Connect AI models to NetSuite, Salesforce, and other integrated platforms through Celigo's controlled layer. Real-time data access. Full audit trails. No credential exposure.
Talk to an integration expert →Celigo supports two distinct patterns for bringing AI into your integration stack. The first is AI Agents embedded inside flows — the model participates as a processing step, receiving only what the flow exposes to it, and returning structured output that the rest of the flow acts on.
The second is MCP Server acting as a governed query layer — giving AI assistants direct, real-time access to your connected systems through Celigo's controlled interface. The model authenticates via token. Queries are logged. No credentials enter the model context.
Both patterns keep AI away from raw API credentials. Both produce audit logs. Neither requires maintaining a parallel, unmonitored connection between your AI tooling and your systems of record.
An AI model embedded as a processing step inside a Celigo integration flow — it receives a structured payload, evaluates it, and returns structured output that the downstream flow acts on.
A Celigo-hosted interface that lets AI assistants query your integrated systems in real time — without holding API credentials. Authentication, rate limiting, and query logging are all handled by the MCP layer.
Giving an AI model direct API credentials means those credentials live in the model context, in prompts, and in logs. A single leaked prompt exposes live system access. A governed layer eliminates this entirely.
Direct API calls from AI models are invisible to your ops team. You can't tell what was queried, when it was queried, or what the model received in return — until something goes wrong.
When an AI model calls an API directly and receives an error, there's no structured handling. The model may retry with a bad payload, infer a workaround, or silently return nothing — any of which can corrupt downstream records.
Direct AI-to-API integrations live outside your integration platform — maintained separately, monitored separately, owned by whoever built the prompt. When they break, no one knows where to look.
A native Celigo flow step that routes a payload to a configured AI model, receives the response, and maps it to structured output fields. Supports OpenAI, Anthropic Claude, and Google Gemini — configurable per flow.
A Celigo-managed interface that exposes your integrated systems to AI assistants via the Model Context Protocol. Handles token authentication, rate limiting, input scoping, and full query logging — all configurable without custom infrastructure.
JavaScript hooks before and after AI steps let you sanitize inputs, validate model outputs, enforce field-level business rules, and prevent any unvalidated data from reaching a system of record.
Celigo's built-in retry logic, dead letter queues, and alerting apply to AI steps the same as any other flow step. AI failures surface in the same dashboard as the rest of your integrations.
Before writing a single flow, establish exactly what the AI model is allowed to decide, query, and modify. Document it as a constraint spec — not a design guideline.
The system prompt is part of the integration specification, not an afterthought. We version it, review it, and test it the same way we'd test a field mapping.
Every AI response passes through a validation step that checks structure, range, and business logic before any downstream write operation hits a system of record.
Logging, monitoring, and alerting go in from day one. Retroactively instrumenting a high-volume AI flow is expensive. We don't build without them.
For MCP Server implementations, we configure the Celigo MCP layer to expose a defined set of queries against your connected systems. Each query has defined inputs, outputs, and access scope. The AI assistant — whether an internal tool, a Claude Project, or a custom LLM application — authenticates via scoped token, not credentials. All queries are logged with timestamp, model identifier, input parameters, and a response hash. We scope the query surface tightly: the AI can query what you define, nothing more.
Real-time NetSuite access for an internal AI assistant — finance team queries order status, inventory levels, and customer records without opening NetSuite. Zero credential exposure.
NetSuite integrations →AI outreach orchestration across three model providers in a single Celigo flow. 10,000+ leads processed simultaneously with automated HubSpot record updates and Smartlead deployment.
See related flows →Automated resume screening, Whisper transcription of recorded interviews, and AI-generated candidate summaries appended directly to Google Sheets candidate records. 4+ hours saved per recruiter per day.
See featured case study →AI-driven opportunity scoring — new Salesforce opportunities are evaluated by a Celigo AI Agent and high-priority deals are routed to Slack with context, owner, and recommended next step.
Salesforce integrations →A mid-market client needed their internal AI assistant to answer operational questions in real time — current inventory levels, open order status, recent customer activity. The existing process was a 24-hour reporting cycle: someone ran a NetSuite report, exported it, and shared it. Questions that should take seconds took a day.
We implemented a Celigo MCP Server exposing four NetSuite query types: inventory lookup by SKU, order status by order number, customer activity by account ID, and open AP/AR summary. The AI assistant — a custom internal tool built on Claude — authenticates via a scoped token managed by Celigo. No NetSuite credentials appear in the model context, the system prompt, or any log.
Every query is captured by the MCP layer: timestamp, input parameters, which model made the request, and a hash of the response. The client's ops team can audit every AI-initiated query from a single Celigo dashboard — the same place they monitor the rest of their integrations.
Read the full write-up: Celigo MCP Server — Governed AI Access to NetSuite → · What an AI agent for NetSuite costs →
Also: AI Agent Governance — closing the 2026 enterprise gap →
Security: what Celigo's MCP Server blocks, and what it doesn't →
Celigo AI Agent is a capability within Celigo integration flows that allows the flow to call AI models (like OpenAI or Claude) mid-execution. The agent can interpret unstructured data, make conditional routing decisions, enrich records, and trigger downstream actions — all inside a governed integration environment.
The Celigo MCP Server exposes your integrated systems — NetSuite, Salesforce, Shopify — as structured tools that an AI assistant can query using the Model Context Protocol (MCP). The AI never accesses your systems directly; all requests route through Celigo and are fully logged.
Yes. Entech has live production MCP Server deployments giving AI assistants real-time, governed access to NetSuite data. The AI queries ERP records in natural language while Celigo enforces access controls and maintains a full audit log of every query.
Celigo AI Agents can automate: lead scoring and CRM data enrichment, candidate screening from unstructured applications, natural-language ERP data retrieval, document classification and routing, and multi-step decision flows that previously required human judgment at each step.
A direct API connection gives the AI model full credential access to your ERP — any query, any record. Governed AI access via Celigo MCP Server restricts what the AI can query (to specific tools and scopes), logs every request, and keeps credentials in Celigo's secure connection layer rather than exposed to the AI model.
We've implemented both patterns across multiple clients and know where the complexity is. Tell us what you're building and we'll scope it.