Custom AI Integration Services - Connect AI to Legacy Systems, ERP, CRM & Enterprise Software

Yes. We've integrated AI with AS/400 mainframes, COBOL systems, and even paper-based processes via OCR. For legacy systems, we use: - **Screen scraping** for terminal emulators (IBM 3270, VT100) - **Database connectors** (ODBC, JDBC to Oracle, DB2, SQL Server) - **File-based integration** (FTP, SFTP for CSV, EDI, fixed-width files) - **RPA** (UiPath, Automation Anywhere) for UI-based automation - **Protocol translators** (converting proprietary protocols to REST) Example: We connected a 1987 AS/400 inventory system to GPT-4o via DB2 ODBC connector + REST API wrapper.

Timeline depends on complexity: **Simple (1-3 systems, modern APIs):** 6-8 weeks - Week 1-2: Discovery, API testing, architecture design - Week 3-5: Development (API gateway, AI integration) - Week 6-7: Testing, security audit - Week 8: Deployment, training **Production (3-8 systems, some legacy):** 10-14 weeks - +4 weeks for legacy system adapters - +2 weeks for compliance (HIPAA, SOC2) **Enterprise (8+ systems, regulated):** 16-24 weeks - +8 weeks for complex data pipelines - +4 weeks for full compliance validation (HIPAA, PCI, 21 CFR Part 11) We provide weekly status updates and demo working features every 2 weeks.

We specialize in on-premise AI deployments. Options: **1. On-Premise LLM Hosting:** - Deploy Llama-3-70B, Mistral Large 2, or fine-tuned models on your servers - Requires: NVIDIA GPUs (A100, H100) or CPU inference (slower) - We provide: vLLM, Ollama, or TGI (Text Generation Inference) setup **2. Hybrid Architecture:** - Sensitive data stays on-premise (HIPAA, PCI-DSS) - De-identified data sent to cloud AI APIs (OpenAI, Anthropic) - Example: Strip PII, send to GPT-4, merge results back **3. Secure Enclaves:** - Azure Confidential Computing (encrypted RAM) - AWS Nitro Enclaves (isolated compute) - Google Confidential VMs We've deployed 50+ HIPAA-compliant on-premise AI systems for hospitals.

Decision matrix: **OpenAI GPT-4o (Cloud):** ✅ Best for: Function calling, structured outputs, vision (image analysis) ✅ Speed: 300-800 ms response time ✅ Cost: $2.50/$10 per 1M input/output tokens ❌ Data sent to OpenAI servers (GDPR/HIPAA concerns) **Anthropic Claude 3.5 Sonnet (Cloud):** ✅ Best for: Long documents (200K context), complex reasoning, coding ✅ Safety: Lower hallucination rate than GPT-4 ✅ Cost: $3/$15 per 1M tokens ❌ Data sent to Anthropic (unless using AWS Bedrock in your VPC) **Meta Llama-3-70B (On-Premise):** ✅ Best for: Data sovereignty, HIPAA/PCI compliance, fine-tuning ✅ Cost: $0 per token (after GPU infrastructure) ✅ Customization: Fine-tune on proprietary data ❌ Requires GPUs: 2× A100 (80GB) minimum for 70B model ❌ Slightly lower quality than GPT-4 (but improving) **Our Recommendation:** Start with cloud APIs (faster iteration), migrate critical workloads to on-premise LLMs as you scale.

Multi-layer optimization strategy: **1. Semantic Caching (58% cost reduction):** - Cache AI responses for similar queries (vector similarity) - Tools: Redis + embeddings, GPTCache - Example: "What's the status of order #12345?" cached for 5 min **2. Intelligent Routing:** - Simple queries → GPT-3.5 ($0.50/$1.50 per 1M tokens) - Complex queries → GPT-4o or Claude 3.5 - Privacy-sensitive → on-premise Llama-3 **3. Rate Limit Management:** - Request queuing with exponential backoff - Multiple API keys with round-robin - Fallback to alternative models (OpenAI → Azure OpenAI → Anthropic) **4. Batch Processing:** - Non-urgent tasks batched (50% cheaper with OpenAI Batch API) - Example: Overnight report generation **Real Results:** Client (e-commerce chatbot): Reduced AI costs from $18K/month → $7.5K/month via caching + routing.

