Connect AI to your SAP, Oracle, Salesforce, PACS, MES, and legacy systems. Expert integration services for real-time AI, APIs, middleware, and enterprise modernization.
Your business problems demand more than off-the-shelf AI SaaS tools
The Pain: Your ERP (SAP, Oracle), CRM (Salesforce), databases, and custom applications were built 10-20 years ago. No REST APIs, no webhooks, no cloud connectivity. Building custom integrations from scratch takes 6-12 months and costs $150K-$500K. Every new AI tool requires another costly integration project.
The Solution: Custom AI Integration Platform with Universal Connectors. We build middleware that speaks both languages: legacy protocols (SOAP, XML-RPC, database triggers, file drops) and modern AI APIs (REST, GraphQL, gRPC). One integration layer connects ALL your AI tools to ALL your systems.
The Pain: Off-the-shelf AI SaaS products (ChatGPT Enterprise, Jasper, Copy.ai) force you to adapt YOUR processes to THEIR limitations. Can't access your proprietary data formats, business logic, approval workflows. Export-import workflows waste hours. Data leaves your security perimeter.
The Solution: Deeply Integrated AI Within Your Existing Systems. AI becomes a native feature in your applications—same UI, same authentication, same data governance. Users never leave your ERP/CRM/portal. AI reads/writes directly to your databases following YOUR business rules.
The Pain: AI needs data from 10-15 systems to be useful (CRM + ERP + support tickets + docs + emails + databases). Building ETL pipelines to centralize data costs $200K-$1M. Real-time sync is nearly impossible. Data becomes stale, AI gives outdated answers.
The Solution: Federated AI Integration—Query Data Where It Lives. AI orchestration layer queries systems in real-time via APIs, doesn't require data migration. Smart caching for performance, live data when needed. AI gets fresh data from all 15 systems without moving a byte.
The Pain: Hard-coded integrations to OpenAI GPT-4, Anthropic Claude, or specific AI APIs. When you want to switch models (cost, performance, new capabilities), you rewrite integrations. Vendor price hikes lock you in. Multi-model strategies require parallel integrations.
The Solution: Model-Agnostic Integration Architecture. Abstraction layer decouples your systems from specific AI providers. Swap GPT-4 → Claude → Llama in config file, zero code changes. Run multiple models in parallel, route requests by workload. Complete vendor flexibility.
Comprehensive technologies for seamless AI integration with any system
| Technology | Use Case | Details |
|---|---|---|
| API Gateway Pattern | Centralized entry point for all AI requests, routing, auth, rate limiting | Kong, NGINX, AWS API Gateway |
| Event-Driven Integration | Async processing via message queues (RabbitMQ, Kafka), decoupled systems | On-prem or cloud |
| Database Triggers & CDC | AI reacts to data changes in real-time (Change Data Capture) | Debezium, database-native triggers |
| File-Based Integration | Legacy systems without APIs: CSV/XML file drops, SFTP, watched folders | Custom file processors |
| Microservices Orchestration | AI as microservice, orchestrates calls to other services (Kubernetes) | Kubernetes, Docker Swarm |
| Technology | Use Case | Details |
|---|---|---|
| REST APIs (FastAPI, Express, Go) | Modern HTTP-based APIs for AI services | JSON |
| GraphQL | Flexible queries, reduce over-fetching, client-driven data selection | GraphQL |
| gRPC | High-performance binary protocol, streaming, microservices | Protobuf |
| WebSockets / Server-Sent Events | Real-time bidirectional communication, streaming AI responses | JSON/Binary |
| SOAP / XML-RPC (Legacy) | Integrate with old enterprise systems (SAP, Oracle) | XML |
| Database Connectors | Direct database access (PostgreSQL, MySQL, Oracle, SQL Server, MongoDB) | Native drivers |
| Technology | Use Case | Details |
|---|---|---|
| Custom Middleware Layer (Go/Node.js) | Lightweight, fast, tailored to your exact needs | Self-hosted |
| Apache Camel | Open-source integration framework, 300+ connectors | Self-hosted |
| MuleSoft Anypoint | Enterprise ESB, visual design, pre-built connectors | Cloud/on-prem |
| Dell Boomi | iPaaS (Integration Platform as a Service), low-code | Cloud |
| Zapier / Make (for simple workflows) | No-code automation, 5,000+ integrations | Cloud |
| Technology | Use Case | Details |
|---|---|---|
| Apache Airflow | Workflow orchestration, schedule ETL jobs, DAGs | Self-hosted or managed |
| Prefect / Dagster | Modern Python-first workflow engines, better than Airflow | Self-hosted or cloud |
