Generative AI is good at reading, summarising, drafting and answering. In a business it is only useful when it answers from your policies, contracts, products and records, respects who is allowed to see what, and shows where each answer came from.
We design and build generative AI assistants, search and document AI on your content, with permissions, citations and evaluation built in.
Answers grounded in your documents and systems, with citations.
Users see answers only from content they can already open.
Accuracy measured on real questions before release.
Your data is not used to train public models, and access follows your permissions.
Six patterns cover most enterprise generative AI. Each one is built on the same foundations of grounding, permissions and evaluation.
Assistants that answer questions from policies, procedures, product documentation and past cases, with citations to the source.
Answers say so when the content does not cover the question.
Fewer questions to experts, faster answers to staff.
Semantic search across SharePoint, file shares, wikis, tickets and applications, combining keyword and vector retrieval.
Permissions are enforced at retrieval, not filtered afterwards.
The document found on the first try.
Summaries, comparisons and extraction from contracts, reports, RFPs and correspondence.
Extracted fields validated before they reach a system of record.
Hours of reading reduced to minutes of checking.
First drafts of proposals, responses, product descriptions and reports in your style and terminology.
A person reviews and approves before anything is sent.
A better first draft, faster.
Assistants embedded in service desks, CRM and HR tools that suggest replies, summarise history and fill forms.
Built into the tool, not beside it.
Shorter handling times without lower quality.
Microsoft 365 Copilot, Oracle and Salesforce AI features configured, grounded and governed for your organisation.
Data access cleaned up before Copilot exposes it.
Value from licences you already pay for.
How a generative AI project runs
Typical generative AI use cases and the content they draw on.
| Pattern | Typical content | Users | Model |
|---|---|---|---|
| Knowledge assistant | Policies, procedures, manuals | Employees | Project or managed |
| Enterprise search | SharePoint, files, tickets, wikis | Employees | Project or managed |
| Document AI | Contracts, invoices, reports | Specialist teams | Project |
| Customer copilot | Product data, case history | Service agents | Project or managed |
| Copilot configuration | Microsoft 365, Oracle, Salesforce | All staff | Project |
Generative AI will always produce a confident answer. In a business, a confident wrong answer about a policy, a price or a contract term costs more than no answer.
So our assistants answer from your content, cite the source, respect permissions and say when they do not know. Accuracy is measured before launch and every month after.
Discuss a generative AI use caseScope depends on the sources and users involved.
Retrieval-augmented generation means the AI first finds relevant passages in your content, then answers from them. It keeps answers current and specific to you, and lets each answer cite its source.
No. Retrieval is permission-aware, so a user only receives answers built from content they are already allowed to open.
For many everyday tasks, yes, and we help configure and govern it. Custom assistants make sense when the content, process or accuracy requirements go beyond what Copilot covers.
That depends on the content and the questions. We measure accuracy on a set of real questions before launch and agree the bar with you.
No. We use enterprise configurations where your content and prompts are not used for training.
Tell us which content your teams search most. We will show what a grounded assistant would look like on it.