV, and demand forecasting with explainable outputs for business teams.
Classification and NLP models
Ticket routing, sentiment analysis, and document classification at scale.
Recommendation engines
Product, content, and next-best-action recommendations with A/B test hooks.
Computer vision pipelines
Quality inspection, OCR, and visual search integrated into ops workflows.
MLOps and deployment
Model serving on AWS SageMaker, Azure ML, or custom Kubernetes with drift detection.
Feature stores and data pipelines
Reliable feature engineering feeding both batch and real-time inference.
Capability detail: AI & Machine Learning, AI Chatbot Development, Generative AI, Computer Vision, NLP Development, AI Consultation.
Typical engagement ranges
AI feature integration
From $7,000
- LLM feature inside existing product.
- Single use case, 4 to 6 weeks.
- OpenAI or Claude API.
Production AI system
From $12,000
- RAG, agents, or multi-step workflows.
- Evaluation harness and monitoring.
- Typical 6 to 10 weeks.
Enterprise AI program
From $35,000
- Multiple AI products or divisions.
- Compliance documentation.
- Dedicated team retainer option.
FAQ
Not always. We assess data quality and volume on the scoping call. Some problems need transfer learning or synthetic data; others need more collection first.
Containerized inference APIs, batch scoring jobs, or edge deployment depending on latency needs. Monitoring for drift and performance regression included.
Often. We audit the pipeline, retrain with better features, and fix deployment issues that cause silent accuracy decay.
Structured prediction on tabular or time-series data usually fits classical ML. Unstructured text and reasoning tasks fit LLMs. We recommend honestly on the call.