Technology & Tools
AI tools we work with.
We match the tool to the problem — not the hype cycle. Most AI projects succeed or fail based on data quality and problem framing, not which platform you pick.
The tool is the easy part.
Every AI vendor promises transformation. The reality is more specific: a churn model that flags at-risk accounts, a document processor that cuts review time in half, a forecasting pipeline that actually runs in production.
We've built these across industries using the tools below. We pick the platform based on your data, your infrastructure, and what your team can maintain after we leave.
Machine Learning
For prediction, classification, and pattern detection. Most business ML problems don't need deep learning — they need clean data and the right algorithm.
Python / scikit-learn↗
Our default for most ML work. Classification, regression, clustering, and feature engineering — battle-tested and well-documented.
TensorFlow↗
We use TensorFlow when the problem requires deep learning — image recognition, NLP at scale, or complex sequential data.
Qlik AutoML↗
Automated ML for business users already in the Qlik ecosystem. Good for teams that want predictions without writing code.
Generative AI & LLMs
For text generation, document processing, and conversational interfaces. We build applications that use LLMs — not demos that just call an API.
OpenAI / GPT↗
The most capable general-purpose LLMs available. We use these for summarization, extraction, classification, and conversational AI.
LangChain↗
Framework for chaining LLM calls with retrieval, memory, and tool use. We use it to build production AI applications, not just prompts.
Hugging Face↗
Open-source model hub. We use it when clients need on-premise deployment or want to fine-tune models on proprietary data.
Google Gemini↗
Google's multimodal foundation model family powering advanced reasoning, code generation, and vision capabilities.
Anthropic Claude↗
Anthropic's frontier AI assistant models designed for safety, reliability, and complex enterprise reasoning tasks.
Cloud ML Platforms
For training, deploying, and monitoring models in production. The right platform depends on where your data already lives.
Azure ML↗
Our recommendation for Microsoft-stack organizations. Handles training, deployment, and model monitoring in one managed environment.
AWS SageMaker↗
Full lifecycle ML on AWS — from notebook experimentation to production endpoints with autoscaling.
Voice AI & Agents
Platforms for building AI-powered voice agents and conversational interfaces.
Vapi↗
Developer platform for building, testing, and deploying voice AI agents with real-time conversation capabilities.
Related Services
Trying to figure out where AI fits?
We'll help you assess readiness and identify the right use case — before you pick a tool.