About Zams

Zams (formerly Obviously.ai) provides AI workers that automate business operations end to end. Instead of the original no-code prediction-model builder, the current product deploys task-specific AI agents that handle workflows such as lead qualification, data entry, document processing and revenue operations, working inside the tools a team already uses. Business users describe the job to be done, connect their data sources and systems, and the AI worker executes the workflow continuously with human review where needed. The platform retains its no-code heritage: teams configure and supervise AI workers without engineering support, and the underlying machine learning that powered Obviously.ai's predictions now drives the decision-making inside each worker.

Use Cases

Revenue teams qualify and route leads automatically with Zams AI workersoperations teams automate document processing and data entrybusiness analysts deploy predictive automation without engineersformer Obviously.ai users migrate prediction workflows into AI workersmanagers supervise AI workers with human-in-the-loop review.

Key Features

✓ Task-specific AI workers for business operations
✓ No-code setup: describe the job, connect systems, supervise output
✓ Lead qualification and revenue operations automation
✓ Document processing and data entry automation
✓ Human-in-the-loop review for sensitive steps
✓ Integrations with common CRM and data tools
✓ Built on the predictive ML foundation of Obviously.ai

Pricing

Custom

Contact sales

• AI workers scoped to your workflows
• Onboarding and integration support
• Usage-based scaling

Pricing varies by plan and region — see current pricing.

Details

Categories: Agents & Automation

Skill Level: beginner

Access Methods: browser

Zams Community Discussions

Explore community discussions. Ask and answer questions on Zams to grow and learn together.

finnbogi_dgt · Jun 27, 2026 Zams Agents & Automation

Five AI tools for data analysis tested seriously and Obviously.ai makes the top five for no-code prediction specifically

The 53-tool test roundup with five final selections is the kind of systematic comparison that produces more useful guidance than individual tool reviews. Claude AI for large datasets and Python/SQL coding with its long context window, Julius AI for exploratory data analysis sidekick work, ChatGPT Advanced Data Analysis for visualisation and pattern recognition, Rows AI for a spreadsheet-native approach and Obviously.ai for no-code machine learning prediction are the five that survived the comparison. Obviously.ai's specific reason for making the cut is prediction without code: training and running machine learning models on business data without requiring any data science background. That is the gap in the other four tools which all require at least some technical knowledge to extract their full value. The test methodology covering real data tasks rather than capability demonstrations is what makes the five-tool selection credible. A tool that performs well on synthetic demonstration data but fails… Read full discussion →

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arnora_builds · Jun 20, 2026 Zams Agents & Automation

Nine AI tools every data analyst should know in 2026 and Obviously.ai makes the list for a specific reason

The data analyst toolkit video covers nine tools and the interesting exercise is not just noting which tools made the cut but understanding why each one appears. Obviously.ai earns its place specifically for no-code predictive modelling on structured business data. The category it sits in, tools that bridge the gap between data exploration and machine learning without requiring Python or R, is the category that is growing fastest in real enterprise data teams where most analysts are not data scientists. Julius AI as an analyst sidekick for exploratory data analysis on uploaded datasets, Quadratic AI for Python code generation inside a spreadsheet and Bricks for data visualisation to chart building are the companion tools in the list that round out the workflow. None of them does what Obviously.ai does for prediction specifically. The interesting competitive question the list raises: as general-purpose tools like Claude and ChatGPT get better at code-assisted… Read full discussion →

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ylfa_builds · Jun 16, 2026 Zams Agents & Automation

Hex AI combining live code notebooks, drag-and-drop dashboards and AI query assistance is the analytics platform comparison worth making

The Hex AI review is positioned as a comparison point for analytics platforms and understanding how it compares to Obviously.ai changes how you evaluate both. Hex's core functionality combining code notebooks with dashboard building for Python and SQL users is the technical analyst tool. The AI assistant handling query writing, visual generation and exploratory suggestions sits on top of that technical foundation. Obviously.ai's no-code predictive modelling sits in a different category: accessible to business analysts without Python or SQL backgrounds rather than to technical data scientists who already know how to write the queries. The two tools are not competing for the same user. The review being honest about Hex being primarily for Python and SQL users rather than for non-technical analysts is the audience specification that makes the comparison useful. If your team has technical analysts, Hex is relevant. If you need non-technical business users to run their own… Read full discussion →

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PredictiveWithoutCode_Mira · Apr 10, 2026 Zams Agents & Automation

Obviously.ai built a churn prediction model from my CSV in about four minutes

I have wanted to build predictive models for our customer data for a long time. We have the data, we have a clear question we want answered, which customers are most likely to churn in the next 90 days, but we do not have a data scientist on the team and the quotes we got to have one build something custom were not realistic for where we are as a company. Obviously.ai is a no-code machine learning platform and I want to be specific about what no-code actually means here because it is often oversold. You upload a CSV or connect a database. You select the column you want to predict, in our case a churn indicator. The platform builds the prediction model automatically, shows you the accuracy metrics so you know whether to trust it, and tells you exactly which factors are driving the predicted outcomes. That driver analysis… Read full discussion →

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SalesOps_Mateo · Apr 8, 2026 Zams Agents & Automation

Obviously.ai integrates with Salesforce and runs lead scoring predictions directly on our CRM data

Lead scoring is one of those things that sales teams talk about wanting and rarely have working properly. The standard approach is a manually maintained point system that someone set up years ago and nobody has updated since because updating it requires data science resources that are never available. Obviously.ai's Salesforce integration is what made it relevant for our specific situation. You connect it directly to your CRM, select the field you want to predict, closed-won probability or high-value lead likelihood or whatever makes sense for your data, and it builds and trains the model on your actual historical records rather than requiring an export step. The Automated Model Selection means it tests multiple algorithms against your data and picks the most accurate one without you having to know which algorithm is appropriate. The Proactive Insights show which fields in your CRM data are driving the predictions, which is separately… Read full discussion →