We build companies
where AI works

From LLM-based AI Agent design to RAG pipelines,
multi-agent workflow automation, and prompt engineering.
We architect and operate AI systems tailored to your business.

LangGraph Claude API OpenAI RAG MCP Vector DB n8n

AI adoption isn't about choosing a model.
It's about designing
which tasks become agents,
what context to inject,
and how to evaluate outcomes.
Acres builds production-grade AI systems.

Services
End-to-End AI Transformation
01

AI Agent Architecture Design

Agentic AI LangGraph Tool Use MCP

We map your business logic to single or multi-agent architectures. Built on LangGraph state machines with task routing between agents, Tool Use for external system integration, and MCP for shared context — all production-grade.

Use cases: Contract review agent, CS intake classification-response-escalation pipeline, automated recurring report generation
# Multi-Agent Orchestration from langgraph.graph import StateGraph workflow = StateGraph(AgentState) workflow.add_node("classifier", classify_intent) workflow.add_node("retriever", rag_retrieve) workflow.add_node("executor", execute_action) workflow.add_node("reviewer", human_in_loop) workflow.add_conditional_edges( "classifier", route_by_confidence, {"high": "executor", "low": "reviewer"} )
02

RAG Pipeline & Knowledge Systems

RAG GraphRAG Embedding Reranking

We transform internal documents, manuals, and databases into AI-ready knowledge systems. Semantic Chunking, Hybrid Search (BM25 + Vector), and Cross-encoder Reranking to minimize hallucinations, with evaluation pipelines to measure answer quality against hard metrics.

Tech stack: Pinecone / Weaviate / pgvector, OpenAI Embeddings, Cohere Rerank, RAGAS-based automated evaluation
# RAG Pipeline with Reranking chunks = semantic_chunker.split(docs) vectors = embed_model.encode(chunks) vectorstore.upsert(vectors) # Hybrid Search + Rerank candidates = hybrid_search( query, bm25_k=20, vector_k=20 ) ranked = reranker.rank( query, candidates, top_n=5 ) answer = llm.generate(query, ranked) # RAGAS Eval faithfulness: 0.94 relevancy: 0.91
03

Workflow Automation

n8n API Integration Webhook

We wire AI into your existing systems — ERP, CRM, messengers, email — through automation workflows. Orchestration platforms like n8n and Make, combined with LLM nodes for trigger-based automation: webhooks, schedulers, conditional branching, all production-ready.

Use cases: Inbound lead auto-classification & CRM entry, scheduled report generation & Slack distribution, auto-reply & ticket creation
# Trigger-based Automation trigger: webhook("/inbound") │ ├─ classify(intent) # LLM node │ ├─ sales → crm.create(lead) │ ├─ support → ticket.open() │ └─ general → auto_reply() │ ├─ summarize(context) └─ slack.post("#ops") → avg response: 47s (was 4.2h)
04

AI Enablement & Governance

Prompt Engineering AI Governance Red Teaming

We design training programs so your team can operate and improve AI systems on their own. Prompt engineering workshops, Few-shot / Chain-of-Thought pattern design, governance frameworks for Korea's AI Basic Act (effective Jan 2026), and red teaming for prompt injection defense.

Programs: AI literacy workshop for staff (4-8h), LLM app dev bootcamp for engineers (2-5d), AI strategy session for executives (2h)
# Governance Config governance: data_classification: internal_only model_access: role_based_acl prompt_guard: - injection_detection: enabled - pii_redaction: auto - output_filter: toxicity_check compliance: ai_basic_act_2026: aligned audit: full_trace_logging
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