AI DEVELOPMENTAI Development
Implementing Cutting-Edge Technology into Real Business
SERVICE OVERVIEWService Overview
Implementation of cutting-edge AI technology in partnership with Matsuo Lab. From multi-agent systems to generative AI and enterprise platforms, we provide full technical support for corporate AI adoption. Our world-class team of GAFAM and AI lab alumni delivers end-to-end support — from LangGraph-powered multi-agent systems, industry-specific LLM fine-tuning, RAG-based internal knowledge utilization, sprint-based PoC, to ISO/IEC 42001-compliant AIOps infrastructure.
Multi-Agent AI SystemsMulti-AI Agent System Development
AI evolution is moving from standalone models to the era of multi-agent systems where multiple AIs collaborate. Rather than conventional RAG + vector DB approaches, we leverage Agentic Search (Tool-Calling Search) — as adopted by Claude Code — and Context Engineering as the foundation for next-generation architecture, dramatically improving agent search accuracy and autonomy. We design and build enterprise-grade multi-agent systems powered by LangGraph, achieving 85% business process automation and 3.5x task processing speed improvement.
Are you facing these challenges?
- ✓"A single AI model cannot handle our complex business workflows"
- ✓"We don't know how to coordinate AIs specialized for different processes"
- ✓"We lack the engineering capability to integrate multiple AI systems"
- ✓"How do we balance scalability with governance?"
Solutions from Storyteller AI
Where vibe coding falls short for large-scale development, we deliver through Spec-Driven development — built on best practices from Silicon Valley GAFAM and AI lab engineers experienced in large-scale service design. By clearly defining specifications and enforcing type safety with test automation, we bring order to multi-AI agent systems. From LangGraph-powered enterprise-grade multi-agent design to orchestration and scalable infrastructure, we provide end-to-end support to build organizational AI systems that automate complex business workflows.
Agentic Search × Agent Design
Built on Agentic Search (Agentic Retrieval) as adopted by Claude Code, agents autonomously determine search strategies. File-Based Retrieval and Context Engineering — not vector search — dramatically improve search accuracy. LangGraph State Management explicitly manages inter-agent state, enabling autonomous execution of complex business processes.
Orchestration
Automated task decomposition and assignment via LangChain/LangGraph. Graph structures express complex workflows, optimizing agent execution order, parallel processing, and conditional branching. Type Safety guarantees inter-agent contracts, building transparent, debuggable systems.
Scalable Infrastructure
Microservice architecture enables easy agent addition. Cloud-native design delivers auto-scaling based on demand. ISO/IEC 42001:2023-compliant governance ensures enterprise-grade reliability and security.
Results
Built a multi-agent system for a manufacturing DX division. Market research, data analysis, and report generation agents collaborated, achieving 85% business process automation and 3.5x task processing speed improvement.
Case Studies
Challenge
Market research takes 3 weeks, report creation 1 week. Slow decision-making falling behind competitors.
Solution
Three agents for web collection, data analysis, and report generation collaborate. Time reduced from 3 weeks to 2 hours.
Result
85% process automation, 3.5x speed improvement, 90% manual work reduction
Challenge
Credit assessment process is complex and time-consuming. Dependent on assessor experience with inconsistent criteria.
Solution
Automated data collection, risk assessment, and report generation with multiple agents. Unified criteria for faster assessments.
Result
70% shorter assessment period, unified criteria, 20% reduction in non-performing loans
Challenge
Customer support inquiry handling consumes excessive resources. Many complex questions beyond FAQ coverage.
Solution
Multi-agent auto-response for knowledge search, answer generation, and escalation decisions. Seamless human support integration.
Result
60% response time reduction, 15% customer satisfaction improvement, 50% support workload reduction
Challenge
Patient data collection, diagnostic support, and report creation are fragmented. Heavy burden on physicians with insufficient data coordination.
Solution
Three agents for data integration, diagnostic support, and report generation collaborate. FHIR-compliant secure data coordination.
