AI Integration Services
Enterprise AI integration: LLM APIs, MCP/A2A agent connectivity, legacy system modernization, and ERP/CRM enhancement. Projects start at $5,000.
- CONNECT AI TO CRM, ERP, AND HRIS YOU ALREADY USE
- OVERLAY INTEGRATION — NO CORE SYSTEM REPLACEMENT
AI integration is the engineering of connectivity layers that embed large language models, AI agents, and intelligent automation into existing enterprise software systems. These layers turn CRM, ERP, and HRIS platforms into systems that respond to natural language queries and route decisions to automated workflows. Riverborn is an AI integration company that connects GPT-5.6, Claude Sonnet 5, Gemini, and Llama 4 to enterprise systems via REST APIs, MCP, and A2A protocols. Riverborn's 10+ production systems serve 100K+ users on this same integration infrastructure.
What Your Team Gets
GPT-5.6, Claude Sonnet 5, Gemini Pro, and Llama 4 connected to enterprise applications via REST API. Structured output parsing, function calling, and streaming come standard.
Model Context Protocol connects AI models to enterprise tools and data via a standard protocol, replacing custom API adapters per Anthropic's specification.
AI agents from different vendors communicate, delegate tasks, and discover capabilities across systems. Riverborn implements A2A per Google's specification.
custom connectors for Salesforce, SAP, Workday, ServiceNow, and HubSpot using OAuth 2.0 authentication and webhook callbacks.
an AI overlay adds LLM query capabilities to existing ERP, CRM, and HRIS systems via API gateway, without touching core architecture.
fixed scope 4 to 6 week engagement for a single integration target. Engagements start at $5,000.
What We Build: AI Integration System Types
Riverborn delivers six categories of AI integration services covering ChatGPT integration, agent connectivity, and enterprise modernization.
AI integration is a specialization within enterprise software engineering and applied AI. Integration types include LLM API integration, agent system integration, legacy AI modernization, and enterprise connector development. An AI integration system consists of an API gateway, authentication layer, model routing engine, data transformation pipeline, and monitoring layer.
LLM API Integration
GPT-5.6, Claude Sonnet 5, Gemini Pro, and Llama 4 integrated into enterprise applications via REST API. Structured output parsing, function calling, streaming responses, and token management handle production-scale inference.
ChatGPT and Generative AI Integration
ChatGPT integration embeds GPT-5.6 into customer support platforms, internal knowledge bases, and workflow tools via the OpenAI API, extending to Claude, Gemini, and open-source alternatives through a unified routing layer.
MCP-Based Integration
Model Context Protocol standardizes connectivity between AI models and enterprise tools, eliminating custom API adapters. Riverborn implements MCP tool registration, resource exposure, prompt serving, and sampling per Anthropic's specification.
A2A Agent Connectivity
The A2A protocol enables AI agents from different vendors to communicate, delegate tasks, and discover capabilities. Riverborn implements agent cards, task management, streaming, and push notifications per Google's A2A specification.
Legacy System AI Modernization
An AI overlay adds LLM query capabilities to legacy ERP, CRM, and HRIS systems via API gateway without replacing core infrastructure. LLM responses surface directly from existing database records without any ERP code change.
Enterprise Connector Development
Custom connectors for Salesforce, SAP, Workday, ServiceNow, and HubSpot via OAuth 2.0 authentication, webhook callbacks, and event-driven triggers. Each handles authentication, data transformation, and error handling.
Our Technical Approach
According to McKinsey’s 2024 enterprise AI adoption survey, 72% of organizations use AI in at least one business function. MuleSoft’s 2025 Connectivity Benchmark Report finds that 95% of IT leaders say integration issues impede AI adoption. The gap between adoption and production-grade integration is where most implementations stall.
Integration Architecture Methodology
01. Integration Pipeline Architecture
Six layers: API gateway, authentication, model routing, data transformation, response processing, and observability. Every enterprise integration follows this pipeline without exception.
02. Multi-Model Routing
Requests route to GPT-5.6 for complex reasoning, Claude Sonnet 5 for long documents, and Llama 4 for batch processing at scale. Automatic fallback logic handles provider outages without manual intervention.
03. MCP Implementation
Server configuration covers tool registration, resource exposure, prompt serving, and sampling. Connects enterprise data sources to AI models via Anthropic's standardized protocol, eliminating custom adapter maintenance.
04. A2A Implementation
Covers agent cards, task management, streaming responses, and push notifications. Agent cards allow capability discovery across vendor boundaries without custom adapter development, per Google DeepMind's specification.
05. Security Layer
OAuth 2.0 handles authentication across all connector integrations. JWT token management, API key rotation, and mTLS certificates cover service-to-service security. Input and output validation runs on every request.
06. Observability
Datadog and Grafana dashboards for request logging, latency tracking at p95 and p99, token usage monitoring, and error alerting from day one of production.
Integration Architecture Reference
Standardised integration patterns for connecting LLM capabilities to enterprise software architectures.
| Component | Technologies & Protocols |
|---|---|
LLM APIs | GPT-5.6Claude Sonnet 5Gemini ProLlama 4 |
Protocols | RESTGraphQLWebSocketMCPA2AgRPC |
Authentication | OAuth 2.0JWTmTLSAPI key management |
Cloud & Gateway | AWS API GatewayGCP Cloud RunGCP Compute EngineAzure API ManagementKong |
Messaging | Amazon SQSRabbitMQApache KafkaRedis Pub/Sub |
Enterprise Connectors | SalesforceSAPWorkdayTwentyServiceNowHubSpotJiraSlack |
Monitoring | DatadogGrafanaCloudWatchToken cost trackingCustom dashboards |
Data Transformation | Apache AirflowCustom ETL pipelinesJSONataData validation |
LLM APIs
Protocols
Authentication
Cloud & Gateway
Messaging
Enterprise Connectors
Monitoring
Data Transformation
Industries and Use Cases
Enterprise: CRM Enhancement
Sales team managed 200+ daily Salesforce deal reviews with no AI capability. GPT-5.6 integrated via the Salesforce REST API to generate call summaries, score deals, and draft follow-ups. Rep productivity improved 35%.
Customer Support: AI Layer
Support platform handled 5,000+ daily tickets via manual triage. Claude Sonnet 4 integrated via the Anthropic API to classify tickets, draft responses, and flag escalations. First response time dropped 60%.
Manufacturing: Legacy ERP Modernization
Manufacturer ran SAP R/3 with no natural language reporting. API gateway deployed above the existing SAP database to expose data to GPT-5.6. Report generation time dropped 80%, no ERP migration required.
Multi-Agent System Connectivity
Enterprise deployed AI agents from three vendors with no cross-system communication. A2A protocol implemented across the agent ecosystem for task delegation and capability discovery. Handoffs complete across vendor boundaries.
Technology Stack
Every AI integration project ships on the production validated stack below.
| Stack Layer | Technologies & Frameworks |
|---|---|
LLM APIs | GPT-5.6 (OpenAI)Claude Sonnet 5 (Anthropic)Gemini Pro (Google)Llama 4 (Meta via vLLM) |
Protocols | RESTGraphQLWebSocketMCPA2AgRPC |
Authentication | OAuth 2.0JWT token managementAPI key rotationmTLS for service-to-service |
Cloud & Gateway | AWS API Gateway + LambdaGCP Cloud RunAzure API ManagementKongNginx |
Messaging | Amazon SQSRabbitMQApache KafkaRedis Pub/Sub |
Enterprise Connectors | SalesforceSAPWorkdayServiceNowHubSpotJiraSlack |
Monitoring | DatadogGrafanaCloudWatchCustom dashboardsToken cost tracking |
Data Transformation | Apache AirflowCustom ETL pipelinesJSONataData validation |
LLM APIs
Protocols
Authentication
Cloud & Gateway
Messaging
Enterprise Connectors
Monitoring
Data Transformation
How It Works: Our Delivery Process
Five-step process. Standard engagements complete in 6 to 7 weeks. The AI Integration Sprint delivers a single defined integration in 4 to 6 weeks as a fixed scope package, starting at $5,000.
System Audit and Integration Mapping
Inventory existing systems, APIs, data flows, and authentication mechanisms. Rank integration opportunities by implementation effort and business impact before architecture work begins.
Deliverable
Architecture Design and Protocol Selection
Design the API gateway layer, model routing logic, and security specifications. Select REST, MCP, or A2A based on the integration target and standardization requirements.
Deliverable
Integration Build and Connector Development
Build the API integration layer, enterprise connectors, authentication implementation, and data transformation pipelines. Weekly build reviews ensure scope alignment.
Deliverable
Testing and Security Validation
Load testing, security audits, failover testing, and latency benchmarking against p95 and p99 targets. Input and output validation runs across all endpoints before production clearance.
Deliverable
Production Deployment and Monitoring
Deploy to production infrastructure. Datadog or Grafana dashboards cover request logging, token tracking, and error alerting from day one. Full documentation and handover package delivered.
Deliverable
Why Riverborn
Gartner’s 2024 API management report shows organizations with unified API governance reduce integration failure rates by 40% compared to disconnected point-to-point integrations.
MCP and A2A as next generation integration standards.
Riverborn implements both Model Context Protocol and A2A across client integrations. MCP replaces custom API adapters per Anthropic's specification. A2A enables inter-agent task delegation across vendor boundaries per Google's specification. Riverborn applies the same patterns for clients that it runs internally across 10+ products.
Every Riverborn product uses the same integration patterns.
The architecture connecting Vocalo.ai, QuizMakerAI, and Rachona AI to their AI model backends runs on the same patterns Riverborn builds for enterprise clients.
A cost structure that makes enterprise integration accessible.
Riverborn's Bangladesh delivery model provides production-grade capability at 40 to 60% of the cost of comparable US and EU integration firms. The AI Integration Sprint delivers a fixed scope integration in 4 to 6 weeks, starting at $5,000.
4.8+ avg.client rating
MCP + A2Anext-generation protocols in production
100K+global users on Riverborn AI systems
Multi-model routingGPT-5.6 + Claude Sonnet 5 + Gemini + Llama 4
AI Integration Sprintfixed scope, 4 to 6 weeks
40 to 60% cost advantagevs. US/EU integration firms
Related Services
AI Agent Development
Organizations deploying AI agents into enterprise environments require integration architecture before agent deployment. Riverborn's AI agent development service builds the agents this integration layer connects.
Learn more →Agentic AI Systems
Multi-agent deployments using A2A protocol require dedicated agentic architecture alongside integration. Riverborn's agentic AI systems service covers the full multi-agent architecture scope.
Learn more →AI Consulting and Strategy
Organizations evaluating which systems to integrate first can access Riverborn's AI consulting and strategy service for a Maturity Audit before committing to a build.
Learn more →Industries We Serve
Riverborn's AI integration engagements concentrate in financial services, manufacturing, and SaaS and technology. Each vertical is covered with connector-specific patterns in the Industries and Use Cases section above.
Frequently Asked Questions
AI integration services connect large language models, AI agents, and intelligent automation to existing enterprise software systems via APIs, protocols, and custom connectors, embedding AI capability without replacing core infrastructure. Riverborn delivers integration through REST APIs, MCP, A2A, and enterprise connector development.
Riverborn integrates GPT-5.6 via the OpenAI REST API with structured output parsing, function calling, and OAuth 2.0 authentication. Custom middleware connects the model to platforms like Salesforce, SAP, and ServiceNow, including monitoring dashboards and token cost tracking.
MCP is Anthropic's open protocol for standardized connectivity between AI models and enterprise tools and data sources. It replaces custom API adapters with protocol-level interoperability, covering tool registration, resource exposure, prompt serving, and sampling.
A2A is Google's protocol enabling AI agents from different vendors to communicate, delegate tasks, and discover capabilities. It implements agent cards, task management, streaming, and push notifications for multi-agent orchestration across enterprise systems.
Standard engagements complete in 6 to 7 weeks across five phases: system audit, architecture design, integration build, security testing, and production deployment with monitoring. The AI Integration Sprint delivers a single defined integration in 4 to 6 weeks.
Yes. Riverborn specializes in AI overlay integration, adding LLM capabilities to legacy ERP, CRM, and HRIS systems via API gateway without replacing core infrastructure. Existing data is exposed to AI models without touching databases or application code.
GPT-5.6 (OpenAI), Claude Sonnet 5 (Anthropic), Gemini Pro (Google), Llama 4 (Meta via vLLM), and Mistral Large. Riverborn builds multi-model routing layers for automatic provider selection with fallback logic for outages.
Cost depends on system complexity, connector count, and security requirements. The AI Integration Sprint starts at $5,000 for a fixed scope single system integration. Riverborn's Bangladesh delivery model provides 40 to 60% cost advantage versus US and EU firms.