Services
We build AI for a specific job. The work is agent development, AI added to an application you already run, and new applications designed as a bot or as generative AI. MCP, RAG, and LangChain are how those applications reach your systems, your documents, and the model.
An agent is built to carry a task, not to stop after one reply. It can read the case, call a tool, draft the next step, and hand the result back. We design that path for intake, follow-up, and decisions that need a draft first. A person still confirms the step before a record changes or a message goes out.
We integrate AI into existing applications to automate tasks, streamline workflows, and boost productivity—from drafting responses and summarizing cases to extracting data—while keeping your team in control of information and approvals.
We build custom AI applications tailored to your business needs, from conversational bots to generative AI tools that create content, summarize information, and automate tasks. Our solutions combine intuitive experiences, secure integrations, and human oversight to deliver reliable results while keeping your business in control.
AI delivers the most value when it can access the right information and tools. We connect AI models to your business systems and documents using MCP for tool integration, RAG for retrieving relevant information, and LangChain for building multi-step AI workflows. We work with leading AI models and platforms, including Claude, ChatGPT, Gemini, Llama, and DeepSeek, as well as Azure AI Foundry and Amazon Bedrock, selecting the right fit for your business needs.
We use the Model Context Protocol (MCP) to safely expose capabilities directly to the AI model. Whether performing a search, looking up a record, or running a status check, each capability is treated as an explicit tool bound to the user's role—with every single execution securely limited and fully logged.
Our Retrieval-Augmented Generation (RAG) system grounds every AI response in your actual business material—including internal policies, contracts, product notes, and user guides. The model drafts answers exclusively using the specific passages it finds, attaching the exact source document directly to the response so your team can verify it instantly.
We use LangChain to dynamically assemble every AI request—binding your instructions, the current user record, and the relevant data passages into a single context-rich payload. When a workflow demands a coordinated sequence of steps rather than a single static reply, LangGraph choreographs the multi-turn logic to see the task through to completion.
Our platform remains entirely model-agnostic, running seamlessly across Claude, ChatGPT, Gemini, Llama, or DeepSeek—whether deployed directly or orchestrated through enterprise hubs like Azure AI Foundry and Amazon Bedrock. We select the optimal model for each specific task while strictly limiting the context payload to only what that task requires.
Technology Stack
Claude
ChatGPT
Gemini
DeepSeek
Azure Foundry
AWS Bedrock
MCP
RAG
LangChain
LangGraph