Result
30% shorter consultation time, 50% reduction in physician administrative work, improved diagnostic accuracy
Generative AI & LLM SolutionsGenerative AI & LLM Solutions
Since the advent of ChatGPT, generative AI has dramatically improved corporate creativity and productivity. Innovation is happening across every business domain — document creation, code generation, image generation, and more. Through industry-specific fine-tuning, advanced prompt optimization, and multimodal support, we deliver 92% answer accuracy improvement and 60% content creation time reduction.
Are you facing these challenges?
- ✓"General-purpose models don't understand our industry terminology and context"
- ✓"Hallucinations generate incorrect information"
- ✓"We lack internal prompt engineering expertise"
- ✓"We don't know how to effectively use different AI models"
Solutions from Storyteller AI
We integrate the latest LLMs (GPT, Claude, Gemini) with industry-specific fine-tuning to learn your proprietary knowledge. Advanced prompt engineering techniques like Chain-of-Thought improve accuracy, while multimodal AI integrating text, image, and audio supports all content generation needs.
Custom LLM Development
Industry-specific fine-tuning and optimization. Additional training on your documents, FAQs, and manuals to build models that accurately understand domain terminology and industry context. Cost-effective approaches using LoRA, QLoRA, and other efficient techniques for high-accuracy custom LLMs.
Prompt Optimization
Advanced techniques including Chain-of-Thought, Few-shot Learning, and Self-Consistency for accuracy improvement. Systematized prompt template libraries for task-specific optimal prompts. Continuous improvement through A/B testing. Minimized hallucinations for reliable responses.
Multimodal Support
Building generative AI that integrates text, image, and audio. From documents to images, audio to automated meeting minutes, images to descriptions — handling diverse content generation tasks. Leveraging the latest models like Gemini and GPT Vision to expand business possibilities.
Results
Built a custom LLM for a legal department. Automated contract review achieved 60% review time reduction and 92% legal check accuracy improvement. Significantly improved domain terminology comprehension, reducing attorney workload.
Case Studies
Challenge
Contract review takes too long. General LLMs don't understand legal terminology, leading to incorrect assessments.
Solution
Built a custom LLM fine-tuned on legal documents. Automated contract review and risk detection.
Result
60% review time reduction, 92% accuracy improvement, 85% terminology comprehension improvement
Challenge
FAQ response generation lacks product-specific information. Providing incorrect information to customers causing complaints.
Solution
Fine-tuned on product manuals, FAQs, and past inquiries. Built an LLM with deep product knowledge.
Result
90% answer accuracy improvement, 20% customer satisfaction improvement, 70% complaint reduction
Challenge
Blog posts and social media content creation is resource-intensive. Maintaining consistent brand tone is difficult.
Solution
Fine-tuned on past articles to learn brand tone. Prompt templates ensure consistent quality.
Result
70% content creation time reduction, 3x posting frequency, 25% engagement rate improvement
Challenge
Product specifications and manual creation require enormous resources. Accurate expression of specialized technical terminology is challenging.
Solution
Fine-tuned on past technical documents. Auto-generation with an LLM trained on technical terminology and industry-standard formats.
Result
65% manual creation time reduction, 95% terminology accuracy improvement, 3x update frequency
Post-RAG: Agentic Search & Deep ResearchNext-Gen Search Infrastructure (Agentic RAG)
Conventional RAG + vector DB suffers from structural limitations: context loss from chunking, search accuracy ceilings, and ballooning operational costs. We are the first in Japan to commercially implement the Post-RAG architecture adopted by Claude Code (Anthropic) and OpenAI Codex — Agentic Search × Agentic Retrieval × Context Engineering. Through ontology-based data systematization and Deep Research Agent orchestration, we achieve 95% search accuracy improvement and 70% inquiry response time reduction.
Are you facing these challenges?
- ✓"We ran a RAG PoC, but vector search accuracy is low and we can't retrieve needed information"
- ✓"Dirty data was vectorized without preprocessing, causing AI to return incorrect information"
- ✓"Conventional RAG has structural flaws in information retrieval and can't handle complex queries"
- ✓"Internal documents are scattered and AI agents lack sufficient context capacity to grasp the full picture"
Solutions from Storyteller AI
We fundamentally solve conventional RAG + vector DB structural flaws through Agentic Search (Tool-Calling Search) and Deep Research Agent orchestration. File-Based Retrieval eliminates context loss from chunking, and Context Engineering optimizes search result injection into LLMs. Ontology (OWL/RDF) data systematization and medallion architecture ensure data quality, building the AI agent infrastructure for the Post-RAG era.
