Generative AI consulting grounded in your own documents and data

Where does generative AI consulting pay back in a mid-size company?

It pays back where staff spend hours reading, searching, summarizing or drafting from documents the company already has. Internal question-answering over policies and procedures, first drafts of proposals and reports, summarizing long contracts or call notes, and pulling fields out of PDFs are the usual front-runners.

Adoption is already broad. The Federal Reserve reported in an April 2026 note that 41% of US workers said they used generative AI at work as of November 2025. The problem for most companies is not getting people to try it; it is that each person uses a different tool, with different data terms, and nobody can measure what it is worth. Generative AI consulting focuses that activity on a few shared tools with measured results.

• Staff assistant over SharePoint or Google Drive policies, with citations.
• Proposal and report drafting from approved templates and past work.
• Contract, call and ticket summarization for managers.
• Field extraction from invoices, forms and purchase orders.

How do you stop a generative AI tool from making things up?

You ground it in your own content and make it show its sources. The pattern is retrieval: the tool searches an index of approved documents, passes the relevant passages to the model, and returns an answer with citations a user can click to verify.

We design the retrieval layer (which documents, how they are split, how permissions carry over from SharePoint or Google Drive), choose a vector store such as Pinecone, Qdrant, Supabase with pgvector or Azure AI Search, and set rules for when the tool should say it does not know. We then test it against an eval set of real questions with agreed answers, so accuracy is a number rather than a feeling.

Which generative AI platform should we use?

Start with what your staff already work in, then test for the custom pieces. If your company runs on Microsoft 365, Copilot and Copilot Studio may cover the first needs. If you are on Google Workspace, Gemini is the natural first look. For custom assistants, we compare OpenAI, Claude and Gemini directly on your tasks.

Some companies need more control over where data goes. Amazon Bedrock offers models from several providers inside an AWS account, Microsoft Foundry does the same inside Azure, and open-weight models such as Llama, Mistral or Qwen can run in your own cloud. We set out the trade-offs in accuracy, cost per task and data terms, and you make the call.

How do you measure whether generative AI is working?

We measure it against a baseline taken before launch: time per task, error rate and volume. After launch we track the same numbers, plus accuracy on the eval set and how often users accept, edit or reject the output.

Every production tool logs prompts and outputs, with sensitive fields masked where needed. Logs show which questions fail, which documents are missing and where staff stop trusting the answers. That feedback drives monthly improvements, and it also gives leadership a clear answer when they ask what the tool is returning for the money.

What does generative AI cost to run?

Running cost is mostly model usage, retrieval hosting and any per-seat licenses, and it varies with volume and model choice. We estimate cost per task during testing and set a monthly cap per workflow so a spike in usage triggers an alert before it becomes a surprise invoice.

Our own consulting and build work is quoted at a fixed price after a free review. For a market view of consultant rates, see our AI consultant cost guide. You hold the accounts with each vendor, so usage bills come to you directly with no markup.

What generative AI risks need a human in the loop?

Anything that reaches a customer, moves money or touches regulated data needs a person to approve it first. That includes outbound emails drafted by AI, claims or billing decisions, and any output based on health or financial records.

We build those checks into the workflow rather than relying on a policy alone. Drafts land in a review queue, confidence thresholds route uncertain answers to a named owner, and synthetic images or video are labeled. Where the EU AI Act's transparency rules apply from August 2, 2026, users should be told they are dealing with an AI and synthetic media should be marked. Our case studies show the same principle in other work: a therapy services provider's AppSheet portal used validation and approvals to stop errors at source.