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AI Systems Engineering

Agent Systems · RAG · MCP · Fine-Tuning

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AI Systems Engineering illustration

The production AI wall is real: your agent fails unpredictably after step 3, your RAG system hallucinates at edge cases, your fine-tuned model lost general reasoning. The failure is engineering — agent state management, retrieval architecture, fine-tuning methodology — not model selection.

Obox Systems is an international software engineering company headquartered in the US, specializing in production AI systems engineering. Engagements are led by our CTO, backed by a structured team with mandatory peer review; NDA available on request.

Proven: 40–60% of repetitive manual work eliminated across client workflows; a repeat client with 11+ projects delivered across data enrichment, transcription, and lead intelligence.

We can help you with:

  • AI agent systems — LangGraph state machines, multi-agent orchestration, structured tool interfaces, persistent memory, and failure recovery logic. Agents that execute reliably beyond three steps.
  • MCP server development — standardized Model Context Protocol interfaces for your APIs, databases, and internal tools, so Claude Desktop, Cursor, or your own agents can use them securely — with explicit control over which actions AI is allowed to take.
  • RAG systems — hybrid retrieval (dense and sparse), reranking, chunking matched to document type, metadata filtering, and evaluation pipelines built to hold accuracy on real enterprise documents.
  • LLM fine-tuning — LoRA/​QLoRA on Llama, Mistral, Gemma, or Phi, instruction dataset construction, and DPO alignment, validated against domain and general reasoning benchmarks.
  • Context engineering — structured outputs, semantic caching, model routing, and inference cost optimization.

n8n moves data. It cannot make a model accurate on your documents.

The Diagnostic Architecture Audit pinpoints where your AI system fails — or how to build a new one — in five days: a written plan you own.

Technologies we use

  • LangGraph
  • LangChain
  • OpenAI
  • Anthropic
  • HuggingFace
  • Pinecone
  • Python
  • Rust

Packages

Diagnostic Architecture Audit

An audit of your current AI system or a blueprint for a new one — where agents or retrieval fail, data readiness, architecture, evaluation approach, inference cost, and a technical roadmap

5 days

Rapid Technical Spike / PoC

One RAG pipeline, agent workflow, or MCP integration running on your real data, measured against one agreed success metric — with an evaluation report and a build-or-stop recommendation

10–15 days

Dedicated Engineering Squad

A dedicated Obox engineering team that runs and evolves your AI system — accuracy, cost, new use cases, and stability, with all code and IP staying yours

Monthly retainer

Core Modernization Sprint

A hallucinating proof of concept or legacy automation rebuilt in phases to hold accuracy — retrieval, agents, MCP integrations, fine-tuning where needed, and monitoring

Milestone-based

Case Studies

AI Bot Optimization & WebSocket Transition project screenshot
Rust DevelopmentLow Latency Systems

AI Bot Optimization & WebSocket Transition

United Kingdom2 weeks

Re-architected a document-explaining AI bot to a WebSocket-first design with real-time streaming and PostgreSQL-backed context retrieval, eliminating wait times and delivering faster, more accurate, more reliable answers.

FAQ

  • PoCs work in controlled demos. Production is messier: multi-column PDFs, footnotes, niche vocabulary, 7-step workflows, and failing tool calls. The wall isn’t the model — it’s the engineering around it: agent state management, retrieval architecture, and fine-tuning. We build through it.

  • RAG usually fails at retrieval, not generation. Naive chunking splits context; cosine similarity returns adjacent but wrong chunks; single-vector search misses exact matches. We fix it with hybrid retrieval, reranking, document-specific chunking, and retrieval evals before deployment.

  • Sometimes n8n is the right data pipe: webhooks, calendar triggers, moving records. We use reliable tools there and don’t reinvent them. But n8n isn’t the AI layer. Complex agents, RAG, and fine-tuning need state, retrieval, and model engineering — that’s where we fit.

  • Yes — and we prefer it. Your ML team is strong on model training and research; we fill the production engineering layer: agent state management, retrieval architecture, inference optimization, deployment infrastructure. We augment your team, not replace it.

  • Off-the-shelf RAG products like Cohere, Vertex AI Search, and OpenAI Assistants handle standard chatbot use cases well. If a managed template fits, we’ll say so in the CTO Consultation. We’re the right fit when you need agent architecture, custom retrieval, or domain fine-tuning.

  • Usually, yes. RAG hallucination is an engineering problem with diagnostics: wrong chunks, missing reranker, poor chunk strategy, or model output beyond context. We identify the failing layer and only rebuild if the architecture is fundamentally wrong — clarified in Discovery.

  • We build. The Diagnostic Architecture Audit maps your failure mode, the Spike proves the approach on your real data, and the Modernization Sprint delivers the production system. No strategy workshops or slide decks — every engagement ends in working code or a clear decision.

  • Yes. We sign NDAs before any technical discussion begins, on request.

  • You do. IP, source code, architecture docs, and deployment runbooks fully assigned on completion. No vendor lock-in, no proprietary platform dependency.

  • Yes. Signed contracts with fixed scope per phase. Wyoming LLC — you’re contracting with a registered entity, not an individual.

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Consult with our engineering team to define the perfect solution for your needs.

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30 minutes