Advisory & Consulting

Your delivery margin is leaking through work nobody wants to do manually.

Advisory teams are under pressure to deliver faster while clients expect sharper evidence, lower fees, and more defensible outputs. Dotnitron builds workflows around workpapers, evidence, research, compliance mapping, and report preparation so practitioners spend more time reviewing and advising.

Start with the work that needs to improve. We learn where your team is losing time, margin, confidence, or speed, then build an AI product or system around the way your business actually operates.

Where AI can help

The work slowing your team down.

The professional services model is being squeezed from both sides: routine research, summarization, and slide preparation are becoming easier to automate, while clients still pay for judgment, methodology, and accountable delivery.
01

Workpapers start from blank pages too often

What you seeTeams collect screenshots, policy excerpts, procedure notes, evidence files, and client explanations, then manually turn them into first-draft workpapers.

What it costsSenior reviewers spend expensive time checking whether the preparer found the right source rather than testing the conclusion.

What could changeGenerate source-backed workpaper drafts, evidence notes, exception flags, and reviewer queues that match the firm's methodology.

02

Client files arrive messy and inconsistent

What you seeEvery engagement has different folder structures, naming conventions, templates, screenshots, and missing context.

What it costsThe team loses margin in intake, sorting, renaming, cross-referencing, and chasing clarification.

What could changeBuild an intake layer that classifies artifacts, detects missing evidence, maps files to requirements, and preserves source traceability.

03

Reusable knowledge stays trapped in engagement teams

What you seePrior issue patterns, clause interpretations, control gaps, and client-specific learnings rarely become reusable assets.

What it costsNew teams repeat the same research and quality checks across similar engagements.

What could changeCreate controlled knowledge workflows that reuse approved methodology, examples, and playbooks without leaking client data across engagements.

What we can build

Turn the problem into a system your team can use.

Manual preparation is the margin problem

The most automatable advisory work is often the preparation layer: evidence intake, first-pass mapping, source extraction, issue list preparation, benchmark assembly, and template population.

Source-grounded analysis across client material

We build retrieval and review workflows that ingest engagement files, evidence, reports, policies, and data exports. Every claim remains linked to source material so reviewers can approve, edit, or reject it.

Client-ready outputs without copy-paste loops

We connect extraction and review to the formats delivery teams use: workpapers, Excel trackers, PowerPoint exhibits, memo sections, dashboards, and reviewer notes.

Multi-client isolation by design

Advisory firms require hard separation between engagements. We design around row-level security, namespace isolation, user permissions, audit logs, and approved reuse boundaries.

FAQ

Frequently asked questions

How do you handle multi-client confidentiality?

We enforce strict row-level security and tenant isolation to ensure data from one client engagement is never accessible to another.

OpenAI Select Partner

Technology partnership

Build with an OpenAI Select Partner.

Recognized by OpenAI for helping organizations build, deploy, and scale AI solutions.
See how we approach security

Have something in mind?

Is this slowing your team down?

Tell us how the work happens today, where it breaks down, and what you want to improve. We will help you decide whether a custom AI system is the right answer.