| Change Data Capture (Debezium) | Real-time database change streaming to AI systems | Kafka Connect |
| Custom ETL Scripts (Python) | Tailored data extraction, transformation, loading | Cron jobs, Airflow |
| Fivetran / Airbyte (Data Replication) | Pre-built connectors for 300+ data sources | Cloud/self-hosted |
| Technology | Use Case | Details |
|---|---|---|
| Docker + Kubernetes | Containerize AI integration services, orchestrate at scale | On-prem or cloud |
| API Gateway (Kong, Tyk, AWS) | Centralize auth, rate limiting, routing, monitoring | Self-hosted or managed |
| Message Queues (RabbitMQ, Kafka, Redis) | Async job processing, decouple AI from systems | Self-hosted or managed |
| Monitoring (Prometheus, Grafana, DataDog) | Track integration health, latency, errors, throughput | Self-hosted or SaaS |
| Service Mesh (Istio, Linkerd) | Advanced routing, security, observability for microservices | Kubernetes-native |
How we solve complex integration challenges across industries
Pain Point:
Legacy SAP ERP (20 years old) manages $500M annual procurement. Purchase orders need AI review (fraud detection, compliance checks, vendor risk). SAP has no modern API. Exporting POs to CSV, manual AI review, re-importing takes 48 hours. 15% of POs have errors requiring rework.
Solution:
Real-Time SAP-AI Integration via RFC/BAPI + Custom Middleware
Recommended Stack:
SAP RFC/BAPI connector (Python pyrfc) → Custom Go middleware → AI fraud detection (fine-tuned Llama 4) → Write results back to SAP custom fields → Email alerts for high-risk POs
Deployment:
Self-hosted middleware (Docker on-prem) + AI models (2x NVIDIA L40S or cloud API fallback)
Workflow:
PO created in SAP → RFC trigger calls middleware → AI analyzes vendor, amount, line items, history → Risk score + compliance flags written to SAP → Auto-approve low-risk, route high-risk to humans
Outcome:
99% of POs processed in <30 seconds (vs 48 hours). 40% reduction in fraudulent/non-compliant POs. Zero user workflow change (everything in SAP).
Timeline: 10-12 weeks (SAP connector + middleware + AI + testing + SAP certification)
Pain Point:
Salesforce has 100K leads/month. Manual lead scoring inaccurate (20% conversion vs industry 35%). Reps waste time on low-quality leads. Email automation rules too simplistic (if/then logic). Need AI personalization reading CRM data + website behavior + past emails.
Solution:
Native Salesforce AI Integration via Apex Triggers + External AI Service
Recommended Stack:
Salesforce Apex triggers (on Lead/Contact create/update) → Webhook to external AI service (FastAPI + Llama 4 fine-tuned on company data) → AI predicts conversion probability, generates personalized email → Write back to Salesforce Lead Score field + Marketing Cloud
Deployment:
AI service on cloud VM (AWS/GCP) or on-prem → Salesforce calls via Apex HTTP callouts
Workflow:
New lead in Salesforce → Apex trigger fires → AI analyzes 20+ CRM fields + enrichment data (Clearbit) → Returns lead score 0-100 + next-best-action + personalized email draft → Salesforce auto-assigns to rep, sends email via Marketing Cloud
Outcome:
Lead conversion 20% → 38% (90% increase). Rep productivity +50% (focus on high-score leads). Personalized emails have 3.5x open rate vs generic.
Timeline: 8-10 weeks (Salesforce Apex dev + AI training + Marketing Cloud integration + A/B testing)
Pain Point:
Hospital PACS system stores 50K radiology images/month (X-rays, CT, MRI). Radiologists overloaded, 72-hour report turnaround. AI tools like Aidoc, Zebra Medical require cloud upload (HIPAA concerns, $0.50-$2/image). PACS system is on-premise, air-gapped, no internet.
Solution:
On-Premise AI Integration Directly Into PACS (DICOM Bridge)
Recommended Stack:
PACS DICOM server → Custom DICOM listener (Python pydicom) → AI radiology models (self-hosted: chest X-ray classifier, brain hemorrhage detector) → Results written back to PACS as DICOM Structured Reports → HL7 integration to EMR
Deployment:
On-premise only: AI server (4x NVIDIA A100 80GB) physically in hospital data center, air-gapped, HIPAA-compliant infrastructure
Workflow:
New DICOM image arrives in PACS → DICOM listener auto-routes to AI → AI analyzes (10-30 seconds) → Findings (nodules, fractures, hemorrhage) written back to PACS + HL7 message to EMR → Radiologist sees AI pre-read in PACS viewer
Outcome:
Zero cloud costs ($0 API fees vs $25K-$100K/year SaaS). 100% HIPAA compliance (data never leaves premises). Radiologist productivity +40% (AI pre-reads reduce report time 15 → 9 min). Critical findings flagged in real-time.
