No-code machine learning platform for predictive analytics and data insights
Obviously AI is a no-code machine learning platform that enables users to build predictive models without programming.
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AI Editor ApprovedApproved and published by our AI Editor-in-Chief after full panel analysis.Obviously AI is a no-code machine learning platform that lets users build predictive models without programming. Through a drag-and-drop interface, it automatically handles data preprocessing, model selection, and deployment, with its Edge-Sharp AutoML engine covering classification, regression, time series, and clustering. Other capabilities include what-if scenario simulation, real-time predictions via REST API, one-click model deployment, automated model monitoring, prediction visualization in BI tools, and dedicated data science expert support. There is no free plan, but a free trial is offered; subscriptions start at $300 per month for Limited Access, jumping to $999 for Full Access. TopReviewed's six-seat AI review panel scored it 6.9/10, praising the SMB-priced no-code entry point that undercuts DataRobot's enterprise minimum while noting the steep 3.3x price jump between tiers with no middle option. It suits ops, marketing, and analyst teams that need predictions from business data without hiring data scientists.
Obviously AI is a no-code machine learning platform designed to democratize predictive analytics for business users and data professionals. The software enables users to build, train, and deploy machine learning models without writing code, using an intuitive drag-and-drop interface that automates complex data science processes.
The platform handles various predictive modeling tasks including classification, regression, and time series forecasting. Users can upload datasets in common formats, and the system automatically performs data cleaning, feature engineering, and model selection. Obviously AI provides explanations for model predictions and generates insights that help users understand which factors drive their outcomes.
Targeted at business analysts, marketers, operations teams, and citizen data scientists, the platform aims to bridge the gap between business needs and machine learning capabilities. It competes in the growing no-code AI space alongside tools like DataRobot and H2O.ai, focusing on ease of use and accessibility for non-technical users.
The software includes features for model monitoring, automated retraining, and integration capabilities that allow users to embed predictions into existing business workflows. Obviously AI positions itself as a solution for organizations that want to leverage machine learning but lack extensive data science resources or technical expertise.
Proprietary AutoML engine that automatically evaluates a group of machine learning models, shortlists the top five for a given dataset, tunes their hyperparameters, and returns prediction results.
Enables anyone to build and train machine learning predictive models in minutes by uploading historical data and answering basic questions, without writing any code.
Supports visualization of prediction data in third-party business intelligence tools such as Power BI and Looker for richer reporting and dashboards.
Allows users to build personalized simulations to predict outcomes for different hypothetical situations, enabling scenario-based decision making.
Continuously monitors deployed AI models to track accuracy and performance over time without requiring manual intervention.
Deploys trained AI models to production instantly via web apps and shareable links without requiring any DevOps setup or infrastructure configuration.
Enables users to share interactive prediction reports with teammates or make them public, allowing anyone on the team to run predictions on a deployed model.
A built-in data preparation tool that lets users seamlessly shape and clean datasets — setting filters, applying drag-and-drop columns, and making data ML-ready — without wrangling files manually.
Accepts data via CSV file upload or direct integration with popular data sources, making it easy to import datasets without lengthy preparation or SQL queries.
Provides a low-code REST API so developers can bring dynamic ML predictions — such as lead scoring and willingness-to-pay — directly into their own apps in real time.
Connects to external tools and data sources including Zapier, Airtable, Dropbox, and Salesforce, allowing users to pull data from where it already lives.
Pairs users with a dedicated data science expert who assists with tasks such as data merging, enrichment, cleaning, and industry-specific best practice recommendations.
For students and non-profits exploring no-code AI and predictive modeling
For small teams and businesses needing more predictions and dedicated data science support
For growing teams needing high-volume predictions, more seats, and unlimited training data
For enterprises requiring a full data science team and express support with customizable pricing
Obviously AI sits between Pecan AI's predictive specialism and DataRobot's enterprise stack — at SMB pricing.
“Obviously AI has raised $9.75M across four rounds since 2019, including a $5.05M Series A in October 2023 led by O'Shaughnessy Ventures. The business question is whether the SMB-priced no-code wedge holds while DataRobot, Pecan AI, and Microsoft AutoML push at both ends.”
The buyer is an ops lead or marketing analyst, not a data scientist. Obviously AI sells that buyer Edge-Sharp AutoML, Data Dialog cleanup, and What-If Scenario Simulation at $300/month for 12,000 predictions — well below where DataRobot starts a conversation.
Co-founders Nirman Dave and Tapojit Debnath have been at this since 2019 and pulled $9.75M across four rounds, with the $5.05M Series A from O'Shaughnessy Ventures landing October 2023. Nvidia is on the cap table. That's enough runway to defend the SMB segment for the next 24 months.
But Pecan AI owns the predictive-analytics narrative with banks and retailers, and Microsoft Azure AutoML is free if you're already on the stack. The tradeoff is segment fit versus enterprise gravity. Pilot Obviously AI with a marketing or ops team. Skip it if data already lives in Azure or Snowflake at scale.
DataRobot, Pecan AI, and Microsoft Azure AutoML occupy the larger budget conversations across enterprise and cloud-native segments.
