Generative AI — large language models, copilots, content and code assistants — is where most of the hype lives, which makes honest consulting more valuable, not less. The job is to separate the use cases where generative AI delivers real value from the many where it doesn’t, and to deploy it safely. This page explains how. (dgm implements osFoundry, a separate company’s platform — we are not osFoundry.)

What generative AI is good at — and what it isn’t

Generative AI excels at tasks involving language and unstructured content: drafting and editing, summarizing long material, answering questions over your own documents, assisting customer support, and accelerating knowledge work. It’s weaker — and sometimes dangerous — when applied to problems that demand exactness, auditability, or determinism, where a rules-based automation or a traditional model would be more reliable.

Honest generative-AI consulting names that boundary clearly. A firm that says generative AI is the answer to everything is selling hype; the value is in matching the tool to the task.

The thing that makes it work: grounding

A generic chatbot is a parlor trick. Generative AI becomes a business tool when it’s grounded in your data — your documents, policies, and records — so its answers reflect your reality rather than the open internet. Grounding (often via retrieval over your own content) is also the main defense against hallucination, because the model is answering from your sources instead of inventing. Connecting the model to your data and workflows is therefore the core of the work — and exactly where integration comes in.

Handling the real risks

Three risks deserve direct attention in any generative-AI project:

  • Hallucination. Models can be confidently wrong. Grounding, citations back to source, and human-in-the-loop review for high-stakes outputs keep this in check.
  • Data privacy. What can the model see, and where does your data go? A model-agnostic setup that keeps your data under your control matters here — see also AI Security & Governance Consulting.
  • Over-application. Using a probabilistic model where a deterministic automation would be safer and cheaper. Sometimes the right answer is “don’t use generative AI for this part.”

What dgm delivers, and what it costs

dgm structures the work in two fixed-price stages:

  • Assessment + roadmap ($399, one-time). We identify your highest-value generative-AI use cases and produce a concrete plan, including which tasks are not a fit.
  • Full integration ($3,999/month). We ground the models in your data, build the copilots and agents, integrate them into your workflows, and train your team — with ongoing optimization and no per-seat fees.

We specialize in osFoundry because it’s model-agnostic — you can use the best model for each task and switch as the landscape changes, avoiding lock-in to a single lab.

How dgm helps

dgm brings generative AI into US businesses the way it actually delivers value — grounded in your data, integrated into your workflows, and honest about its limits. If you’d rather explore the platform yourself first, you can go straight to osFoundry; if you want generative AI put to work properly, that’s where dgm comes in.