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EPIOSO
Applied Intelligence

AI Engineering & Intelligent Systems

We build production AI systems designed for actual enterprise workflows, not fragile demos. From high-throughput vector retrieval and cognitive agent orchestration to custom model fine-tuning and inference pipelines, we engineer reliable intelligence directly into your core product architecture.

AI Engineering & Intelligent Systems

Building interfaces that people love to use.

Strategic AI Architecture & Integration

AI engineering at EPIOSO is driven by verifiable performance, not speculative experiments. We transform frontier foundation models and custom architectures into resilient production systems: low-latency agent loops, zero-hallucination knowledge retrieval, autonomous document pipelines, and clinical-grade decision support.

Core Deliverables & Capabilities

  • Production RAG & Knowledge Retrieval

    Hybrid search architectures, dynamic reranking, and semantic vector stores engineered for deterministic accuracy and zero hallucination over massive proprietary datasets.

  • Autonomous Cognitive Agents

    Multi-agent orchestration frameworks equipped with state persistence, structured function calling, and strict human-in-the-loop controls for complex operations.

  • Custom Model Alignment & Inference

    Domain-specific fine-tuning (LoRA/QLoRA), specialized embeddings, and optimized inference serving built for predictable throughput and low latency.

AI Engineering Methodology

Domain Framing & Model Selection

/001

We formalize system boundaries, evaluation benchmarks, and latency targets before selecting proprietary models or open weights.

Vector Architecture & Context Design

/002

We structure semantic chunking strategies, dense-sparse embedding pipelines, and deterministic metadata filters for hybrid retrieval.

Agent Orchestration & Deterministic Controls

/003

We implement function calling contracts, state machines, structured JSON outputs, and strict guardrails to prevent hallucinations.

Benchmarking, Evals & Hardening

/004

We stress-test pipelines against synthetic failure datasets, optimize token economics, and establish automated LLM-as-a-judge evals.

AI Engineering Methodology
Optimized End-to-End Inference Latency
<1.2s
Tolerance for Unvalidated Hallucinations
Zero
Autonomous Agents with Deterministic Safeguards
Production

Ready to start?

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What's your business called?

Prefer direct?

LinkedIn

Oussama Zaki

GitHub

gloxoss

Based in

Casablanca, Morocco

We reply within 24 hours.