Generative AI services grounded in your own content LLMs that answer from your documents, inside the tools your teams use

Generative AI suits some jobs well and others badly. We list the jobs where your people read, write or search the most, rank them by the hours they'd save and the risk of a wrong answer, and start with one that's high on the first and low on the second.

We configure the model that fits the job: Anthropic Claude, OpenAI GPT, Google Gemini, Meta Llama or an open-source model you host yourself. We test it on your real cases, tune the settings, and fine-tune on your data only when that beats better prompts and better sources.

Prompts are the instructions behind every answer. We write, test and version them like code, so a change that makes one answer better doesn't make another one worse.

Retrieval-augmented generation, or RAG, makes the model answer from your content instead of from memory. We index your documents, wikis and records, keep the index current, and apply the same access rules as the systems they came from, so nobody sees an answer from a document they couldn't open.

Every answer shows its sources, so people can check it in a click.

• Structured and unstructured sources in one index
• Access rules carried over from SharePoint, Confluence and your core systems
• Answers that say when the content doesn't cover the question

Assistants are used when they sit where people already work. We build them into Teams, Outlook, your CRM, your intranet and your service portal.

When the job is a task rather than an answer, such as closing a ticket or updating a record, we build an agent that prepares the work and a person approves it.

Compliance signs off on generative AI when they can see what it does. Every question, source and answer is logged, sensitive data is masked before it reaches a model, and the model provider is set to keep none of it.

We test for wrong answers, leaks and prompt attacks before launch, and keep testing after it.

Generative AI creates new text, images or code from patterns it learned in a large amount of data. A large language model predicts the most likely next words, so it can draft, summarize and answer. Grounding it in your own content keeps its answers accurate for your business.

Common uses are answering staff and customer questions from your documents, drafting replies and reports, summarizing long files, and preparing tasks for a person to approve. Start where people spend the most time reading or writing.

It depends on the use case, how much content we index, the systems we connect, and how many people use it. We quote the first use case at a fixed price after a short assessment, and model usage costs are estimated up front.

Consulting, LLM configuration and fine-tuning, prompt engineering, enterprise RAG, AI agents, integration into your tools, and governance and security. We also run the assistants after launch.

Ask to see how its answers are grounded and logged, how access rules are kept, and how it measures accuracy after launch. Then ask what number the first use case will move.