What we build
AI Agents & Automation
Intelligent agents that handle repetitive work: document processing, data extraction, email triage, content classification. LLM-powered workflows that integrate with your existing systems.
- Document extraction and parsing
- Classification and routing
- Human-in-the-loop workflows
Predictive Models
Forecasting, churn prediction, lead scoring, anomaly detection. Models trained on your data, validated against your business metrics, deployed where they create value.
- Demand and sales forecasting
- Customer segmentation
- Risk and fraud scoring
NLP & Text Analytics
Sentiment analysis, entity extraction, summarization, semantic search. Turn unstructured text into structured insights.
- Customer feedback analysis
- Contract and document analysis
- Knowledge base and RAG systems
MLOps & Model Management
Production deployment, monitoring, retraining. Models that stay accurate over time and don't silently degrade.
- Model versioning and registry
- Drift detection and alerting
- Automated retraining pipelines
Our approach to ML projects
We don't build models for the sake of building models. Every project starts with the business problem and works backward to the technical solution.
Feasibility Assessment
Do you have the data? Is ML the right solution? What would success look like? We answer these before writing any code.
Data Preparation
Clean, label, and structure your data. Build the pipelines that will feed production models long after we're gone.
Model Development
Experiment, validate, iterate. We optimize for your business metrics, not just accuracy scores.
Production & Monitoring
Deploy, monitor, maintain. Models in production with alerting, logging, and retraining when performance drifts.
Why work with us
Business-first thinking
We start with the problem you're trying to solve, not the technology we want to use. Sometimes the answer isn't ML at all.
Production mindset
Notebooks don't count as shipped. We build for production from the start: logging, monitoring, error handling, and graceful degradation.
Honest about limitations
We'll tell you when ML isn't the right fit, when your data isn't ready, or when a simpler solution will work better.
Technology we work with
ML Frameworks
- Python (scikit-learn, pandas)
- PyTorch / TensorFlow
- XGBoost / LightGBM
- Hugging Face Transformers
LLMs & AI APIs
- OpenAI / GPT-4
- Anthropic / Claude
- AWS Bedrock
- LangChain / LlamaIndex
MLOps & Deployment
- MLflow / Weights & Biases
- AWS SageMaker
- Docker / Kubernetes
- FastAPI / Flask
Frequently Asked Questions
What's the difference between AI and machine learning?
Machine learning is a subset of AI that enables systems to learn from data. AI is the broader concept of machines performing tasks intelligently. In practice, we use both terms to describe building systems that can make predictions, classify data, extract information, or automate decisions based on patterns learned from data.
Do I need a lot of data to use machine learning?
It depends on the problem. Some ML models need thousands of examples, while modern LLMs and transfer learning techniques can work with much smaller datasets. During our feasibility assessment, we evaluate your data to determine what's possible and what additional data collection might be needed.
How do you handle AI/ML model accuracy and reliability?
We validate models against business metrics, not just technical accuracy. We implement monitoring to detect model drift, set up alerting for performance degradation, and build automated retraining pipelines when needed. Every model ships with logging and observability built in.
Can you integrate AI with our existing systems?
Yes. We build AI solutions that integrate with your existing tech stack via APIs, webhooks, or direct database connections. Whether it's adding document extraction to your workflow or predictions to your CRM, we design for seamless integration.
How long does it take to build and deploy an ML model?
A proof-of-concept can often be built in 2-4 weeks. Production deployment with monitoring, testing, and integration typically takes 6-12 weeks depending on complexity. We always start with a feasibility assessment to give you accurate timelines for your specific use case.
Should I use GPT-4, Claude, or an open-source model?
It depends on your use case, data sensitivity, and cost constraints. GPT-4 and Claude excel at general reasoning and text generation. Open-source models offer more control and can be fine-tuned. For sensitive data, you might need on-premise deployment. We help you evaluate trade-offs and choose the right approach.
What is RAG and when should I use it?
RAG (Retrieval-Augmented Generation) combines LLMs with your own documents or knowledge base. Instead of fine-tuning a model, RAG retrieves relevant context and feeds it to the LLM. It's ideal for chatbots, Q&A systems, and any application where the AI needs access to your specific company data.
How do you handle sensitive data with AI models?
We implement multiple safeguards: data anonymization before training, private cloud or on-premise deployments when needed, access controls, and audit logging. For LLM applications, we can use self-hosted models or enterprise API agreements with data processing commitments.
What's the difference between a chatbot and an AI agent?
A chatbot responds to messages in a conversation. An AI agent can take actions—searching databases, calling APIs, updating records, or orchestrating multi-step workflows. Agents are more powerful but require careful design to ensure they act reliably and within defined boundaries.
Can AI really automate document processing?
Yes, modern AI excels at document processing. We build systems that extract data from invoices, contracts, forms, and emails with high accuracy. The key is designing for the edge cases—we implement confidence scoring and human review workflows for uncertain extractions.
Related services
Data Engineering
Build the data pipelines and infrastructure that feed your ML models.
Full Stack Development
Build applications and interfaces that put your AI capabilities in users' hands.
Analytics & BI
Visualize ML results and track model performance with custom dashboards.
Cloud Services
Deploy ML models on AWS, Azure, or GCP with proper infrastructure and scaling.