Renamed
Obviously AI became Zams in 2025
Obviously AI
Renamed: Obviously AI is now Zams, an AI agents platform for sales and RevOps. The old no-code prediction site is an archive.
Obviously AI became Zams in 2025 and pivoted from no-code predictions to AI agents for sales and RevOps. If you need no-code predictive modelling, look elsewhere; if you want Zams, go to Zams.
Verified 8d ago · liveness 87/100 · cite: rightaichoice.com/tools/obviously-ai
- Business analysts who wanted predictive models from CSV files without writing Python
- Sales and marketing ops forecasting lead conversion, churn, and customer lifetime value
- Teams that wanted a dedicated data scientist bundled with the software
- Researchers and readers studying the 2020–2025 no-code ML landscape
- Anyone buying today — the domain now redirects to Zams and the product is archived
- Teams needing unstructured data support: images, text, audio, or video
- Organizations requiring custom model architectures or deep hyperparameter tuning
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Skip Obviously AI entirely if you are shopping today — the obviously.ai domain redirects to Zams and this site is an archive of how the tool operated through 2025, not a product you can sign up for.
Predictions are counted per row scored, so uploading a CSV with 100 rows consumes 100 of your 1,200 free predictions in one go.
The archived tiers ran from a $0/mo Free plan at 1,200 predictions and 10,000 rows up through Startup, SMB, and Enterprise plans listed as contact sales, scaling to 250 million rows and 10 GB files. It sat between simple spreadsheet tools and full data science platforms like DataRobot, which is priced for teams with dedicated data science capacity rather than analysts working from CSVs.
In short
Obviously AI — Renamed: Obviously AI is now Zams, an AI agents platform for sales and RevOps. The old no-code prediction site is an archive. Best for Business analysts who wanted predictive models from CSV files without writing Python, Sales and marketing ops forecasting lead conversion, churn, and customer lifetime value, Teams that wanted a dedicated data scientist bundled with the software. Free to use.
What people actually say about Obviously AI — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
15 mentions across 5 sources (Hacker News, YouTube, Product Hunt, Stack Overflow, Lemmy), 56 more we could not attribute · researched Oct 8, 2026.
Weighted by the 71 posts each of 5 sources contributed.
- +CSV upload to a working classification or regression model without writing any Python
- +Targeted business analysts, sales ops, and executives rather than data scientists
- +Software plus Data Scientist tier included a Ph.D. support resource via Slack or Teams
- +One-click deployment to web apps or real-time REST APIs simplified shipping predictions
- +Genuinely enthusiastic Product Hunt reception at launch with over 270 upvotes
- −Product has been rebranded to Zams; the obviously.ai site is now an archive
- −A user publicly accused the site of dark-pattern pricing with hidden conditions
- −Tier limits and data caps were unclear and undefined at the point of signup
- −Enterprise-only data residency, SSO, and audit trails locked controls behind the top plan
- −Verification emails landing in spam created a minor onboarding annoyance
- • Tier limits and data caps were reportedly not stated clearly at signup
- • Enterprise-only data residency, SSO, and audit trails push governance into the top plan
- • Human data-scientist support was gated behind higher-priced plans
Viability Score
How well maintained and how widely used is Obviously AI? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- No-code model building from CSV uploads
- Classification models for churn, lead conversion, loan repayment, and fraud
- Regression models for sales, dynamic pricing, yield, deal size, and costs
- Time-series forecasting for values at a specific date
- Clustering models on tabular data
- One-click model deployment to production
- Dynamic web apps for sharing models via a link
- Real-time REST APIs for predictions inside your own apps
- Automatic model monitoring
- RPA automations using Zapier if-this-then-that rules
- Recurring reports and model dashboards
- Dedicated Data Scientist and AI Strategist support via Slack, Microsoft Teams, or video call
- Power BI and Looker visualization of prediction data
- SAML / SSO, logging and audit trails, and data residency on Enterprise
About Obviously AI
