Duvi vs Sheet0: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Duvi and Sheet0 — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Duvi
Duvi DigiIQ, Inc.
Build voice and chat support agents by describing them in conversation; one configuration answers on your website, WhatsApp and phone line.
Key features
- Conversational Agent Builder: Creating an agent opens a conversation with a builder that writes the system prompt, picks a model and ingests the websites the agent should answer from, so setup is a dialogue rather than a configuration form.
- Unified Omnichannel Configuration: One agent configuration serves website chat, a WhatsApp number and a phone line, with the same knowledge behind every channel so context is not lost when a customer switches.
- Live Knowledge Lookups: The agent checks your connected store as it answers, so stock and catalogue responses reflect what is actually available at that moment rather than a stale snapshot.
- Website Actions: With the customer's instruction the agent operates the on-page controls you allow, completing the task in front of them instead of handing them a link and instructions.
- Grounded Answering: The agent answers from the pages you point it at and says so when the information is not there, rather than guessing.
- Preview Before Launch: Agents are tested in Preview and only go live once the domain is allowed and a snippet is pasted on your site.
- Broad Connector Library: Sign-in level integrations for Shopify, WooCommerce, Wix, Salesforce Commerce Cloud, Stripe, PayPal, Notion, Airtable, Webflow, Linear, monday.com, Sentry, Supabase, Cloudflare and Zapier.
- Team Workspace: Staff query the day's conversations and orders through their own authorized connection, so the answer reflects the order a customer placed moments ago.
Best for
- Ecommerce Support Deflection: A Shopify store answers stock, shipping and returns questions automatically, with the agent reading live catalogue data instead of a static FAQ.
- Lead Capture With Context: An agent takes a caller's email or number and routes it to the team with the whole conversation attached, so nobody asks the customer to repeat themselves.
- Phone Line Replacement: A small team replaces a recorded phone menu with an agent that answers real questions using the same knowledge base as the website chat.
- WhatsApp Commerce: A brand serving customers primarily on WhatsApp runs the same support agent there without maintaining a separate bot.
- Startup Support Coverage: An early-stage team keeps answering customers around the clock while engineers focus on building the product.
- Enterprise Support Augmentation: An established support operation adds agents to an existing stack via connectors rather than replacing its tooling.
Sheet0
Sheet0
A conversational spreadsheets agent that automates data collection, analysis, formula creation and decision-making via natural-language chat.
Key features
- Conversational Interface: Interact with spreadsheets via natural-language chat to query data, request calculations, and instruct actions without writing formulas or scripts.
- Data Collection Automation: Collects and consolidates inputs into spreadsheets through conversational prompts and connectors, reducing manual import and cleanup work.
- Accurate Formula & Calculation Generation: Generates and inserts spreadsheet formulas and computed columns from user intents, aiming to reduce formula errors and improve correctness.
- Decision Support & Recommendations: Analyzes sheet data to surface insights, recommendations, and suggested next actions to support decision-making directly from the sheet context.
- NPi Action Library Integration: Provides an open-source action library (NPi) and developer APIs that let agents register and call external functions or tools to extend spreadsheet workflows.
- Developer Extensibility: Enables developers to build custom functions, connectors, and tool integrations so agents can perform actions outside the spreadsheet and bring results back into sheets.
- Natural-language spreadsheet queries and commands
- Automated data collection from websites and sources
- Structured spreadsheet generation in real time
- Workflow automation for repetitive spreadsheet tasks
- Focus on accuracy and error reduction
- Conversational spreadsheet interface allowing natural-language commands to read, write, and analyze sheets
- NPi action library: declarative tool/function API to expose actions to agents
- Multi-language support and SDKs (primary languages: Python and Go; Starlark present)
- Open-source distribution under Apache-2.0 with GitHub releases, issues and contribution workflow
- Support for function decorators/@function to register callable tools
- Stateful function-calling and mechanisms to save function-calling state and handle human confirmation
- Extensible tool integrations enabling agents to operate external apps and pipelines
- Release and versioning via GitHub (releases, commits, issue tracking)
Best for
- Natural-language Formula Creation: Ask the agent to compute complex formulas or transformations and have it generate and insert correct spreadsheet formulas automatically.
- Automated Data Intake: Consolidate survey, form, or external data into a single sheet by instructing the agent to fetch, normalize, and append records via conversational commands.
- Report and Dashboard Generation: Convert raw data into summarized reports and visualizations by requesting the agent to aggregate metrics, create pivot tables, and prepare dashboards.
- Repetitive Task Automation: Automate recurring spreadsheet workflows such as reconciliation, cleansing, or template population through agent-run actions and scheduled instructions.
- Augmenting Sheets with External Tools: Use the NPi action library to let agents call external APIs or services (e.g., enrichment, validation) and write results back into the spreadsheet.
- Decision Workflow Enablement: Run scenario analysis and get prescriptive recommendations (hiring, pricing, prioritization) by asking the agent to evaluate sheet data and propose actions.
- Automating web data extraction into spreadsheets
- Business reporting and decision support
- Data analysis and cleaning without coding
- Student and research data collection
- Streamlining repetitive spreadsheet workflows for teams
- Automate spreadsheet edits, calculations, and reporting via natural-language chat
- Build agents that integrate spreadsheets with external services and enterprise workflows
- Automate ETL, data validation, and aggregation tasks inside spreadsheets
- Prototype and deploy custom tool functions for agent-driven automation using Python or Go
- Enable human-in-the-loop workflows where agents request confirmations or save state during operations