AI Project Manager
We relaunched the app and created a web version so users can work with AI assistant

TechnologiesReact

IndustriesAWS

ServicesSaaS

Context

We initially built AI Project Manager for our own product and delivery teams managing multiple projects in parallel.

Despite using modern tooling, critical decisions were scattered across Slack threads, project documentation drifted from reality, and meeting outcomes frequently depended on someone remembering to update trackers manually.

The goal wasn't to create another project management tool. It was to eliminate the manual coordination work that sits between conversations, documentation, planning, and execution.

Results Based on internal use and early testing:	Before	After
Documentation & status updates	~40 min	10 min
Meeting follow-up	~5 hrs/week	2 hrs/week
Action item tracking	~20% missed	0% missed
New hire ramp-up	~5-7 Days	2-3 Days

Challenges

Solutions

Critical decisions disappeared inside conversations
Important project decisions were made in Slack threads  and meetings but rarely documented consistently.
Searchable project knowledge layer
A RAG-powered knowledge layer indexes conversations, documentation, and meetings, making verified project context instantly searchable.
Documentation became outdated almost immediately
Project briefs and planning documents were usually accurate at kickoff but drifted from reality as requirements evolved.
Continuously updated project documentation
The assistant automatically maintains project briefs, decisions, risks, owners, milestones, and next steps as work evolves.
Meeting follow-up created operational overhead
After every meeting, someone needed to write notes, extract action items, update project trackers, and notify stakeholders
Automated meeting summaries and task sync
Meeting discussions are converted into summaries, action items, and synchronized tasks without manual follow-up.
Risks surfaced too late
Project blockers, unresolved questions, and delivery risks often became visible only after impacting timelines.
Proactive delivery risk monitoring
Meeting discussions are converted into summaries, action items, and synchronized tasks without manual follow-up.

Technical Architecture

The manager is built on a modular AI architecture that transforms domain-specific data into intelligent workflows through four core layers: data ingestion, context retrieval, AI reasoning, and enterprise integrations.  Each layer is designed for production environments with security, explainability, and scalability built in from the start.

What We Learned Building This

Conversations are the real source of project knowledge.
AI delivers the most value when it understands discussions, meetings, and decisions—not just project documentation.
Automation should fit existing workflows.
Adoption is highest when AI works inside Slack and existing tools instead of introducing new processes.
Trust depends on traceability.
Every summary, action item, and recommendation should be backed by verifiable project context.
Context beats bigger models.
Accurate retrieval of project history has a greater impact on output quality than model size alone.
The biggest wins come from invisible work.
Automating documentation, meeting follow-ups, and task coordination delivers the greatest productivity gains.

Our Cases

Innovation isn’t just about ideas - it’s about execution, turning vision into reality, and creating solutions that truly make an impact. See what we’ve built and how it works: