AI Integration Services - Connect AI to Your Enterprise Stack

Generative AI integration is the process of connecting large language models and AI capabilities to your existing business systems — CRMs, ERPs, helpdesks, databases, and custom applications. Instead of building standalone AI tools, we embed AI directly into the platforms your teams already use.

The integration typically involves three layers:

• API layer: Secure connections between your systems and AI providers (OpenAI, Anthropic, Google)
• Context layer: RAG pipelines that give AI access to your enterprise data and documents
• Action layer: Tool integrations that let AI take actions — update records, send emails, create tickets

The result: AI that works inside your existing tools, with your data, following your business rules.

Costs depend on the number of systems to integrate, data complexity, and security requirements:

• Single system integration: $25,000–$60,000 for integrating AI into one platform (e.g., CRM copilot, support ticket automation, document processing).
• Multi-system integration: $60,000–$150,000 for connecting AI across 3-5 systems with shared context, RAG pipelines, and unified monitoring.
• Enterprise-wide AI integration: $150,000–$400,000+ for organization-wide deployment with custom middleware, governance, multi-department rollout, and training.

Most clients start with a single high-impact integration ($25K–$50K) to prove ROI, then expand. We offer fixed-price, T&M, and dedicated team models.

We offer outcome-based pricing — pay for delivered integration results, not hourly billing. This means you get a fixed scope with guaranteed deliverables, not an open-ended time-and-material engagement.

Yes — legacy system integration is one of our core strengths. Many enterprise systems were not designed for AI, but that does not mean they cannot benefit from it.

Our approach for legacy systems:

• API wrappers: We build custom API layers around legacy systems that lack modern APIs
• Database connectors: Direct database integration for systems with accessible data stores
• Screen scraping + AI: For systems with no API and no direct database access
• Middleware bridges: Integration platforms that translate between legacy protocols and modern AI APIs

We have integrated AI with SAP R/3, AS400 systems, Oracle E-Business Suite, and custom COBOL-based applications. If your system has data, we can connect AI to it.

Timeline depends on the scope and complexity:

• Single integration POC: 2–4 weeks for a proof-of-concept connecting AI to one system
• Production single integration: 4–8 weeks with security, monitoring, and testing
• Multi-system integration: 8–14 weeks connecting AI across multiple platforms with RAG
• Enterprise rollout: 14–24 weeks for organization-wide deployment with training

The biggest variables are data preparation (how clean and accessible your data is) and security requirements (compliance reviews, penetration testing). We recommend starting with one high-impact integration to prove value, then expanding systematically.

Data security is built into every layer of our integration architecture:

• Encryption: All data encrypted at rest and in transit (AES-256, TLS 1.3)
• Access control: Role-based permissions ensuring AI only accesses authorized data
• PII handling: Automatic PII detection and redaction before data reaches LLM APIs
• Data residency: Options for on-premise LLM deployment or region-specific cloud hosting
• Audit logging: Every AI query, response, and data access logged for compliance
• Certifications: ISO 27001, SOC2 Type II, GDPR, and HIPAA compliant processes

For highly sensitive environments, we deploy private LLM instances on your infrastructure so no data ever leaves your network.

We are model-agnostic and choose the right tools for each use case:

• LLMs: OpenAI GPT-4o, Anthropic Claude, Google Gemini, Meta LLaMA 3, Mistral
• Orchestration: LangChain, LlamaIndex, LangGraph for complex AI workflows
• Vector databases: Pinecone, Weaviate, ChromaDB, Qdrant for RAG pipelines
• Cloud AI: AWS Bedrock, Azure OpenAI, Google Vertex AI for enterprise deployment
• Middleware: Custom API gateways, webhook processors, and event-driven architectures
• Monitoring: LangSmith, Langfuse, custom dashboards for cost and quality tracking

We select technology based on your requirements — cost, latency, compliance, and existing cloud infrastructure — not what is trending.

We integrate AI with virtually any enterprise system:

• CRM: Salesforce, HubSpot, Zoho, Pipedrive, Microsoft Dynamics
• ERP: SAP, Oracle, Odoo, NetSuite, Microsoft Dynamics 365
• Helpdesk: Zendesk, ServiceNow, Freshdesk, Jira Service Management
• Communication: Slack, Microsoft Teams, email (Exchange, Gmail), WhatsApp
• Document management: SharePoint, Confluence, Google Drive, Notion
• Databases: PostgreSQL, MongoDB, Snowflake, BigQuery, Redis
• Custom applications: Any system with REST/GraphQL APIs or database access

If your system has data or APIs, we can connect generative AI to it — including legacy systems that were not designed for modern integrations.