I am imagining a recruiting workspace with candidates, interview notes, scorecards, scheduling status, reminders, and a hiring dashboard. This seems like a natural Teable use case.
@krystal_wuu Krystal, exactly. Teable can keep the entire hiring process connected, from candidate intake and interviews to reminders and reporting. Thanks
@charlenechen_123Â Charlene, exactly. The familiar spreadsheet experience helps teams move their data over and start working without a steep learning curve.
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@Jocky that's a nice loop, going from raw data straight to a working automation without switching tools. does the AI ever get the logic wrong on the first try for more conditional workflows, or is it reliably right once the relationships are set up correctly?
@omri_ben_shoham1 Great question. For complex conditional workflows, the AI may not always get every detail right on the first attempt—but it doesn’t simply stop and leave the failure to you. It can inspect the execution logs, identify what went wrong, and actively fix the workflow until it reaches the intended result.
And this capability keeps getting stronger as the underlying models improve. Teable continuously brings the latest, most capable models into the product as quickly as possible, while keeping pricing highly competitive.
So the real advantage isn’t pretending AI never makes mistakes. It’s giving you an agent that can understand relational context, recover from problems, and rapidly improve alongside the best models available.
@guangyu_li “From Excels to AI operations” is a strong pitch. A lot of companies are quietly running critical processes from folders full of files, so this problem is very real.
@guangyu_li @zhangxing Exactly. The spreadsheet is rarely the whole problem—the real system is often scattered across Excel files, folders, emails, scripts, and knowledge held by one operations hero.
Teable brings those facts into one relational foundation, then lets teams build AI workflows and custom apps directly on top. The goal is not simply moving Excel online, but turning fragmented business data into an operating system the whole team can trust and evolve.
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congrats on the launch!
I found Teable is constantly evolving, could u please share some thoughts on who r your ICP and what are the critical moments that shaped this product to its 3.0 version. @bieber_chen@guangyu_li
@guangyu_li @new_user___152202643cbe5e07d1e4dea Thank you! Our core users are teams whose important operations have outgrown spreadsheets and disconnected SaaS tools, but whose workflows are too specific for standard software. Operations, marketing, sales, project delivery, and other data-heavy teams often feel this pain first. Teable is especially valuable when they need both the flexibility to build around their business and the governance to make that system trustworthy.
The most important lesson from the past three years—and thousands of customers—was that teams didn’t only need a better database-spreadsheet. Their data was just the foundation. They also needed apps, automations, permissions, and collaboration built around it.
AI created the next critical moment. We saw that generating another isolated app was not enough; agents needed to operate where the real data and workflows already lived. That shaped Teable 3.0 into a complete agent environment where connected data, custom apps, automations, permissions, and collaborators work together on PostgreSQL.
Our belief has remained consistent throughout: software should adapt to the work, not force teams to adapt their work to the software. Teable 3.0 is the fullest expression of that idea so far.
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the Postgres-as-backend part is what makes this interesting to me over the usual Airtable clones, since your data isn't stuck in some proprietary format if you ever want to leave or query it directly. the migration piece is the thing I'd want to test hardest though, especially anything with deeply nested lookups or rollups built on filtered views. that structural logic is usually where "we support Airtable migration" tools quietly fall apart even when the raw values move over fine. did you build this off real customer migrations or mostly synthetic test bases while developing it?
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@Bieber that self-correction loop is the part I'd actually want to see - most tools just surface the error and stop. does the agent ever get stuck in a retry loop trying to fix something that's actually a bad assumption in the workflow design itself, or is there a cap on how many times it'll try before it hands it back to you?
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My company has about 10 different metric reports (spreadsheets) that monitor/track placements and things for different campaigns. It also monitors our social metrics from Planable. We also have Google analytic reports for our website metrics. My dream is to be able to have something where we could enter in our data and then the app analyzes our metrics, the days & times we had placements and published things, and then figure out the true impact (in terms of numbers and value) of our campaign placements, social posts, and web placements. Does that make sense? Can this app do that? 🤔
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Typeless
I am imagining a recruiting workspace with candidates, interview notes, scorecards, scheduling status, reminders, and a hiring dashboard. This seems like a natural Teable use case.