Obviously AI was a no-code machine learning platform for structured, tabular data. You uploaded a CSV and got classification, regression, time-series, or clustering models back without writing Python. It targeted business analysts, sales and marketing ops, and executives who needed predictions on churn, lead conversion, loan repayment, and dynamic pricing without waiting on a data science team. The vendor advertised one-click model building, deployment through dynamic web app links or real-time REST APIs, and automatic model monitoring combined with Zapier if-this-then-that automations. A distinguishing part of the offer was human support: the Software + Data Scientist plans matched you with a Ph.D.-level data scientist for data cleaning, merging, enrichment, and model tuning, reachable over Slack, Microsoft Teams, or video call. Data limits scaled by tier, from CSV-only uploads on the free plan up to 250 million rows on the Enterprise plan. The company has rebranded: the obviously.ai domain now redirects to Zams, and this site is preserved as an archive of how the tool operated through 2025. That matters for anyone evaluating it today. If you are shopping for a no-code predictive analytics platform, Obviously AI is no longer a live product to sign up for — the pricing tiers, feature descriptions, and integrations below describe the archived service. The closest live alternatives in the no-code tabular ML space remain DataRobot and H2O.ai, though both lean harder toward teams with some data science background than toward purely non-technical users.
Behind the Verdict
Obviously AI's core proposition was narrow and useful: take a CSV, get a working classification, regression, time-series, or clustering model back without touching Python. The workflow was deliberately shallow — model building in a few clicks, one-click deployment to a shareable web app link, REST APIs for embedding predictions in your own product, and Power BI or Looker for reading the output. For a business analyst forecasting churn or lead conversion off a spreadsheet, that was a real shortcut. The differentiating pitch was human: paid tiers bundled a dedicated Ph.D. data scientist and an AI Strategist who joined your Slack or Microsoft Teams and handled data cleaning, merging, enrichment, and tuning — sold as "an extension of your team" rather than a support queue. On the free tier you got 1,200 predictions, one user seat, unlimited models, up to 10 MB per file and 10,000 rows, and email support. Paid tiers (Startup, SMB, Enterprise) scaled use cases, file size, and row ceilings — up to 250 million rows on Enterprise — and added SAML/SSO, logging and audit trails, and data residency. Weaknesses were structural. The tool was built for tabular data only; images, text, audio, and video were out of scope. There was no path to custom model architectures or deep hyperparameter tuning, which caps how far a data science team can push it. And the free tier's 1,200 predictions, counted per row scored including API calls and CSV batch jobs, runs dry quickly for anything resembling production volume. The larger issue now is that the product no longer exists under this name: the company rebranded to Zams and the obviously.ai site is an archive of how the tool operated through 2025. Our call: if you were evaluating this in 2023, the Software + Data Scientist bundle was a genuinely unusual offer worth shortlisting. Today, Anyone comparing no-code tabular ML should evaluate DataRobot or H2O.ai for the live market, and should treat everything on this page as historical.
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Real-world workflow fit
Concrete scenarios for the personas Obviously AI actually fits — and what changes day-one when you adopt it.
Export a year of closed-won and closed-lost deals to CSV, upload it, and build a classification model that scores lead conversion probability per prospect.
Outcome: A ranked list of prospects by predicted conversion, refreshed on a recurring schedule, plus a Zapier rule that fires a follow-up task when a score crosses 80%.
Feed historical price and demand data into a regression model to forecast the optimal price point for each product SKU.
Outcome: Dynamic pricing recommendations published to a shared web app link, reviewed weekly against actual sales.
Upload applicant records and build a classification model that estimates loan repayment risk before a human underwriter reviews the file.
Outcome: Applicant decisions returned in minutes rather than days, with predictions pushed through the REST API into the existing loan system.
Use Cases
- Sales rep predicts lead conversion probability before reaching out, prioritizing high-fit prospects.
- E-commerce business uses dynamic pricing to adjust product prices based on demand forecasts.
- Micro-lending company predicts loan repayment risk in minutes, cutting loan processing time by 5X.