Timeline: 14-16 weeks (DICOM integration + AI model validation + HIPAA compliance + clinical testing)
Pain Point:
Proprietary trading system processes 1M market events/second. Need AI to detect patterns, predict price movements, auto-execute trades. Latency requirement: <1ms (cloud APIs 50-200ms too slow). Trading data cannot leave secure network (regulatory).
Solution:
Ultra-Low-Latency AI Co-Located with Trading System
Recommended Stack:
Trading system (C++) → Shared memory IPC → AI inference engine (NVIDIA Triton + TensorRT optimized models) → Sub-millisecond predictions → Trading system executes
Deployment:
On-premise co-location: AI inference servers (8x NVIDIA H100 SXM) physically next to trading servers, InfiniBand networking, shared memory for zero-copy data transfer
Workflow:
Market data arrives → Trading system writes to shared memory → AI reads (zero-copy), infers in 200-800 microseconds → Prediction written to shared memory → Trading system reads, executes trade in <1ms total
Outcome:
Sub-millisecond AI inference (vs 50-200ms cloud APIs). 100% data security (never leaves secure facility). AI improves trade win rate 52% → 61% (competitive advantage worth millions).
Timeline: 16-20 weeks (low-latency infra + model optimization + backtesting + regulatory approval)
Pain Point:
Custom e-commerce platform (not Shopify/Magento) has 100K SKUs, 10M sessions/day. Generic recommendation engines (Nosto, Dynamic Yield) cost $50K-$200K/year, require full catalog sync (slow). Need real-time recommendations reading live inventory, user behavior, promotions from your databases.
Solution:
Native E-Commerce AI Integration (Database-Driven Recommendations)
Recommended Stack:
E-commerce backend (Node.js/Go) → AI recommendation API (FastAPI + collaborative filtering + vector search) → Reads PostgreSQL (products, inventory, users, sessions) + Redis (real-time behavior) → Returns personalized product list → Frontend displays
Deployment:
Self-hosted AI service (Kubernetes cluster) + vector database (Qdrant for similarity search) + PostgreSQL replica for read queries
Workflow:
User browses product page → Frontend calls AI recommendation API → AI queries: user history (PostgreSQL), similar products (Qdrant), current session (Redis), inventory (PostgreSQL) → Returns 10 personalized products in <50ms → Frontend renders "You May Also Like"
Outcome:
$0 SaaS fees (vs $50K-$200K/year). Real-time accuracy (live inventory, prices, promotions). 25% increase in cart value (better recommendations). <50ms latency (vs 200-500ms external APIs).
Timeline: 10-12 weeks (AI training on historical data + API dev + vector DB + caching + A/B testing)
Pain Point:
Manufacturing Execution System (MES) tracks 500 production lines. Manual quality inspection catches only 85% of defects (15% reach customers). Equipment failures cause $2M/year downtime. MES is closed system, no APIs. Data trapped in proprietary SQL database.
Solution:
MES Database Integration + AI Vision & Predictive Models
Recommended Stack:
MES SQL database (read-only replica) → Change Data Capture (Debezium) streams to Kafka → AI services: (1) Computer vision (self-hosted YOLO for defect detection from production cameras), (2) Predictive maintenance (XGBoost models on sensor data) → Results pushed to MES via database writes + operator dashboards
Deployment:
On-premise: Kafka cluster, AI inference servers (NVIDIA GPUs for vision), PostgreSQL for AI results, custom dashboards (React + Grafana)
Workflow:
Production line camera captures product image → Stream to AI vision model → Detects defects in 100ms → If defect found, trigger MES alert + stop line. Sensor data (temperature, vibration) streams to predictive model → Predicts failure 48 hours ahead → Auto-schedule maintenance in MES.
Outcome:
Defect detection 85% → 99.2% (3.5x reduction in customer complaints). Downtime reduced 60% (predictive maintenance vs reactive). ROI: $2.5M savings/year on $180K integration.