Agentic Search × File-Based Retrieval
Enterprise implementation of the Post-RAG architecture adopted by Claude Code (Anthropic). Agents autonomously determine search strategies using Tool-Calling Search — Glob (file pattern search), Grep (content search), Read (direct access) — for pinpoint information retrieval. Fundamentally eliminates vector DB chunking and semantic drift issues, building an Agentic Retrieval infrastructure with direct access to the latest data.
Deep Research Agents × Context Engineering
Building autonomous Deep Research capabilities equivalent to Google Deep Research and Perplexity Pro for internal use. Multiple AI agents (collection, analysis, synthesis, verification) collaborate via LangGraph for multi-step reasoning, recursive search, and self-verification. Context Engineering filters, prioritizes, and optimizes tokens from search results, enabling advanced Agentic Retrieval impossible with conventional RAG.
Ontology × Medallion Architecture
Systematically organizing scattered documents with ontology (OWL/RDF) to build a unified data catalog enabling AI agents to correctly understand business concepts. Bronze → Silver → Gold three-tier medallion architecture progressively improves dirty data quality. Agentic Search accesses correct data via the shortest path while conserving context, deriving business insights that directly impact ROI.
Results
Built a RAG system for a pharmaceutical R&D division. Integrated papers, patents, and internal reports, achieving 70% reduction in researcher information search time and 95% search accuracy improvement. Significantly accelerated drug development speed.
Case Studies
Challenge
Papers, patents, and internal reports are scattered. Researchers spend excessive time on information retrieval, delaying R&D.
Solution
Vectorized all documents with semantic search for unified knowledge. RAG enables instant reflection of latest information.
Result
70% search time reduction, 95% accuracy improvement, 30% shorter development cycles
Challenge
FAQs are outdated and don't cover latest product information. Incorrect responses increase customer complaints.
Solution
RAG integrates manuals, FAQs, and past inquiries. Always references latest information for accurate responses.
Result
60% response time reduction, 90% accuracy improvement, 25% customer satisfaction improvement
Challenge
Finding past contracts and precedents is time-consuming. Low search accuracy for similar cases leads to judgment errors.
Solution
High-accuracy semantic search for similar contracts and precedents. RAG supports attorney decision-making.
Result
80% search time reduction, 95% similar case search accuracy, 70% fewer judgment errors
Challenge
Searching through extensive manuals for equipment troubleshooting. Past failure cases underutilized, leading to slow recovery.
Solution
Ontology-based systematization of equipment and failure patterns. RAG instantly retrieves similar trouble cases with solutions.
Result
65% faster troubleshooting, 85% first-resolution rate improvement, 50% downtime reduction
PoC & R&D SupportPoC & R&D Support
With the rapid evolution of AI technology, the speed of experimentation and learning has become the source of competitive advantage. In the AI agent industry where paradigm shifts occur every six months, we consistently stay 6 months ahead of domestic competitors by applying cutting-edge technology (Agentic Search, Context Engineering, Post-RAG architecture, etc.) from the PoC stage. Sprint-based PoC in 2-4 weeks enables rapid value validation, achieving an 80% PoC-to-production transition rate.
Are you facing these challenges?
- ✓"We want to verify effectiveness before making a large-scale AI investment"
- ✓"We're unsure about PoC design methodology and whether our validation is effective"
- ✓"Our PoC keeps dragging on and never transitions to production"
- ✓"We can't keep up with the latest technology, falling behind competitors"
Solutions from Storyteller AI
Our GAFAM and AI lab alumni team pre-evaluates technical feasibility of cutting-edge technologies. Sprint-based PoC in 2-4 weeks enables rapid value validation while minimizing failure risk. We provide a clear roadmap from PoC to production deployment, supporting AI adoption that delivers guaranteed results.