- Inventory planner forecasts future stock levels to avoid overstocking or stockouts.
- Marketing operations predicts customer churn to target retention campaigns.
- Business analyst builds a time-series model to forecast sales six months out.
Limitations
- Obviously AI was a no-code predictive AI platform for tabular data only: classification, regression, time-series, and clustering.
- Unstructured data — images, text, audio, video — was out of scope, and there was no path to custom model architectures or deep hyperparameter tuning, which caps a data science team's ability to push beyond the default algorithms.
- The vendor site is now an archive: the tool was relaunched as Zams and the site preserves how it operated through 2025, so nothing here is a live product to sign up for.
- Documented integrations are Zapier, Airtable, Dropbox, and Salesforce.
- No underlying model names are stated anywhere in the archived content.
- Free-tier predictions are capped at 1,200 and counted per row scored, so the free plan runs dry quickly outside of evaluation.
as of 2026-09-29
Verification history
We have re-verified Obviously AI 18 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 18 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Obviously AI tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
Individuals and non-profits evaluating no-code predictions on a small tabular dataset, capped at 1,200 predictions and 10,000 rows.
What this tier adds
Free entry point: 1,200 predictions, one user seat, unlimited models, CSV-only uploads up to 10 MB, email support.
Startup Plan
Contact sales
Ideal for
A small team with one prediction use case that wants a dedicated data scientist on hand and 100 MB files.
What this tier adds
Adds a dedicated AI Strategist and Ph.D. Data Scientist, model finetuning and monitoring, and grows file size to 100 MB and data to 1 million rows.
SMB Plan
Contact sales
Ideal for
A scaling business running up to three prediction use cases across sales, pricing, or risk.
What this tier adds
Raises use cases from 1 to 3, files from 100 MB to 1 GB, and training data from 1 million to 100 million rows.
Enterprise Plan
Contact sales
Ideal for
Large organizations that need security controls, audit trails, and data residency alongside the predictive models.
What this tier adds
Adds SAML / SSO, logging and audit trails, and data residency, with up to 5 use cases, 10 GB files, and 250 million rows.
Where the pricing makes sense
The company stage and team size where Obviously AI's pricing actually pencils out — and where peers do it cheaper.
The archived tiers ran from a $0/mo Free plan at 1,200 predictions and 10,000 rows up through Startup, SMB, and Enterprise plans listed as contact sales, scaling to 250 million rows and 10 GB files. It sat between simple spreadsheet tools and full data science platforms like DataRobot, which is priced for teams with dedicated data science capacity rather than analysts working from CSVs.
Setup time & first value
How long it actually takes to get something useful out of Obviously AI — broken out by persona, not the marketing-page minute.
For a business analyst with a clean CSV, first predictions were reachable in an afternoon — the vendor's own framing is minutes per model once the data is loaded. Teams on a Software + Data Scientist plan could hand off data cleaning, merging, and enrichment to the assigned Ph.D. data scientist, which typically added days to the first production model but removed the cleanup work entirely.
Switching to or from Obviously AI
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From spreadsheet heuristics: Export your historical outcome data as CSV and rebuild the scoring rule as a classification model in a few clicks.
- →From an in-house Python model: Export the training set, upload it to Obviously AI, and use the REST API to swap predictions into your existing app.
- →From a BI dashboard: Connect Power BI or Looker to the prediction output to keep your existing reporting layer intact.
- ↗To Zams: Any live evaluation of this vendor now runs through Zams, the successor agents platform for sales and RevOps.
- ↗To DataRobot: Export your training data and rebuild the models in DataRobot if you need deeper tuning and a live no-code ML platform.
- ↗To H2O.ai: Rebuild tabular models in H2O.ai when you want open-source options and some data science capability on the team.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Obviously AI”, and we withheld 6: 6 could not be judged, because “Obviously AI” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Obviously AI.
Official links
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Common stack mates teams adopt alongside Obviously AI, with the specific reason each pairing earns its keep.
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Alternatives to Obviously AI
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boost.space
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