Production-grade AI and machine learning systems, not just prototypes.
From RAG pipelines and LLM agents to custom ML models, computer vision, and predictive analytics — we build AI systems with evaluation, guardrails, and monitoring from day one. Your data, your models, your competitive advantage.
LLM-powered systems built for production
RAG (Retrieval-Augmented Generation)
What it's for: Internal knowledge assistants, support copilots, policy Q&A, document intelligence, enterprise search.
What you get:
- Ingestion pipelines & chunking strategy
- Embeddings & vector database setup
- Access control & citation tracking
- Evaluation harness & monitoring
- Hybrid search (semantic + keyword)
LLM Fine-tuning & Customization
What it's for: Specialized tone/format, domain patterns, structured outputs, higher consistency than prompting alone.
What you get:
- Dataset preparation & curation
- Fine-tuning strategy & execution
- Safety filters & benchmarking
- A/B testing vs. base models
- Deployment & version management
AI Agents & Automation
What it's for: Workflows that take action — ticket triage, document processing, approvals, CRM updates, data extraction.
What you get:
- Tool calling & function execution
- Human-in-the-loop approval steps
- Audit logs & role-based permissions
- Multi-step reasoning chains
- Integration with business systems
Custom ML models for your specific problems
Beyond LLMs — purpose-built machine learning for prediction, classification, vision, and language understanding.
Custom ML Model Development
Purpose-built machine learning models trained on your data for classification, regression, clustering, and anomaly detection.
Computer Vision
Image classification, object detection, OCR, and visual inspection systems for manufacturing, retail, and healthcare applications.
Natural Language Processing
Text classification, sentiment analysis, entity extraction, summarization, and language understanding beyond basic LLM prompting.
Predictive Analytics
Forecasting models for demand, churn, pricing, and business metrics using time series analysis and ML regression techniques.
MLOps: From experiment to production
We don't just build models — we operationalize them with monitoring, versioning, and automated pipelines.
Model Training & Experiment Tracking
Reproducible training pipelines with experiment logging, version control, and hyperparameter tracking.
MLflow, Weights & Biases, DVCModel Deployment & Serving
Production model serving with auto-scaling, A/B testing, canary deployments, and real-time inference.
FastAPI, TorchServe, SageMakerMonitoring & Drift Detection
Continuous monitoring for data drift, model degradation, and performance anomalies with automated alerting.
Evidently, Prometheus, GrafanaData Pipeline Engineering
Automated data ingestion, transformation, and feature stores for consistent model inputs across environments.
Airflow, dbt, FeastTrust, Safety & Governance
Tech & platforms
Frequently asked questions
Most teams should start with RAG + evaluation for knowledge-based use cases. Fine-tuning is recommended when it materially improves quality or consistency beyond what RAG achieves. Custom ML models are best for structured data problems (prediction, classification, anomaly detection). We'll help you evaluate the right approach during discovery.
Yes. We integrate with your existing data warehouses, databases, and cloud infrastructure. We can also help set up data pipelines and feature stores if you're starting from scratch. We support AWS, GCP, Azure, and on-premise deployments.
We mitigate hallucinations through careful retrieval design, evaluation harnesses, confidence thresholds, and fallback mechanisms. For safety-critical applications, we implement human-in-the-loop workflows, content filters, and extensive testing. No AI system is perfect, but we build for accountability.
We set up comprehensive monitoring including data drift detection, model performance tracking, latency monitoring, and automated alerting. When model quality degrades, we have automated retraining pipelines and rollback procedures ready.
Both. We use OpenAI, Anthropic, and other API-based models for rapid deployment, and open-source models (Llama, Mistral, etc.) when you need more control, lower costs at scale, or data privacy requirements preclude API-based solutions. We'll recommend the right approach based on your constraints.
Ready to put AI/ML to work for your business?
Get an AI readiness assessment — we'll evaluate your data, identify high-impact use cases, and build a realistic implementation roadmap.
What happens next