Celigo's new natural language interface lets anyone build and manage integrations by describing what they need. Here's what this means for small businesses drowning in manual workflows.
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If you've ever tried to set up an integration between two business systems, you know the pain. API documentation that reads like it was written for robots. Field mapping screens that look like spreadsheets designed by committee. Error messages that require a computer science degree to decode.
Celigo just announced something that might finally kill that pain:
Ora
, a natural language interface that lets you build, manage, and troubleshoot integrations by literally describing what you want.
No API schemas. No manual field mapping. No 2 AM debugging sessions.
If your business is running multiple systems that need to talk to each other (and whose isn't?), this is worth understanding. We do free 30-minute discovery calls to help you figure out whether new tools like this fit your automation roadmap.
What Ora Actually Does
Celigo is an iPaaS (Integration Platform as a Service) that connects business systems—your CRM to your accounting software, your ecommerce store to your inventory system, your HR platform to your payroll. Over 1,000 prebuilt connectors, templates for common workflows, the works.
The problem: someone still has to configure all of this. That someone usually has technical skills, which creates a bottleneck. The operations team knows what they need but can't build it. IT can build it but has a ticket queue. Everyone waits.
Ora removes that bottleneck. You type (or speak) what you need in plain English, and Ora's network of specialized AI agents handles the technical implementation. The system:
Builds integrations from conversation.
Describe what you need—"sync new Shopify orders to NetSuite as sales orders with the right tax codes"—and Ora generates the connections, mappings, and logic. It stages everything for your review before anything goes live.
Maps your entire automation landscape.
Within minutes of connecting, Ora builds a knowledge graph of every integration, flow, connection, and dependency in your account. It understands what touches what, so when you make a change, you know the ripple effects.
Diagnoses root causes.
When something breaks, ask "what went wrong?" and Ora traces the error through your audit logs and dependency graph to find where it actually started—not just where it surfaced.
Coordinates across systems.
Multiple specialized agents (flows, connections, error management, scripts, APIs) work together under the hood. You interact with one interface; the orchestration happens behind the scenes.
The Number That Caught My Eye
Celigo partnered with MIT Technology Review Insights on research that reveals something important:
95% of executives expect AI autonomy to increase. Only 1% of companies without a unified integration strategy have successfully scaled AI beyond a single department.
That's a massive gap between expectation and reality. Everyone wants AI to run more of their business. Almost no one has the infrastructure to make it happen safely.
The companies that
have
scaled AI successfully?
90% of them already use an integration platform (iPaaS).
The pattern is clear: before you can run AI agents across your business, you need your systems connected and talking to each other through something more sophisticated than manual exports and imports.
We wrote about how we diagnose broken business processes before recommending AI because the same principle applies here. AI agents can't automate what isn't connected.
Why This Matters for SMBs
Natural language interfaces for enterprise software are having a moment. Microsoft Copilot, Salesforce Agentforce, now Celigo Ora. The pattern: vendors are realizing that the barrier to adoption isn't the software's capabilities—it's the expertise required to use them.
For a 20-person company, this is significant:
Your operations manager can build their own integrations.
They understand the business process. They know what data needs to move where. Previously, they needed IT or a consultant to translate that knowledge into technical configuration. Now they describe it, review the preview, and approve.
Your IT specialist focuses on architecture, not configuration.
Less time mapping fields means more time on security, compliance, and strategic decisions about which systems should connect and why.
Your troubleshooting cycle shrinks from hours to minutes.
Instead of navigating multiple screens to trace an error across systems, you ask a question and get an answer that connects the dots.
This doesn't eliminate the need for technical expertise entirely. Someone still needs to understand your data model, your security requirements, and your compliance obligations. But it shifts the day-to-day from "how do I make this work?" to "what should this do?"
What's Available Now
The announcement includes three pieces, each at a different stage:
Ora (Beta).
The natural language interface. Currently in beta, which means it's real but evolving. Early users will shape how it develops.
Agent Builder (Generally Available).
A low-code environment for creating AI agents that can reason through tasks and take action across systems. This is for more sophisticated automation—agents that don't just move data but make decisions about what to do with it.
Enterprise MCP Server (Generally Available).
The Model Context Protocol server provides standardized, governed connectivity between AI agents and enterprise systems. Think of it as a security layer that lets AI agents interact with your business data in controlled, auditable ways.
For most SMBs, Ora is the starting point. If you're already running integrations manually or through fragile scripts, a natural language interface to an iPaaS is a meaningful upgrade.
Do You Need This?
Not every business needs an iPaaS. Here's the honest breakdown:
If you're running 1-2 systems and data moves between them occasionally
, manual exports or simple automation tools (Zapier, Make) might be sufficient. The complexity doesn't justify the platform investment yet.
If you're running 3+ business systems with regular data flows
—orders flowing from ecommerce to accounting, leads syncing between marketing and CRM, inventory updates across multiple channels—an iPaaS becomes valuable. Ora specifically reduces the expertise required to get value from that investment.
If you're planning to deploy AI agents that need to access multiple systems
, the MCP Server piece matters. AI agents without controlled access to business data are either useless or dangerous. A governance layer that's already integrated with your iPaaS is infrastructure worth having.
The research data point matters here: if you want AI agents to run your business processes, you need integration infrastructure first. The companies successfully scaling AI almost all have it. The companies struggling to move beyond pilots mostly don't.
What You Should Do Now
Ora is in beta, which means two things: it's available to try, and it's still maturing. If integration pain is slowing your business down:
Take inventory of your manual workflows.
Every place someone exports data from one system and imports it to another, or manually copies information between screens, is a candidate for automation. List them. Prioritize by time spent and error rate.
Evaluate your current integration approach.
Are you running scripts that break? Paying a consultant for every change? Waiting weeks for IT to connect two systems? These are signs that your current approach doesn't scale.
Consider the AI angle.
If you're thinking about AI agents for your business in the next 12 months, understand that those agents need connected systems to be useful. An iPaaS isn't optional infrastructure for AI adoption—it's foundational.
If you're not sure where to start, we can help you map out what should be automated and what infrastructure makes sense for your specific situation . No pressure, no sales pitch—just clarity on what would actually help.
The Bigger Picture
Celigo's announcement is part of a larger shift. Enterprise software is moving from "here are powerful tools that require expertise to use" to "tell me what you want and I'll figure out how to make it happen."
That shift has two implications for SMBs:
First, the expertise bottleneck is collapsing. Tools that previously required consultants or dedicated technical staff are becoming accessible to the people who actually understand the business problems.
Second, the gap between companies with good integration infrastructure and companies without it is widening. AI agents, natural language interfaces, and autonomous workflows all assume your systems can talk to each other. If they can't, you're locked out of the next wave of automation.
Ora makes integration infrastructure more accessible. The question isn't whether natural language automation will become standard—it will. The question is whether your business will have the foundation in place to benefit from it.