AI Platform That Redefines How Consultants Work

Powering a new-generation AI workspace that liberates consultants from routine work

Project Highlights

Full product redesign shipped across every major section of the platform

Modules running in production for 6+ months with zero major incidents

New integrations added in under 2 weeks thanks to modular architecture

DocAssist shipped for both Google Workspace and Microsoft 365 from a shared core

$2M seed round closed during the engagement

Client: Moonnox (Chicago, USA)

Industry: AI / B2B SaaS

Engagement: Ongoing

Uinno Team: Engineering Manager, Project Manager, Frontend Developer, Backend Developer, AI Engineer, Product Designer, QA team

See it live: Moonnox

3 levels of AI agents

L1: System Agents

Pre-configured by Moonnox and locked at the platform level. Even admins cannot change them. These handle the most critical workflows, like the "golden" call summary agent that produces consistent meeting summaries across every organization.

‍

L2: Template Agents

Users choose from a library of ready-made agents such as Kickoff, Retrospective, Implementation, and Drafter. Each comes with a working prompt that users can customize to fit their team's language and process.

‍

L3: Custom Agents

Built from scratch. The user starts with a blank prompt, picks the model, sets advanced parameters, and connects the agent to specific project contexts. Full control for teams that know what they want the AI to do.

‍

TECH DEEP DIVE

Functional Design

The product had grown fast, but the initial UI wasn't keeping up.

Solution

We redesigned every major surface of the platform piece by piece, shipping new interfaces alongside feature work so customers never experienced downtime or a jarring overnight change.

Structuring Startup Chaos

Like many startups shaping a new category, requirements shifted constantly as the founders learned what drives value. Ideas came fast, but they needed structure before they could become working features.

Solution

Uinno turned founders' ideas into actionable development requirements with clear scope, priorities, and timelines, keeping the team productive through constant change instead of waiting for specs that would never be final.

Collaboration with the In-House Team

As the product grew, two teams working on the same codebase needed shared standards to avoid breaking each other's work.

Solution

We aligned development workflows with the Moonnox team by establishing shared quality benchmarks and a synchronized PR review process, keeping the system robust and scalable as the codebase expanded.

What Uinno Built

Module 1. The Event-Driven Suggestion Feature

When a client meeting wraps, Moonnox processes the call against the project context stored in its database. We built the "Suggestion Service" as an abstract, event-driven "black box".

The Suggestion Feature needed to process events from multiple systems, understand what happened in a meeting, and reliably generate the right action suggestions for the right context, every time, at scale.

It evaluates the context and proposes next steps like creating a Jira ticket, drafting an email follow-up with attachments, or posting to Slack. Because we built integrations as self-contained modules, the system is infinitely scalable. That is why adding a new action type takes under two weeks without touching core code.

Module 2. Co-Pilot and Live Coaching

Call Pilot module joins client calls and works as an active participant, not just a recorder. Beyond providing post-call transcripts and sentiment analysis, it offers Live Coaching during the meeting itself.

If a consultant is pulled into a large call mid-way or loses track during a complex discussion, the Call Pilot provides real-time mini-summaries of what has been said in the last few minutes, directly on screen.

The AI processes the conversation as it happens, pulling in relevant project context from the knowledge base so the summaries are not just a recap of words spoken but a contextual briefing tied to the client, the engagement, and the decisions that matter.

Module 3. DocAssist for Google and Microsoft

Consultants spend their days on documents. We built an add-on that brings Moonnox agents directly into Google Docs, Sheets, Slides, and the Microsoft 365 equivalents (both web and desktop).

By building a shared core of business logic with two lightweight adapters, we shipped the Microsoft version in a fraction of the time it took to build the Google version, and future updates push to both platforms simultaneously.

Module 4. Project Management

The project management layer allows users to run their engagements without paying for separate tools like Jira. But unlike a standard task board, this module is connected to the same AI context layer that powers everything else in the platform.

A Project Management head can open a dashboard and see health scores across all active engagements, generated through sentiment analysis of what is happening in calls, tickets, and team communication.

Module 5. Project Health Scoring

Uinno helped develop a Project Management Agent that runs sentiment analysis across project activity on a configurable schedule.

It produces a health score for each project so users can see which projects need attention without reading every update themselves.

FAQ

What is Moonnox and how does it help consulting firms?



Moonnox is an AI-powered operating system built for consulting firms, systems integrators, and Salesforce partners. It connects to the tools services teams already rely on, including Salesforce, Jira, Google Drive, Confluence, and live client calls, and brings everything into a shared project context that custom AI agents can work from. Those agents handle the routine parts of an engagement, like drafting follow-ups, generating meeting summaries, creating Jira tickets, and producing client documents, so consultants spend their time on the work that clients actually pay them for.

How do you build AI agent features that can grow with the product?



The key is designing each AI feature as an event-driven service with clear, reusable interfaces rather than hard-wiring it to one tool. When Uinno built Moonnox’s Suggestion Service, we made every integration a self-contained module, so new action types can be added in under two weeks without touching the rest of the system. That flexibility matters because Gartner expects 40% of enterprise applications to include AI agents by the end of 2026, and the platforms that can add new integrations quickly will be the ones that win.

What does it take to build an AI product that works inside both Google Workspace and Microsoft 365?



Shipping the same experience across both ecosystems without doubling the development effort means sharing the core business logic and keeping the platform-specific code as thin as possible. Uinno built Moonnox’s DocAssist add-on on exactly that pattern, which is why it runs inside Google Docs, Sheets, Slides, Word, Excel, and PowerPoint on both web and desktop. Once the Google version was stable, the Microsoft version shipped in a fraction of the usual time, and every future improvement updates both platforms at once.

What does a real product development partnership look like for an AI startup?



For a platform like Moonnox, it covers design, engineering, AI, and project management, not just task execution. Uinno works as a dedicated embedded team. The founders fly to Kraków for in-person planning weeks, and the roadmap is shaped together. That is how you build a product, not just features.

Why is now the right time to build an AI platform for professional services?



Gartner predicts 33% of enterprise software will include agentic AI by 2028, up from less than 1% in 2024. The AI agent market is expected to grow from $7.84 billion to over $50 billion by 2030. Consulting firms, whose business runs on project context and knowledge that usually lives in individual heads, are one of the industries that will benefit the most.