Applied AI consulting for US businesses

Internal AI Knowledge Assistants

Help staff find and use the information they are allowed to access, with source-linked answers and clear limits.

For teams switching between documents, wikis, and business tools to answer recurring questions or prepare first drafts.

Discuss your workflow

Investment, timing, and scope

Published investment (USD): $35,000 – $50,000
Planning duration: 6–8 weeks

An evaluated internal assistant pilot, with agreed source access, integration requirements, and a handoff plan.

The proposal defines the phases and deliverables you are buying. An assessment produces a decision and pilot plan; it does not include a build. For a pilot engagement, the proposal specifies whether work ends at evaluation or includes deployment and integration into an agreed environment. Wider rollout follows acceptance; a working prototype alone does not establish production readiness. Hosting, model usage, third-party licenses, and ongoing support are itemized in the scope. Published fees and durations are planning ranges, subject to data readiness, access, and integration complexity.

Is this the right starting point?

Should we use an existing tool or build a custom assistant?

Start by checking whether your existing search or licensed AI tools can answer the required questions using approved sources and access rules. Private company documents alone do not require a custom build. Custom work becomes a candidate when a tested gap remains in retrieval, permissions, integration, or the staff workflow.

Work through the build-versus-buy decision →

What does retrieval-augmented generation mean for staff?

Retrieval-augmented generation, or RAG, finds relevant source passages before drafting an answer. For an internal knowledge assistant, the practical questions are whether the sources are current, whether the employee may see them, and whether each answer is supported. A citation is something to check, not proof that an answer is correct.

What needs to be ready before a pilot?

Choose a document owner, an approved source set, representative staff questions, and examples of answers that should be withheld or escalated. Outdated or conflicting policies may need repair first. Agree on content updates and access tests before expanding to more teams.

Use the build-versus-buy decision worksheet →

What the engagement produces

Knowledge and access assessment

Identify the source material, ownership, update frequency, and access rules. Define which questions the assistant should answer and which it should decline or escalate.

Assistant prototype and evaluation

Build a retrieval-based prototype on approved documents. Test source references, incomplete information, conflicting documents, and attempts to retrieve material outside a user’s permissions.

A practical integration plan

Scope a connection to Slack, Teams, an intranet, or another existing interface where its APIs and access rules permit. Include feedback handling, content updates, and operating documentation.

How we work with your team

Subject-matter owners provide representative questions and approved answers. Technical owners identify identity and access constraints.
We test retrieval and answer quality before expanding the source set. Your reviewers assess whether citations support the answer and whether escalation is useful.
We agree on deployment, monitoring, user guidance, and ongoing content ownership before wider use.

Scope and responsibilities

An assistant can make mistakes. It is not a substitute for professional judgment, and access to a chat interface must not grant access to otherwise restricted records. Model providers, retention, hosting, and connector permissions require explicit scope decisions. A prototype is not proof of production readiness.

Customer support automation

Support teams can apply the same approach to ticket categorization, knowledge retrieval, and draft replies. Start with suggestions reviewed by staff, a maintained knowledge base, and a clear path to a human. Any customer-facing answer or automated send needs separate evaluation and an agreed release boundary. We measure answer usefulness and escalation quality rather than assuming that fewer tickets means better support.

Illustrative use case: an internal policy assistant

A staff member asks which purchasing approval is needed. The assistant locates the relevant policy, links to its source, and directs ambiguous cases to the policy owner. This is an example of a possible workflow, not a client result.

Inspect the fictional assistant evaluation worksheet →

Prepare for the conversation

Bring the process you want to improve, the tools involved, and examples you are allowed to share. Leave confidential records out of the initial inquiry.

Compare building and buying an AI tool →

Working together across the US

Based in Detroit, we work with businesses across the United States. Before work begins, we agree on working sessions, access to approved examples, review milestones, and the people responsible for decisions. Delivery format, any on-site work, and support hours are specified in the proposal.

Plan a pilot with clear acceptance criteria →

Start with one useful problem

Tell us what your team needs to do, where the current process breaks down, and who will own the result.