Timeline: 14-16 weeks (MES database integration + vision AI + predictive models + line integration + testing)
Choose the right integration approach based on your requirements
| Criteria | Approach 1 | Approach 2 | Approach 3 | Approach 4 |
|---|---|---|---|---|
| System Age & Technology | REST APIs, webhooks: Direct integration | SOAP/XML-RPC: Protocol adapter middleware | No APIs (mainframe, AS/400): Database triggers, file-based, screen scraping | |
| Data Volume & Latency | <1K requests/day: Simple REST API | 1K-100K/day: API gateway + caching | >100K/day: Event-driven (Kafka, async processing) | Sub-second latency: In-process, shared memory, gRPC |
| Security & Compliance | HTTPS + API keys | OAuth2, RBAC, encryption, audit logs | HIPAA/SOC2/PCI: On-premise only, end-to-end encryption, full audit trail, access controls | |
| Data Location | Cloud-based integration, SaaS AI APIs | Some cloud, some on-prem: VPN, API gateway | Air-gapped, zero internet: 100% on-premise AI + integration | |
| Integration Complexity | 1-3 systems: Direct API calls | 3-10 systems: Orchestration layer (Apache Camel, custom middleware) | >10 systems: Full ESB (MuleSoft) or custom microservices architecture |
Proven integration solutions across industries
SAP RFC/BAPI connectors, Oracle database triggers, custom middleware
Intelligent invoice processing, fraud detection, procurement optimization, financial forecasting
Real-time AI insights in ERP UI. 80% faster processing, 40% error reduction.
DICOM listeners, HL7 v2/FHIR bridges, EMR database integration (Epic, Cerner)
Radiology AI (X-ray, CT, MRI analysis), clinical decision support, patient risk scoring, drug interaction alerts
HIPAA-compliant on-premise AI. 40% faster diagnoses, zero cloud costs, 100% data security.
Low-latency IPC (shared memory, InfiniBand), FIX protocol, core banking connectors
Algorithmic trading, fraud detection, credit risk scoring, market analysis, KYC/AML automation
Sub-millisecond AI inference. Regulatory compliant. Millions in competitive advantage.
Database-driven (PostgreSQL, MongoDB), Redis caching, REST APIs, webhooks
Personalized recommendations, dynamic pricing, inventory optimization, chatbots, review analysis
25% increase in cart value, <50ms latency, $0 SaaS fees vs $50K-$200K/year.
Database CDC (Debezium), Kafka streaming, OPC-UA protocol, custom dashboards
Computer vision quality control, predictive maintenance, production optimization, supply chain forecasting
99.2% defect detection, 60% downtime reduction, $2.5M annual savings on $180K integration.
Salesforce APIs, Zendesk webhooks, custom CRM database connectors, email server integration
AI ticket routing, sentiment analysis, automated responses, knowledge base Q&A, agent assist
70% tickets auto-resolved, 3x agent productivity, 24/7 support with 5% of headcount.
Transparent pricing for every integration complexity level
1 week
6-8 weeks
10-14 weeks
16-24 weeks
Everything you need for successful AI integration
Everything you need to know about custom AI integration
We integrate AI with virtually any system: Modern (REST APIs, webhooks), Legacy (SAP RFC/BAPI, Oracle databases, SOAP/XML-RPC), Ancient (mainframe, AS/400, proprietary protocols). Integration methods: Direct API calls (if available), Database integration (read/write to system database), File-based (CSV/XML drops, SFTP), Protocol adapters (convert SOAP to REST), Screen scraping (last resort for UI-only systems). Example: SAP ERP (20+ years old) integrated with AI via RFC connectors—AI reads purchase orders, writes fraud scores back to custom SAP fields in real-time.
Real-Time Integration (<1 second latency): Synchronous APIs (REST, gRPC), webhooks, database triggers, message queues (RabbitMQ, Kafka), shared memory (ultra-low latency <1ms for trading systems). Use cases: AI chatbots, fraud detection, quality control. Near-Real-Time (1-60 seconds): Async job queues (Celery, BullMQ), periodic polling, CDC (Change Data Capture). Use cases: Lead scoring, email automation. Batch Processing (hourly/daily): Scheduled ETL jobs (Airflow, cron), large data exports, overnight analysis. Use cases: Reporting, data warehouse sync. We recommend the right approach based on your latency needs vs cost/complexity trade-offs.
Yes, zero-disruption integration is our specialty. Methods: Read-Only Replicas: AI queries database replica, never touches production. Event Streaming: Use CDC (Debezium) to stream database changes to AI without modifying source system. API Middleware: Insert transparent middleware layer between systems—existing apps unchanged. Database Triggers: AI integration via triggers/stored procedures—application logic untouched. Gradual Rollout: Integrate one feature at a time, A/B test, rollback if issues. Example: Hospital PACS AI integration—installed DICOM listener that mirrors images to AI, writes results back. Zero changes to PACS software, radiologists see AI results in existing viewer.