Niche positioning with limited public enterprise logos makes the defensibility story thinner than DataRobot or Pecan AI.
CSV upload plus Edge-Sharp AutoML and One-Click Model Deployment compress time-to-prediction for non-technical users.
Clear no-code wedge for ops and marketing teams that need predictions without standing up a data science function.
Four rounds totaling $9.75M since 2019 with Nvidia and B Capital backing — adequate runway but the $5.05M Series A is modest for the segment.
Ops or marketing teams who need predictions without hiring data scientists.
Enterprises who already run ML pipelines in Azure or Snowflake.
Even the founders bet against no-code AutoML for analysts — that signal travels.
“Obviously AI's Edge-Sharp AutoML and What-If Simulation still serve a clean analyst workflow under $1,000/month. The catch is the founders rebranded the company to Zams in February 2025, and Snowflake Cortex Analyst plus Pecan AI are compressing the lane from both sides.”
The clearest 3-year signal isn't in Obviously AI's docs — it's that Nirman Dave's team rebranded the product to Zams in February 2025 and pivoted to revenue-ops AI workers. For a Head of Analytics sizing a predictive-modeling bet, that's the room reading itself.
The core product is real — Edge-Sharp AutoML auto-tunes five candidate models, Data Dialog handles prep, and REST deployment lands in minutes. Limited Access at $300/month with 12,000 predictions fits a small ops team. But Snowflake Cortex Analyst and ThoughtSpot Spotter now answer most ask-your-data questions natively.
Pecan AI owns the predictive-analytics-for-BI lane with a 2026 generative co-pilot, and DataRobot bundles AutoML inside enterprise contracts starting near $150k. Obviously AI's What-If Simulation and dedicated data scientist are still useful for a one-off churn model — just not a 3-year stack bet.
Squeezed by Snowflake Cortex Analyst and ThoughtSpot Spotter above, and by Pecan AI plus DataRobot in the dedicated predictive lane.
Drag-drop modeling plus REST deployment matches how a business analyst actually ships a churn or lead-score model.
Zapier, Salesforce, Airtable, Power BI, and Looker connectors plus a REST API cover the standard analyst stack.
The founders rebranded the product to Zams in February 2025, the loudest possible signal that the no-code AutoML lane is structurally compressed.
Edge-Sharp AutoML and What-If Simulation are real craft, but the AutoML category is being commoditized by hyperscaler bundles.
Small ops teams who need one or two predictive models without a data scientist.
Enterprises committing to a 3-year predictive analytics platform.
Obviously AI publishes three sticker tiers — $0, $300, $999 — while Pecan and DataRobot demand calls.
“Free, $300/month Limited Access, and $999/month Full Access cover 1.2K, 12K, and 120K predictions respectively, all published without a sales call. Pecan AI starts at $760/month on annual commit and DataRobot ranges into six figures — Obviously AI owns the SMB floor of the no-code ML category.”
Free → $300 → $999. A 3.3x jump between paid tiers, with prediction limits scaling linearly. Edge-Sharp AutoML and What-If Scenario Simulation ship at every paid level. The math is really about prediction volume — 12,000 calls at Limited Access, 120,000 at Full Access.
A 5-seat team on Full Access: $999 x 12 = $11,988/year. Compare to Pecan AI Starter at $760/month annual for 2 prediction batches, or DataRobot enterprise contracts starting north of $150K. Obviously AI sits at the SMB floor of no-code ML.
The catch is the prediction meter — no published overage rate. The dedicated data scientist gates at Limited Access. SSO and custom integrations live in Enterprise with no published sticker. But three visible tiers, a 14-day trial, and a free student path give procurement real numbers without a sales call.
Standard subscription model but SSO and custom integrations gate at the Enterprise tier.
Monthly billing visible and a 14-day free trial on both paid tiers reduce lock risk.
Three paid tiers and a free path are published without a sales call.
Prediction count is countable but model-accuracy ROI stays hand-wavy without case-study numbers.
No published prediction-overage rate and a 3.3x jump from $300 to $999 with no middle tier.
Analysts who need a sub-$1K predictive ML sandbox.
Enterprises who need published volume-overage rates.
Edge-Sharp AutoML and What-If Simulation give analysts fast predictions without the warehouse-connector depth Pecan ships.
“Obviously AI pairs Edge-Sharp AutoML with What-If Scenario Simulation for fast no-code predictive models on CSV data up to 1M rows. The catch is CSV-only ingestion on lower tiers and a single seat on the $300 Limited Access plan.”
CSV-only data input on the Free tier tells the analyst story. No SQL connector, no warehouse pipe — a 1M-row spreadsheet ceiling per dataset. For ops pulling churn data out of HubSpot, that's a daily export ritual, not a workflow.
Edge-Sharp AutoML shortlists five candidate models per dataset instead of running 100 — faster results, less wasted compute, but the analyst sees less of the model bake-off than DataRobot exposes. What-If Scenario Simulation is the practitioner win: hold every column steady, nudge one, watch the prediction shift before deploying.
