AI System
Architecture
We don't build AI experiments or proof-of-concepts that never reach production. We construct autonomous agents and data pipelines that connect directly to your existing databases, analyze real business processes, and execute actions without manual intervention. The difference between AI that saves meaningful time and AI that impresses in demos lies entirely in the architecture — and that's where we focus.
AI and data architecture is the design of systems that use machine learning models, data pipelines, and AI agents to automate business processes. EKLOMA builds RAG (retrieval-augmented generation) agents, integrates LLMs such as GPT-4 and Claude, and designs vector database infrastructure using tools like Pinecone and pgvector. We connect AI directly to your existing databases and APIs — not isolated demos. Typical results include 80–90% reductions in manual classification work and sub-second query response times on knowledge bases of 100,000+ documents. We have implemented AI automation for document processing, internal copilots, and data extraction workflows across insurance, logistics, and e-commerce industries.
Process
/// EXECUTION_FLOWAnalysis & Data
INITWe start by understanding your actual business processes and data landscape — not by asking what AI features you want. We collect and profile historical data, identify bottlenecks where human judgment or repetitive manual work is creating a constraint, and evaluate data quality for model training or RAG indexing. This phase determines whether an LLM integration, a fine-tuned model, or a structured rules engine is the right tool for your specific use case — and prevents building AI on a foundation that can't support it.
Architecture Design
DESIGNWe design the full AI system architecture: LLM agent structure and orchestration logic, model selection (GPT-4o, Claude 3.5 Sonnet, Llama 3, or domain-specific models), vector database integration for RAG pipelines (Pinecone, Qdrant, or pgvector), prompt engineering and context management strategy, and fallback and error handling behavior. We document every architectural decision with reasoning so your team understands the system, not just the outputs.
Integration & Testing
BUILDWe connect the AI system to your existing ERP, CRM, or internal tools via API — with authentication, rate limiting, and data validation at every boundary. Testing goes beyond unit tests: we perform Red-teaming (adversarial prompt injection, jailbreaking attempts), hallucination rate measurement against a golden dataset, latency profiling under realistic concurrent load, and regression testing to ensure model updates don't degrade task performance. Every integration is tested end-to-end before reaching production users.
Deployment
LIVEThe system is deployed to production with a staged rollout — typically starting with 5-10% of traffic while monitoring quality metrics before expanding to full rollout. We configure real-time observability using LangSmith, Arize, or custom dashboards to track token usage, response quality scores, error rates, and business impact metrics. Automated retraining or RAG index refresh pipelines keep the system current as your data changes. We stay involved post-launch to tune performance based on real usage patterns.
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Ready for Integration?
We start with a free technical audit. We will assess your data readiness for AI systems.