INFORMAT

What INFORMAT is

INFORMAT is an AI-native enterprise system platform for building databases, workflows, APIs, dashboards, websites and "digital employees" from natural language. It is written for operations and IT teams that want custom ERP-style systems, internal tools and portals without a long build. The site's own framing is that most AI builders "stop at pages and forms", while INFORMAT creates "the operating layer for custom ERP and operational workflows": connected data, process logic, integrations, dashboards and governed AI execution. The headline promise is to turn procurement, inventory, manufacturing, finance and service requirements into governed business systems. Its FAQ answers the obvious objection directly: asked whether it is just another app builder, the company says no, because it is designed to generate and operate full business systems, including data models, process logic, APIs, dashboards and AI agents.

What you build and how

You start from one business process described in your own words. The home page invites you to describe a procurement, inventory, manufacturing, finance or service process, and says INFORMAT will help structure the data, the workflow, the controls, the dashboards, the integrations and the AI assistance around it. Its tagline for the first step is "Start with one process", and it says the platform generates the connected data model, workflows, APIs, permissions and operational dashboards. The example processes on the home page are customer support, supplier management, HR onboarding and property operations, and the prompt box offers Sales CRM and Warehouse as starting points. The company says the average time to a first app is 30 seconds.

The how-it-works section has four steps. First you describe the business problem in plain English, from CRM and ERP modules to case management and customer service systems. Second, the platform generates the foundation: schema, forms, relationships, permissions, process flows, dashboards, APIs and websites together. Third, you refine it with AI or manually, adjusting fields, updating workflows, adding integrations and expanding logic through prompts or visual configuration. Fourth, you deploy and operate the system with dashboards, access control, APIs, automations and AI agents that keep working after launch. The site sums up the generation step as 5 layers produced together: UI, data, workflow, API and AI agents.

The core capabilities are described in four blocks. "Database-first" generates structured tables, field types, relationships, validation rules and permissions from natural language. The workflow engine covers approvals, automations, task routing, notifications, SLAs and business processes. "Instant APIs" exposes secure APIs for integration, automation, portals, AI agents and third-party systems. Operational dashboards turn business data into charts, KPI boards, analytics and management views. Generation is not the end of it: the FAQ says you can keep refining the system "using prompts, visual configuration, scripts, APIs, and workflow editing", so there is a visual layer, a scripting layer and an API layer under the prompt.

The AI side is where INFORMAT puts most of its weight. The site explains the architecture as a "system graph": AI drives agents, agents use skills, and skills operate the actual system layer of database, workflow, API, dashboards, websites and automation. Every system can come with agents that understand your schema, call tools, follow process rules and act on live business data. The home page names four. The Operations Agent monitors records, identifies bottlenecks, generates reports and recommends next actions. The Workflow Agent launches approvals, routes exceptions, updates statuses and coordinates multi-step processes. The Data Agent queries system data, summarizes trends and answers operational questions. The Builder Agent creates tables, APIs, dashboards, websites and automations as the system evolves. A sample console shows a user asking for a procurement system with suppliers, purchase requests, approvals, orders, receiving, invoice matching and spend dashboards; the agents respond with purchasing tables, approval rules, role-based permissions, budget checks, approval thresholds, three-way matching and dashboards for spend and supplier delivery. The output lists APIs such as ERP Sync, Supplier Portal and Finance Export.

For the internal-tools reader, the use cases are concrete. The site lists CRM and revenue operations (leads, pipeline, approvals, sales analytics), case and service management (tickets, escalations, SLAs, knowledge workflows), operations systems (supply chain, warehouse, project approvals, compliance records, inspections) and portals and internal tools (partner portals, staff systems, AI copilots, admin back offices and role-based data access). Ready-made solutions include an AI CRM Builder, an AI Inventory Management System with stock tables, reorder alerts and warehouse workflows, and an AI Employee Onboarding System with HR checklists, document collection, task routing and assistant agents. There are two free helper tools as well: a CRM Schema Generator that outlines a CRM data model before you build, and a Workflow Prompt Generator that drafts approval, routing, notification, SLA and exception rules. The company recommends launching one focused workflow first, validating it with real users, then expanding on the same platform.

Plans, limits and security

The home page publishes 5 plans under the heading "Start simple, scale to enterprise", each priced per month for a set number of users rather than per seat:

• Free, $0.00 a month, for exploration and proofs of concept: 5 users, up to 3 apps, 10 tables per app, up to 10,000 rows per table, 1 GB of file storage, 1,000 gift points a month, and a 7-day free trial listed among its terms.
• Lite, $10.00 a month, for small teams starting their first system: 10 users, up to 5 apps, 20 tables per app, up to 20,000 rows per table, 10 GB of storage and 20,000 points a month.
• Pro, $60.00 a month, for growing teams: 30 users, up to 10 apps, 100 tables per app, up to 200,000 rows per table, 50 GB of storage and 50,000 points a month.
• Ultra, $200.00 a month, for high-volume operations: 50 users, up to 200 apps, 200 tables per app, up to 500,000 rows per table, 100 GB of storage and 100,000 points a month.
• Enterprise, quoted by sales: a minimum of 100 accounts, unlimited app and table quota, private deployment, scripts and automatic functions, custom AI credits and one-on-one customer support.

The paid plans other than Enterprise are listed with monthly renewal. Note that scripts and automatic functions appear in the plan list only under Enterprise.

On hosting, the security section says you can run on shared cloud, private cloud or your own infrastructure "with full control over data residency", and the FAQ says enterprise deployments can run in private cloud or on-prem environments depending on your security needs. The same section promises role-based permissions, auditable processes and secure system boundaries across teams, and says agents work through governed skills and system actions so teams keep oversight. The home page also states four figures about the company: 2,000+ enterprise systems built, 50+ countries and regions, 99.9% platform uptime and a 30s average time to a first app. These are the vendor's own numbers, given without a period or method. A WhatsApp contact link sits on every page.

Who it suits and who it does not

INFORMAT suits an operations or IT team that has a process living in spreadsheets, email and a few disconnected tools, and wants a governed system built around it quickly. The prompt-first start fits a team that can describe the process clearly, such as an HR lead describing onboarding or a procurement manager describing source-to-pay, and then hand the generated system to IT for review. The published plans make it easy to try: the Free tier holds 3 apps for 5 users, and the jump to Lite or Pro is small. Buyers who need a private-cloud or on-prem option are covered at the Enterprise level.

It is a weaker fit for a solo founder who wants a public marketing site or a polished consumer app, because the pitch is operational systems rather than design control. A team with a vague process will get less from a prompt-driven generator, since the quality of the output depends on how well the process is described. And the row caps per table matter for data-heavy work, so check the plan against your volumes before committing.