AI Models & Technologies We Use
We leverage the most advanced AI models and technologies to build powerful, scalable solutions tailored to your business.
Production-grade LLMs we deploy
Anthropic Claude
Long-context reasoning, tool use, agentic workflows, computer use. Sonnet/Opus/Haiku.
OpenAI GPT
GPT-4o, GPT-4o-mini, o1 reasoning. Function calling, structured outputs, vision, voice.
Google Gemini
Multimodal models with strong native code + reasoning capabilities.
LLaMA (Meta)
Open-weight models for self-hosted, on-prem, or fine-tuned deployments.
Mistral
Fast, efficient open models for cost-sensitive production workloads.
Custom Fine-Tunes
Custom models trained on your domain when off-the-shelf isn't enough.
What we build on top of these models
Natural Language Processing
Understanding and generating human language at production quality.
Generative AI
Content, code, structured data, and synthetic media generation.
Automation Workflows
Multi-step plans with tool use, retries, and human-in-the-loop checkpoints.
Decision-Making Systems
Policy-aware agents that combine rules engines with LLM judgment.
RAG & Knowledge Retrieval
Doc-grounded answers from your policies, KB, tickets, contracts.
Vision & OCR
Image, screenshot, document understanding for legacy + modern flows.
We select and combine the best technologies based on your specific needs — ensuring performance, scalability, and reliability. Sometimes that's a single model. More often it's a routed pipeline of specialized models orchestrated by an agent runtime.
AI tools & model guides
ChatGPT vs Claude
Which model for your build.
OpenAI vs Gemini
Platform trade-offs.
CrewAI vs AutoGen
Multi-agent frameworks.
AI API vs Custom Models
Buy, fine-tune, or build.
Fine-Tuning LLMs
When it's worth it.
AI Integration Guide
Wire models into your stack.
Related: OpenAI / GPT Solutions · Claude Integrations · API Integrations · AI Agents
Frequently asked questions
Which AI model should my business use?
It depends on the task — Claude often leads on long-context reasoning, OpenAI on ecosystem breadth, Gemini on Google-stack fit. We build behind a routing layer so you can use the best model per task and swap as they improve.
Do I need a vector database?
If you want AI to answer over your own documents or data (RAG), yes — a vector database (Pinecone, Weaviate, Chroma) stores embeddings for retrieval. We pick and set this up as part of the build.
What is an MCP server?
The Model Context Protocol (MCP) is a standard way to give AI models secure, structured access to your tools and data. It's becoming the common interface for connecting agents to systems.
Ready to pick the right stack?
We'll map your workflows, identify the highest-ROI agents, and ship a working pilot within weeks — not quarters.