Multi-layer security: Transport: End-to-end TLS 1.3 encryption for all API calls. On-premise option for air-gapped networks. Authentication: OAuth2, API keys, mutual TLS certificates. SSO integration (SAML, OIDC). Authorization: RBAC (Role-Based Access Control), field-level permissions, data masking for sensitive fields. Audit Logging: Every API call logged with user, timestamp, data accessed. Compliance: HIPAA (healthcare), SOC2 (SaaS), PCI-DSS (payments), GDPR (EU data). Secrets Management: HashiCorp Vault, AWS Secrets Manager—never hardcoded. Network Security: VPN tunnels, private VPCs, IP whitelisting, DDoS protection. Example: Financial trading integration—100% on-premise, InfiniBand isolated network, zero internet connectivity, hardware security modules (HSM) for encryption keys.
Our integration architecture is model-agnostic by design: Abstraction Layer: Your systems call our unified API, we handle routing to GPT-4, Claude, Llama, or any model. Config-Based Switching: Change AI provider in config file (YAML), zero code changes. Multi-Model Support: Run multiple models in parallel (GPT-4 for reasoning, Llama for cost, Claude for compliance), route by request type. Graceful Fallback: Primary model down? Auto-failover to backup. Cost optimization: 70% requests → cheap Llama, 30% complex → GPT-4. Vendor Flexibility: Not locked into OpenAI/Anthropic pricing or API changes. Swap providers same day if needed. Example: E-commerce client started with GPT-4 ($8K/month API fees), we switched to self-hosted Llama 4 ($0 API fees) in 2 hours via config change.
Custom Integration vs SaaS—3-Year TCO Comparison: AI Chatbot Integration: Custom ($42K integration + $5K/year hosting) = $57K over 3 years. SaaS (Intercom AI, Ada) = $36K-$120K/year = $108K-$360K over 3 years. Savings: 47-84%. ERP AI Integration (fraud detection): Custom ($95K integration + $10K/year infra) = $125K over 3 years. SaaS (niche ERP AI tool) = $100K-$300K/year = $300K-$900K over 3 years. Savings: 58-86%. Radiology AI: Custom on-premise ($95K + $15K/year GPU servers) = $140K over 3 years. SaaS (Aidoc, Zebra) = $0.50-$2/image × 50K images/month = $300K-$1.2M/year = $900K-$3.6M over 3 years. Savings: 84-96%. Break-even: Typically 6-18 months. After that, pure savings. Plus: Full control, customization, data security, vendor independence.
Yes, multi-model orchestration is common for complex workflows. Example: Customer Support Automation: (1) Sentiment Analysis: Fine-tuned DistilBERT (fast, cheap) classifies ticket urgency. (2) Intent Recognition: GPT-4 (best reasoning) determines customer intent from message. (3) Knowledge Base Search: Embedding model (Sentence-Transformers) + vector DB finds relevant articles. (4) Response Generation: Claude 3.5 (best writing) drafts personalized response. (5) Compliance Check: Custom compliance model ensures response meets regulations. All orchestrated via our middleware—ticket comes in, 5 AI models process in parallel/sequence (2-5 seconds total), human agent gets scored ticket + suggested response + relevant docs. Cost optimization: Use cheapest/fastest model for each subtask vs one expensive model for everything.
Enterprise-grade resilience built in: Error Handling: Automatic retries with exponential backoff. Circuit breaker pattern (stop calling failing service, auto-resume when healthy). Graceful Degradation: AI down? Fall back to rule-based logic or queue requests for later. System continues working (reduced functionality) vs complete outage. Monitoring & Alerts: Real-time monitoring (Prometheus + Grafana). Slack/PagerDuty alerts when errors spike, latency high, or uptime drops. Health Checks: Every service reports health status. Load balancer auto-routes traffic away from unhealthy instances. Multi-Model Failover: Primary AI model down? Auto-failover to backup model (e.g., GPT-4 → Claude → self-hosted Llama). Queue-Based Processing: Critical requests queued (RabbitMQ, Kafka), processed when service recovers. Zero data loss. SLA Guarantees: 99.9% uptime commitment (Enterprise tier). Post-Mortems: Incident analysis, root cause fixes, prevention strategies. Example: Client's OpenAI API hit rate limit—we auto-switched to Claude API in <30 seconds, zero user impact.
Let's discuss your integration requirements and build a seamless solution that enhances your existing infrastructure.
