IWConnect’s Hybrid AI Architecture for Trusted, Low-Cost Sales Prospecting at Scale
Challenge IWConnect’s sales team faced a scaling problem. Hundreds of job postings appear on LinkedIn every day, each a possible opportunity, but reviewing them by
Custom LLMs, RAG systems, and AI agents engineered for your specific workflows, not generic tools hoping to fit.
Generic AI doesn’t know your processes, terminology, or edge cases. Every answer needs correction.
Pre-built tools don’t connect to your existing systems. Data stays siloed, workflows stay manual.
You need to own the model behavior, not rent someone else’s. Your IP, your rules, your infrastructure.
Fine-tuned models that speak your industry’s language and understand your specific domain knowledge.
Your documents, your knowledge, instantly accessible. AI that answers from your actual data, not guesswork.
Autonomous workflows that handle complex multi-step tasks, make decisions, and execute across systems.
Customer-facing AI that actually resolves issues, not deflects. Trained on your support knowledge.
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Map your workflows and identify high-impact AI opportunities
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Design the technical approach, data strategy, and integration points
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Develop in sprints with continuous stakeholder feedback
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Production deployment with monitoring and ongoing refinement
Enterprise-grade document ingestion with layout-aware parsing, OCR, table extraction, and metadata structuring — turning PDFs, contracts, and forms into structured, agent-ready data.
Dockling | Unstructured | Azure Doc Intelligence | Pydantic v2
Stateful, graph-based agent orchestration with checkpointing, conditional routing, and retry logic.
Workflow automation via LLM-enhanced pipelines and custom orchestration for complex agent topologies.
LangGraph | LangChain | N8N | Pydantic v2
High-performance async APIs for agent surfaces and tool endpoints. Built on open standards for agent-tool connectivity (MCP) and multi-agent coordination (A2A).
FastAPI | MCP Protocol | A2A Protocol | gRPC
Flexible vector and relational storage. Hybrid retrieval (vector + keyword + metadata filters) over pure similarity search for enterprise RAG.
Chunking strategies tuned per document type.
pgvector | Qdrant | Weaviate | Redis | Milvus
We support both containerised (Kubernetes) and cloud-native managed deployments: Azure Functions, App Service, and AI Foundry; AWS Lambda, ECS, and Bedrock; GCP Cloud Run and Vertex AI. The right model is chosen per workload — Kubernetes for portability, managed services for speed and cost efficiency.
Kubernetes | Azure Functions | App Service | Foundry | AWS Lambda | ECS | Bedrock | GCP Cloud Run | Vertex AI | Docker
Challenge IWConnect’s sales team faced a scaling problem. Hundreds of job postings appear on LinkedIn every day, each a possible opportunity, but reviewing them by
Challenge A European company ran its operations on roughly 50 Delphi applications built over 20 years. The original designers were long gone, the business logic
See how a US fintech cut FTP incident triage from 60+ minutes to under 60 seconds with n8n, Claude, and 9 modular sub-workflows.
Challenge A global Medical Affairs consulting partner needed answers from complex, multi-source datasets spanning CRM records, field interactions, and qualitative feedback. But even simple questions
Automation rate in banking exception handling
Down from 2 hours for document processing
30-minute technical discovery call. We’ll assess feasibility and outline the path forward.
Practical answers about custom LLM applications, RAG systems, AI agents, intelligent chatbots, enterprise integrations, governance, and production AI deployment.
Agentic AI development is the design and engineering of AI systems that can reason through tasks, use approved tools, retrieve context, call APIs, follow workflows, and support or automate business actions. IWConnect builds agentic AI solutions around enterprise workflows, data, integrations, governance, and production requirements.
A chatbot usually answers questions or follows a predefined conversation flow. Agentic AI can go further by planning steps, using tools, retrieving business context, calling APIs, interacting with enterprise systems, and completing workflow actions under defined controls.
IWConnect can build AI agents for document processing, customer support, internal knowledge access, exception handling, incident triage, workflow automation, service desk assistance, reporting, compliance support, sales prospecting, and other enterprise workflows.
Yes. IWConnect builds RAG systems that connect language models to internal documents, structured data, knowledge bases, metadata, and enterprise data sources using document processing, retrieval pipelines, vector search, grounding, and governance controls.
IWConnect connects AI agents to enterprise systems through approved APIs, integration platforms, MCP servers, workflow orchestration, data pipelines, authentication controls, permissioned tools, and monitored execution paths.
Custom AI agents are controlled through role-based access, tool permissions, approval flows, prompt and response monitoring, logging, data protection, retrieval constraints, API governance, cost controls, and production observability.
IWConnect moves from prototype to production through discovery, architecture, iterative build, testing, integration, governance, deployment, monitoring, and optimization so the AI solution is usable, secure, observable, and supportable in real enterprise environments.
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