Yes. We've integrated AI with 250+ enterprise systems: **SAP:** - SAP S/4HANA: OData, REST APIs via NetWeaver Gateway - SAP ECC: BAPI, RFC via SAP JCo (Java Connector) - SAP BTP: Cloud Foundry, Kyma runtime - Modules: FI/CO, MM, SD, PP, QM, PM, HCM **Oracle:** - Oracle ERP Cloud: REST APIs, SOAP web services - Oracle E-Business Suite: XML Gateway, iSupplier Portal - Oracle Database: JDBC, SQL*Net **Microsoft:** - Dynamics 365: Web API (OData), Dataverse - Power Platform: Power Automate connectors **Others:** - Workday: REST, RaaS (Report as a Service) - NetSuite: SuiteTalk (SOAP), RESTlets - Salesforce: REST, Bulk API, Streaming API, Apex Example: We connected SAP MM (procurement) to GPT-4o for AI-powered purchase order approval (checks supplier history, pricing trends, budget compliance).

Enterprise-grade security at every layer: **Data in Transit:** - TLS 1.3 with forward secrecy - mTLS (mutual TLS) for service-to-service auth - Certificate pinning for mobile apps **Data at Rest:** - AES-256 encryption for databases, file storage - Field-level encryption for PII (SSN, credit cards) - Encrypted backups (AWS KMS, Azure Key Vault) **Authentication:** - OAuth 2.0 + OIDC (SSO with Okta, Azure AD) - API key rotation (every 90 days) - Service accounts with least privilege (RBAC) **Secrets Management:** - HashiCorp Vault for API keys, certificates - AWS Secrets Manager, Azure Key Vault - No hardcoded credentials (enforced via CI/CD) **Audit Logging:** - All API calls logged (ELK, Splunk) - Tamper-proof logs (WORM storage, blockchain) - SIEM integration (Datadog, Sumo Logic) **Compliance:** - HIPAA Business Associate Agreements (BAAs) - SOC2 Type II controls (annual audits) - PCI-DSS Level 1 (for payment data) - 21 CFR Part 11 (pharma/medical devices) We've passed 50+ security audits (Big 4 firms, client InfoSec teams).

Yes. All packages include post-launch support: **Simple Integration ($18K):** - 90 days support (business hours, 8am-6pm ET) - Slack channel for questions - Bug fixes (no SLA) - Monthly check-in call **Production Integration ($42K):** - 120 days support (24/5, M-F) - Slack + email + phone - 8-hour response SLA for critical issues - Monthly health checks (performance, cost, errors) - Quarterly optimization review **Enterprise Integration ($95K):** - 180 days support (24/7/365) - Dedicated Slack channel + on-call engineer - 1-hour response SLA (critical), 4-hour (high) - 99.95% uptime SLA with credits - Monthly architecture reviews - Quarterly roadmap planning **Beyond Included Support:** - **Managed Services:** $5K-$15K/month (full ops ownership) - **Retainer:** $180/hour (senior engineers, 20-hour minimum) - **Emergency Support:** $500/hour (same-day fixes) Most clients renew support annually at 15% of project cost.

Yes. We're experts in regulated industries: **HIPAA (Healthcare):** - Business Associate Agreement (BAA) signed before project start - ePHI encryption (at rest + in transit) - Access controls (role-based, MFA) - Audit logs (who accessed what, when) - Risk assessment documentation - 50+ HIPAA-compliant integrations (hospitals, clinics, payers) **SOC2 Type II (SaaS, FinTech):** - Control implementation (security, availability, confidentiality) - Evidence collection for annual audits - Vendor security questionnaires - 30+ clients with SOC2 certifications **PCI-DSS (Payments):** - Level 1 compliance (>6M transactions/year) - Tokenization (never store raw card numbers) - Network segmentation (cardholder data environment) - Quarterly vulnerability scans (ASV) - 15+ payment integrations (Stripe, Square, Braintree + custom) **21 CFR Part 11 (Pharma/Medical Devices):** - Electronic signatures, audit trails - Data integrity (ALCOA+ principles) - GxP validation (IQ/OQ/PQ documentation) - 8 FDA-regulated clients **ISO 27001, GDPR, CCPA:** - Data residency (EU, UK, US regions) - Right to erasure, data portability - Privacy by design We work with Big 4 auditors (Deloitte, PwC, EY, KPMG) and provide all documentation.