Teable
@krystal_wuu Krystal, exactly. Teable can keep the entire hiring process connected, from candidate intake and interviews to reminders and reporting. Thanks
PopPop AI Vocal Remover
Familiar rows and columns make migration less intimidating.
Teable
@charlenechen_123Â Charlene, exactly. The familiar spreadsheet experience helps teams move their data over and start working without a steep learning curve.
@Jocky that's a nice loop, going from raw data straight to a working automation without switching tools. does the AI ever get the logic wrong on the first try for more conditional workflows, or is it reliably right once the relationships are set up correctly?
Teable
@omri_ben_shoham1 Great question. For complex conditional workflows, the AI may not always get every detail right on the first attempt—but it doesn’t simply stop and leave the failure to you. It can inspect the execution logs, identify what went wrong, and actively fix the workflow until it reaches the intended result.
And this capability keeps getting stronger as the underlying models improve. Teable continuously brings the latest, most capable models into the product as quickly as possible, while keeping pricing highly competitive.
So the real advantage isn’t pretending AI never makes mistakes. It’s giving you an agent that can understand relational context, recover from problems, and rapidly improve alongside the best models available.
AITalk
@guangyu_li “From Excels to AI operations” is a strong pitch. A lot of companies are quietly running critical processes from folders full of files, so this problem is very real.
Teable
@guangyu_li @zhangxing Exactly. The spreadsheet is rarely the whole problem—the real system is often scattered across Excel files, folders, emails, scripts, and knowledge held by one operations hero.
Teable brings those facts into one relational foundation, then lets teams build AI workflows and custom apps directly on top. The goal is not simply moving Excel online, but turning fragmented business data into an operating system the whole team can trust and evolve.
congrats on the launch!
I found Teable is constantly evolving, could u please share some thoughts on who r your ICP and what are the critical moments that shaped this product to its 3.0 version. @bieber_chen @guangyu_li
Teable
@guangyu_li @new_user___152202643cbe5e07d1e4deaÂ
Thank you! Our core users are teams whose important operations have outgrown spreadsheets and disconnected SaaS tools, but whose workflows are too specific for standard software. Operations, marketing, sales, project delivery, and other data-heavy teams often feel this pain first. Teable is especially valuable when they need both the flexibility to build around their business and the governance to make that system trustworthy.
The most important lesson from the past three years—and thousands of customers—was that teams didn’t only need a better database-spreadsheet. Their data was just the foundation. They also needed apps, automations, permissions, and collaboration built around it.
AI created the next critical moment. We saw that generating another isolated app was not enough; agents needed to operate where the real data and workflows already lived. That shaped Teable 3.0 into a complete agent environment where connected data, custom apps, automations, permissions, and collaborators work together on PostgreSQL.
Our belief has remained consistent throughout: software should adapt to the work, not force teams to adapt their work to the software. Teable 3.0 is the fullest expression of that idea so far.
the Postgres-as-backend part is what makes this interesting to me over the usual Airtable clones, since your data isn't stuck in some proprietary format if you ever want to leave or query it directly. the migration piece is the thing I'd want to test hardest though, especially anything with deeply nested lookups or rollups built on filtered views. that structural logic is usually where "we support Airtable migration" tools quietly fall apart even when the raw values move over fine. did you build this off real customer migrations or mostly synthetic test bases while developing it?
@Bieber that self-correction loop is the part I'd actually want to see - most tools just surface the error and stop. does the agent ever get stuck in a retry loop trying to fix something that's actually a bad assumption in the workflow design itself, or is there a cap on how many times it'll try before it hands it back to you?
My company has about 10 different metric reports (spreadsheets) that monitor/track placements and things for different campaigns. It also monitors our social metrics from Planable. We also have Google analytic reports for our website metrics. My dream is to be able to have something where we could enter in our data and then the app analyzes our metrics, the days & times we had placements and published things, and then figure out the true impact (in terms of numbers and value) of our campaign placements, social posts, and web placements. Does that make sense? Can this app do that? 🤔