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The Intelligent Desktop
Application for Knowledge Work

CellarDoor is an AI-native workspace application for designing, building, and editing analytical artifacts, datasets, and deliverables through natural language

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A Centralized Application Purpose-Built for Modern Workflows

CellarDoor is a desktop, IDE-style application designed to automate knowledge work. It provides a single environment to create, inspect, edit, and transform Excel models, CSV datasets, Word documents, PowerPoint decks and PDFs – combining deterministic logic, machine learning models, and natural language in one workflow

The platform integrates directly with existing cloud and local drives, data sources, and systems, allowing teams to add automation and intelligence to the work they already do – without changing how that work is stored or accessed

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Designed & Deployed With

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Use Cases

Use Case 01

Excel Workbooks

Generate, edit, transform, and export new or existing Excel workbooks - through traditional methods or natural language

  1. 1Manual workbook generation, editing & formatting
  2. 2Repetitive actions
  3. 3Prioritized delivery over discovery
  1. 1Natural language-driven deliverables
  2. 2Automated workflows
  3. 3Embedded enterprise & user knowledge
Use Case 02

Document Creation

Seamlessly write, edit, and polish written documents across DOC, DOCX, and TXT file types. Utilize your own writing style and tone via dynamic and personalized embeddings

  1. 1Static text documents
  2. 2Manual written copy creation
  3. 3Human-driven text review & formatting
  1. 1Instant first drafts from a single prompt
  2. 2Personalized writing, from your own natural style
  3. 3Consistent layouts without manual formatting & editing
Use Case 03

CSV Data

Clean, transform, and organize CSV data inside a desktop workflow - powered by natural language and agent commands that execute reliably, every time

  1. 1Repetitive, manual data aggregation
  2. 2Tedious data normalization & review
  3. 3Error prone HITL actions
  1. 1Automated transformations in seconds
  2. 2Production-grade outputs for analysis
  3. 3Faster cycles from data to decisions
Use Case 04

PowerPoint Decks

Create, revise, and standardize PowerPoint decks and presentations, with controlled formatting, reproducible changes, and reliable PDF outputs

  1. 1Manually format slides to match layouts, spacing, fonts, and visual hierarchies
  2. 2Fragmented document stack & tracking
  3. 3Lost individual & institutional knowledge
  1. 1Natural language commands to build elements & slides efficiently
  2. 2Centralized & comparable document stack
  3. 3Persistent & scalable insights
Use Case 05

PDF Generation & Review

Export source documents and data into powerful and vibrant PDFs. Create, share, and edit deliverables within one centralized platform

  1. 1Compare multiple documents across disparate sources
  2. 2Reformatting content after every report
  3. 3Page-by-page manual analysis
  1. 1Query a document as if it's a database
  2. 2Extract data and information with a single command
  3. 3Clean, review, & regenerate deliverables seamlessly
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01

Open

Work across existing folders and files, stored locally or on the cloud
02

Create

Start projects from scratch, building each file individually
03

Transform

Edit, manipulate, and share any new or existing artifacts using a robust machine learning stack

CellarDoor is model-agnostic by design and supports access to multiple providers. These models operate over an individual semantic knowledge layer per workspace, built using industry-specific embeddings and contextual data. This layer captures domain structure, terminology, and prior decisions across files and projects, enabling outputs that remain consistent, context-aware, and aligned with how the user actually works Everything stays fully isolated by default. Files, data, and derived insights are never used to train underlying models, never shared, and never mixed with other customers’ information - ensuring control, privacy, and trust by design

Download
01

Open

Work across existing folders and files, stored locally or on the cloud
01 Open
02

Create

Start projects from scratch, building each file individually
02 Create
03

Transform

Edit, manipulate, and share any new or existing artifacts using a robust machine learning stack
03 Transform
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An ML-forward desktop architecture

Orchestrate

Transform raw user instructions into fully enriched, validated, and structured requests ready for intent classification and pipeline execution.

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