Multi-layer safety strategy: **1. Human-in-the-Loop (HITL):** - High-stakes decisions → human approval required - Example: AI suggests "Deny insurance claim" → human reviews before final decision - Confidence scores: <80% → escalate to human **2. Retrieval-Augmented Generation (RAG):** - AI answers grounded in your actual data (not hallucinated) - Vector DB retrieves relevant docs → AI cites sources - Example: "According to policy #12345, section 3.2..." **3. Output Validation:** - Structured outputs (JSON schemas, type checking) - Regex/business rule validation (e.g., phone numbers, dates) - Cross-model verification (GPT-4 + Claude both answer, compare) **4. Audit Trails:** - Log every AI decision (input, output, model, timestamp) - Compliance requirement for HIPAA, FDA 21 CFR Part 11 - Example: "Why did AI approve this loan?" → retrieve full context **5. Continuous Monitoring:** - Anomaly detection (sudden changes in AI behavior) - User feedback loops ("Was this answer helpful?") - Monthly accuracy reviews (sample 100 AI outputs, manual QA) **6. Fallback Mechanisms:** - If AI fails → graceful degradation (e.g., "I don't know, let me connect you to a human") - Never silent failures **Real Example:** Healthcare client: AI radiology assistant flags "possible lung cancer." Radiologist reviews, confirms 89% of cases (11% false positives). But 0% false negatives (no missed cancers) because AI is tuned for high recall.

Yes. Custom AI options: **1. Fine-Tuning (Best for Specific Tasks):** - OpenAI GPT-4o fine-tuning: $25/1M training tokens + $75/1M inference - Anthropic Claude (coming 2025) - Open-source (Llama-3, Mistral): Free (need GPUs) - Use cases: Domain-specific classification, custom tone/style, structured outputs - Example: Fine-tuned Llama-3-8B for legal contract analysis (95% accuracy vs 78% base model) **2. RAG (Best for Knowledge Retrieval):** - No training needed—embed your docs in vector DB - AI retrieves relevant context, generates answer - Cheaper + easier to update than fine-tuning - Example: Customer support chatbot with 10K support articles **3. Full Custom Model Training:** - Train from scratch (rare, for massive datasets + unique tasks) - Requires: 100K+ examples, GPU cluster (expensive) - Example: Tesla's Autopilot neural networks **4. Hybrid (Fine-Tuned + RAG):** - Fine-tune for task/style, RAG for knowledge - Best of both worlds - Example: Medical diagnosis AI (fine-tuned on clinical reasoning + RAG for latest research papers) **Data Privacy:** - Fine-tuning on OpenAI: Your data not used to train other models (Enterprise API) - On-premise fine-tuning: 100% data sovereignty (Llama-3, Mistral) We've fine-tuned 30+ models for clients (legal, medical, finance, engineering).

We design for vendor flexibility: **1. Abstraction Layer:** - Single interface for all AI models - Change backend without touching application code - Tools: LangChain, LlamaIndex, custom API gateway **2. Multi-Model Support:** - Deploy GPT-4 + Claude + Llama-3 simultaneously - A/B test models (which gives better results?) - Automatic failover (if OpenAI down → use Claude) **3. Prompt Portability:** - Store prompts in config files (not hardcoded) - Version control (Git) - Model-specific optimizations (GPT-4 likes XML tags, Claude likes markdown) **4. Data Portability:** - Embeddings in standard format (OpenAI 1536-dim → normalize) - Vector DB agnostic (Pinecone → Weaviate migration) **5. Cost Optimization:** - Route queries to cheapest model that meets quality bar - Example: Simple questions → GPT-3.5 ($1.50/1M tokens), complex → Claude 3.5 ($15/1M) **Real Migration Example:** Client switched from OpenAI ($12K/month) → Azure OpenAI ($9K/month, same model) + Llama-3 for non-sensitive data ($0 after GPU cost). Migration time: 2 weeks. Zero downtime. We future-proof integrations—you're never locked into one vendor.