Limited Access at $300/month caps 12,000 predictions and one seat — fine solo, painful the moment a second analyst joins. REST API deployment hits Zapier and Salesforce without DevOps. However Pecan goes deeper on Snowflake-native pipelines, and Akkio out-prices on small teams.
CSV export rituals from HubSpot or Salesforce add up across a working week.
Docs lean to analyst vocabulary — target column, training rows — not ML jargon.
Single-seat cap on $300 Limited Access and 12,000-prediction ceiling pinch real teams.
Edge-Sharp AutoML abstracts the bake-off, so advanced tuning stays opaque to power users.
Zapier and Salesforce hooks land, but no native Snowflake or Redshift pipe.
Business analysts who need fast predictive models on CSV data.
Teams who need native Snowflake or Redshift connectors.
Obviously AI's REST API ships, but Edge-Sharp AutoML hides the knobs power users want to turn.
“Real-time predictions over REST and Zapier integration cover the deployment story. But the AutoML engine picks five models and tunes them for you, with no exposed hyperparameters or Python SDK in the box.”
Edge-Sharp AutoML is the headline mechanic — upload a CSV, the engine shortlists five candidate models, tunes their hyperparameters, and returns predictions. That's the no-code thesis working as designed. What-If Scenario Simulation lets you poke variables after the fact. The Data Dialog cleans columns so you don't drag a SQL editor around.
The catch is what's not surfaced. There's no public Python SDK — integration goes through the REST API or Zapier. Hyperparameter visibility is whatever the engine decides to show, and SHAP-style feature attribution isn't documented as a power-user surface. DataRobot exposes more dials; H2O AutoML hands you the code.
Three months in, the Dedicated Data Scientist on the $300 Limited Access tier is the actual unlock — they deliver code files, which is the workaround for the missing SDK. Mobile is web-only, fine for a dashboard product but worth flagging.
Drag-and-drop columns and the Data Dialog show the team sweated the data-prep flow.
First hour is fast, but month three hits a wall when you want hyperparameter access.
Platform is web-only; no native mobile experience documented.
CSV upload plus answer a question is genuinely a ten-minute first model.
Edge-Sharp picks models and tunes them, but you can't see what it did underneath.
Business analysts who want predictions without writing code.
Data scientists who need hyperparameter control and a Python SDK.
$1.5M revenue on $9.75M raised, but Cortex Analyst and Pecan's $117M put real pressure on the SMB segment.
“Obviously AI hit $1.5M revenue and 3,000 customers in 2024 on a $9.75M total raise — the SMB analyst segment is real. The squeeze is the category: Snowflake Cortex Analyst shipped public preview August 14, 2024, and Pecan AI sits on $117M with a $66M Insight Partners Series C.”
$1.5M revenue on $9.75M raised. The math is tight. Series A closed October 2023 — no new round since.
Product works. Edge-Sharp AutoML ships classification, regression, and time series from a CSV upload, with Real-Time Predictions via REST API on the $300/month Limited Access tier. What-If Scenario Simulation is genuinely useful for ops teams. But the category got crowded fast: Snowflake Cortex Analyst hit public preview August 14, 2024, bundling text-to-SQL inside existing data-warehouse contracts. Pecan AI raised $117M with a $66M Series C from Insight Partners — three weight classes up the funding ladder.
Honest read: 3,000 customers in 2024 means the SMB analyst segment exists. Exit is decent — models export, predictions over standard REST. Cortex Analyst doesn't answer the viability question kindly. Could go either way past 2027.
Crowded against Snowflake Cortex Analyst, Pecan AI, and hyperscaler AutoML offerings (Vertex, SageMaker, Azure) bundled with cloud contracts.
Models exportable, predictions over standard REST API, CSV-in workflow means migration off is mechanical, not architectural.
No new funding round since October 2023 Series A, $9.75M total raise sits far below Pecan AI's $117M — runway pressure is the watch.
Claims are calibrated to what the product ships — no-code AutoML described accurately, no "AI revolution" superlatives on the pricing page.
No-code AutoML category has a graveyard — DataRobot pivoted to enterprise, H2O.ai shifted focus, and small no-code entrants rarely escape SMB plateau.
SMB analysts who need quick predictive models without a data science team.
Enterprises already standardized on Snowflake or Databricks.
Common questions answered by our AI research team
Free for students and non-profits with 1,200 predictions, $300/month Limited Access (12,000 predictions, 1 user), $999/month Full Access (120,000 predictions, 5 users), with custom Enterprise pricing.
No. The platform uses a drag-and-drop interface that handles preprocessing, model selection, and deployment automatically.
What-If lets users test predictions against hypothetical input combinations to understand which variables drive the outcome before deploying the model.
Yes. Obviously AI exposes real-time predictions over a REST API with one-click deployment, plus Zapier integration for no-code automation.
The Edge-Sharp AutoML engine builds classification, regression, time series, and clustering models from CSV uploads or connected databases.
Company
Obviously AIFounded
2018Pricing
From $300/moFree Trial
Available
Obviously AI is a no-code machine learning platform that lets non-technical users build predictive models from tabular data.
