AI integrations for New England businesses.

What we build

• RAG systems: chatbots and search tools grounded in your own docs, wikis, and data
• AI-powered internal tools for legal, accounting, real estate, and healthcare workflows
• Custom voice agents (we built the one that answers JTPCK's phone number)
• Email triage, summarization, and automated reply drafting
• Content generation pipelines with human review built in
• AI features embedded directly inside existing Rails, Node, or Django apps

Specialized service

AI Fine Tuning

When prompts and retrieval are not enough, we train models on your own historical data, terminology, and workflows so they respond with the consistency and depth your team actually needs.

Specialized service

RAG AI Systems

We know how to build retrieval-augmented generation systems properly. We built one for the Appalachian Mountain Club's Boston chapter and even turned it into a native macOS app, giving their team a faster way to search institutional knowledge and get grounded answers from their own materials.

RAG works well when a company has a large body of documentation, policies, project files, support history, or research that people need to search and use quickly. Instead of asking a model to guess, a RAG system pulls the relevant source material first and generates answers against that context, which means fewer hallucinations, more useful citations, and much better performance on company-specific questions.

Companies hire us for this when they want an internal knowledge assistant, a support co-pilot, a document search tool, or a customer-facing AI experience that can answer questions based on real source material. We handle the ingestion pipeline, embeddings, retrieval logic, ranking, interface design, and deployment so the system is something your team can actually rely on.

Specialized service

On-Premise AI

For organizations that cannot send their records anywhere, we install a dedicated on-site appliance that runs large language models inside your building. The data never leaves the room, the costs are predictable, and the reasoning layer lives next to the records it reasons about.

Our AI stack

We build against the APIs that matter: Anthropic's Claude, OpenAI, Google's Gemini and Conversational Agents, and open-source models when privacy or cost demand it. For retrieval we use Postgres with pgvector, Pinecone, or Weaviate depending on scale. Everything is wired in with careful attention to prompt caching, evals, and keeping costs predictable.

Who we work with

We partner with teams across Greater Boston (the Seaport, Kendall Square, Back Bay, Cambridge, Somerville) and throughout Massachusetts, New Hampshire, Maine, Vermont, Rhode Island, and Connecticut. We also run hands-on AI workshops for local teams that want to train their people rather than outsource everything.