LLM.co · Private AI & Custom AI DevelopmentPrivate AI for professional services

AI for professional services firms that keeps client work apart

Accounting, consulting, engineering and advisory firms sell expertise, and most of that expertise sits in past deliverables, workpapers, proposals, models, reports and email. Staff rebuild work that already exists because they cannot find it, or because they are not sure they are allowed to reuse it. Useful AI for professional services firms solves both problems at once.

Client confidentiality makes this harder than it looks. Engagement letters and NDAs restrict who may see client material. Audit firms have independence rules. Engineering firms hold clients' drawings and site data. A general chatbot that mixes every document into one index will eventually surface one client's material in another client's answer. LLM.co builds private AI that runs on your infrastructure and enforces engagement-level permissions on every answer.

Knowledge management that people actually use

Most firm knowledge management projects stall because tagging past work is slow and nobody does it. A language model can read the document and fill in metadata such as client, industry, service line, engagement type, methods used and key findings. Staff then search in plain English and filter by what matters. This search layer is the core of most AI for professional services firms, and other tools build on it.

Each answer cites the document and page it came from. Anything a user cannot open in SharePoint, Google Drive or the document management system stays out of their results. Approved, sanitized examples can be marked for firm-wide reuse while the underlying client files stay restricted.

Workflows to automate first

These are the common starting points for custom AI development in a firm.

• Proposal drafting. First drafts and qualifications sections assembled from your approved case library, team bios and service descriptions.
• RFP response. Questions extracted from the RFP and matched to prior approved answers, with gaps flagged for the pursuit team.
• Engagement research. Briefs that combine internal prior work with approved external sources, each claim cited.
• Workpaper review. Checks for missing sign-offs, tickmarks, cross-references and inconsistent figures before a reviewer opens the file.
• Deliverable QA. Reports checked against the firm's style guide, templates and required disclosures.
• Expert finder. Staff matched to questions based on the work they have actually done.

Confidentiality, independence and audit trails

A private LLM hosted on your servers or in your own cloud account keeps client material inside the firm. Users sign in through your identity provider, and permissions come from your existing systems, including ethical walls and restricted engagements. Every question, retrieved document and answer is logged under your retention policy.

For audit and assurance practices, we can separate indexes by client and keep restricted-entity lists in the access rules. The system is built to support your quality management and independence policies. Your risk team decides how it fits them.

What to pilot first and what to avoid

Proposal drafting for one practice is a good first pilot. The source material is already approved for reuse, the output is reviewed by partners before it goes out, and the time savings are easy to see. Knowledge search over closed engagements in one service line is another sound start.

Avoid loading every file share at once before permissions are mapped. Avoid letting the system send anything to a client without review. Avoid tools that train a vendor's shared model on your documents.

How LLM.co delivers

Work starts with a two-week discovery sprint to pick the workflow, map permissions and size infrastructure. A focused first system usually reaches production in eight to twelve weeks. On-premises AI or a dedicated cloud environment are both options. You own the code, prompts, evaluation sets and any fine-tuned weights, with no per-seat license. Your IT team can run the system, or we can operate it under a support agreement with quarterly evaluation reports.