Build the integration layer that lets LLMs and AI agents read, reason and act across SAP, Salesforce, Workday, ServiceNow, databases, APIs, documents and custom applications — with enterprise-grade governance, observability and control.
Expose governed business capabilities for AI
Enable secure tool execution through MCP
Trace prompts, APIs, pipelines and casts
A modern enterprise AI integration stack isn’t a single platform — it’s eight co-operating layers, each with distinct concerns. IWConnect designs, builds, and manages all of them.
Web apps, mobile clients, B2B partners, IoT devices, 3rd-party SaaS — any surface that needs AI-powered data or actions.
REST | GraphQL | WebSocket | EDI | MQTT
Unified entry point with AI-aware traffic routing, OAuth 2.0 / mTLS auth, dynamic rate limiting, threat detection, and a self-serve developer portal.
Kong | AWS API GW | Azure | APIM | Rate limiting | Developer portal
Centralised LLM access layer enforcing model routing policies, token budgets per team, PII redaction before prompts leave the firewall, and every call logged for audit.
REST | GraphQL | WebSocket | EDI | MQTT
Model Context Protocol server exposing versioned, permissioned tool definitions for SAP, Salesforce, Workday — so AI agents can discover and invoke enterprise capabilities safely.
MCP server | Tool versioning | Permission scopes | Agent discovery
Multi-agent workflows that span multiple enterprise systems end-to-end, with RAG for live context, event-driven triggers, and human-in-the-loop escalation for edge cases.
Multi-agent | RAG pipelines | Event-driven | Human-in-loop
AI-enhanced data pipelines, self-healing connectors that detect schema drift, AI-assisted mapping, and event streaming via Kafka. SnapLogic, MuleSoft, Azure Logic Apps, and custom.
SnapLogic | MuleSoft | Boomi | Azure Logic Apps | Kafka | LangfuseSelf-healing
End-to-end LLM call tracing, per-team cost attribution, drift and quality scoring on model outputs, policy engine for data classification, and compliance-ready audit exports.
LLM tracing | Cost dashboards | Drift alerts | Audit logs | Policy engine
The authoritative data sources and action targets: ERP, CRM, HCM, ITSM, data warehouses, mainframes, and bespoke legacy platforms.
SAP | Salesforce | Workday | ServiceNow | Oracle | Legacy ERP
Design and implement a secure AI integration backbone using SnapLogic, APIs, gateways, MCP, custom services and reusable enterprise patterns.
Add classification, summarization, enrichment, routing, exception handling and intelligent transformation into SnapLogic or custom integration workflows.
Expose enterprise capabilities through secured APIs so AI agents can act through governed interfaces instead of direct backend access.
Centralize model access, apply AI traffic policies, monitor prompts and responses, route models, reduce costs and enforce data protection controls.
Create standardized tool and context access for AI clients using Model Context Protocol, backed by enterprise authentication, authorization and auditability.
Connect documents, policies, contracts, tickets and operational knowledge to LLMs using ingestion pipelines, vector search, metadata and retrieval governance.
LLM Gateway and API Gateway
Enterprises need a policy layer between AI applications, model providers, APIs and backend systems. This layer protects data, standardizes access, manages spend and gives teams visibility into every AI-powered transaction.
• What to expose through MCP
• Approved tools such as create case, check invoice, fetch order, trigger reconciliation or update CRM activity.
• Context resources such as customer profile, contract terms, shipment status, policy documents and product data.
• Reusable prompt templates for support, finance, HR, IT operations, compliance and development workflows.
• Controlled workflow triggers for SnapLogic pipelines, APIs, scripts, data retrieval and document automation.
User or AI Agent
intent
LLM Gateway
policy, routing, logging
API Gateway
security, quota, validation
Integration Pipeline
orchestration, mapping, retries
Enterprise System
ERP, CRM, HCM, ITSM, DB
Observability Layer
audit, cost, traceability
Model Context Protocol can become the standardized interface between AI clients, enterprise tools, data resources and approved prompts.
Choose the right implementation pattern based on risk, data sensitivity, latency, action type and business process complexity.
Source system → integration pipeline → AI enrichment → validation → target system. Ideal for classification, summarization and transformation.
User → AI agent → LLM gateway → API gateway → backend system. Ideal for assistants that need to execute business actions safely.
AI client → MCP server → approved tool/API/pipeline → enterprise system. Ideal for reusable agent-to-tool connectivity.
Documents and data → indexing → vector search → LLM → governed answer or workflow action. Ideal for knowledge-heavy processes.
AI recommendation → business approval → pipeline execution → audit log. Ideal for regulated or high-risk decisions.
Business event → message queue → AI decisioning → API or workflow action. Ideal for near-real-time operational scenarios.
A structured approach that moves from discovery to architecture, delivery, testing, governance and scale.
Identify where AI can assist, decide, summarize, classify, recommend or automate business processes.
Inventory applications, APIs, data sources, owners, sensitivity, integration points and operational constraints.
Define pipelines, APIs, gateways, MCP servers, RAG services, event flows and target operating model.
Specify access control, masking, approval flows, audit logging, prompt safety, tool scopes and compliance controls.
Develop integrations, APIs, prompts, MCP tools, model policies, dashboards, CI/CD and environment promotion.
Run functional, integration, performance, security, prompt-injection, fallback and AI output validation tests.
Release safely, monitor usage, manage incidents, track costs, measure outcomes and optimize continuously.
Create reusable connectors, API products, prompt libraries, MCP tools, reference architectures and playbooks.
AI integration should be tied to business workflows where systems, documents, decisions and actions intersect.
Prompt
injection
Use input filtering, instruction separation, tool allowlists, output validation and adversarial testing.
Sensitive
data leakage
Apply data classification, redaction, tokenization, PII masking and model usage policies.
Excessive
agency
Limit autonomous actions, require approvals and use scoped permissions for each tool and API.
Insecure
tool access
Protect tools with authentication, authorization, parameter validation, sandboxing and audit logs.
Cost
overrun
Set budgets, quotas, model routing rules, token monitoring, caching and department-level reporting.
Compliance
gaps
Maintain prompt versions, model policies, trace IDs, retention rules, approvals and evidence reports.
Pete auto-generates Confluence documentation for your SnapLogic projects. From click to comprehensive docs in under a minute, no more manual documentation bottlenecks.
Easy Export & Documentation: Instantly generate detailed SnapLogic project documentation, including dependency diagrams, in PNG, CSV, and PDF formats with predefined retention periods.
Automated Compliance: Integrate Pete into your CI/CD workflows to automate documentation, versioning, and retention, closing compliance gaps.
Security at Every Step: Pete’s AI-powered code review checks 27+ metrics for code quality, maintainability, and security, reducing risks.
Streamlined Auditing: Automatically track project elements, like accounts and tasks, and access audit-ready reports with ease.
Reduction in documentation time
Zero
Manual handoffs in automated flows
Enterprise connectors available
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