Onelook or Continual? A Comprehensive Review

Builds applications through conversation

Generates back-office and non-admin views

In-app review and refining

Built-in analytics

Visualizes application-specific metrics

Automatic user guide generation

Clarifies ambiguities in instructions

Guided application development

Feature explanation in plain English

Simplifies complex application building

Cloud-based predictive modeling

Uses SQL for app creation

Works with BigQuery

Snowflake

Redshift

and Databricks

No need for complex infrastructure

Models improve continually

Data and models stored on warehouse

Easily accessible to operational and BI tools

Depends on user language proficiency

High ambiguity resolution instances

Limited manual code customization

None specific data visualization tools

Automatic user guide limitations

Assuming correct user instructions

Reliant on internet connection

Possible data privacy concerns

SQL-centric

Limited to cloud data platforms

Dependency on modern data stacks

No MLOPS infrastructure

Limited extensibility (Python only)

Dependent on dbt compatibility

Not suitable for traditional data management systems

Data must be on the same warehouse

No mention of multilingual support

Dependent on continuous access to data warehouse