Sprint-Based PoC
Value validation in 2-4 week sprints. Hypothesis testing with minimum viable features and impact measurement. Quick Win approach generates early results to earn executive trust. Gradual expansion minimizes risk while advancing AI adoption.
Technical Feasibility
Pre-evaluation of feasibility and cost. In partnership with Matsuo Lab, we assess applicability of latest AI technologies. Comprehensive evaluation of data quality, compute resources, accuracy requirements, and operational maintenance to develop high-probability PoC plans.
Scale Planning
Clear roadmap from PoC to production deployment. End-to-end support from architecture design, data infrastructure, AIOps setup, to organizational structure. PoC-validated technology scaled to enterprise grade for company-wide deployment.
Results
Conducted a demand forecasting AI PoC for a retailer in 3 weeks. After confirming 85% prediction accuracy improvement, expanded to all stores. Achieved 80% PoC-to-production transition rate and 30% inventory cost reduction. Rapid validation cycles outpaced competitors.
Case Studies
Challenge
Interested in AI demand forecasting but uncertain about effectiveness. Want to validate with PoC before large investment.
Solution
Built a demand forecasting model in a 3-week sprint PoC. After confirming 85% accuracy improvement, decided on full deployment.
Result
Successful PoC, full deployment in 3 months, 30% inventory cost reduction
Challenge
Want to automate defect detection with image recognition but concerned about accuracy. Want to validate with PoC.
Solution
Built an image recognition model in a 2-week PoC. Achieved 95% detection accuracy, decided on production deployment.
Result
Successful PoC, all-factory deployment in 4 months, 70% inspection workload reduction
Challenge
Want to improve retention with churn prediction but lack model-building expertise. Want to confirm feasibility with PoC.
Solution
Built a churn prediction model in a 4-week PoC. Achieved 80% prediction accuracy, applied to retention strategies.
Result
Successful PoC, 25% churn rate reduction, 40% LTV improvement
Challenge
Want to improve fraud detection accuracy but uncertain about AI effectiveness. Want to validate with PoC first.
Solution
Built an anomaly detection model in a 3-week PoC. Achieved 90% detection rate with under 5% false positives, decided on production deployment.
Result
Successful PoC, all-channel deployment in 6 months, 70% fraud loss reduction
Enterprise AI PlatformEnterprise AI Platform
Enterprise AI adoption requires transitioning from experimental initiatives to company-wide deployment. An integrated platform with scalability, security, and governance is the key to AI democratization. Through AIOps infrastructure, unified governance, and elastic infrastructure, we achieve 50% infrastructure cost reduction and 10x AI system deployment speed improvement.
Are you facing these challenges?
- ✓"Each department adopts AI independently. No governance over multi-agent, LLM, RAG systems"
- ✓"No version management or quality control framework for AI systems"
- ✓"Unable to assess security and compliance risks"
- ✓"Afraid of cost explosion when scaling up"
Solutions from Storyteller AI
ISO/IEC 42001:2023-compliant AIOps infrastructure automates the entire lifecycle of AI systems (Agentic RAG, multi-agent, LLM). Integrating next-gen search infrastructure built on Agentic Search × Context Engineering with TEE cryptographic infrastructure for AI deception resistance. Unified governance establishes company-wide AI usage policies and audit frameworks for enterprise-grade AI adoption.
AIOps Infrastructure
Automating the entire lifecycle of AI systems (multi-agent, LLM, RAG, ML models). Building pipelines for experiment management, version registry, auto-deployment, A/B testing, monitoring, and auto-updates. CI/CD integration enables AI engineers and data scientists to focus on development.
Unified Governance
Establishing company-wide AI usage policies and audit frameworks. ISO/IEC 42001:2023-compliant responsible AI development. Guaranteeing model transparency, explainability, fairness, and security with full regulatory compliance. AI risk management framework supports safe AI adoption.
Elastic Infrastructure
Building auto-scaling infrastructure based on demand. Kubernetes + Cloud Run/ECS handles traffic fluctuations. Cost optimization algorithms ensure only necessary resources are used, eliminating waste. Spot instance utilization reduces infrastructure costs by 50%.
TEE Cryptographic Infrastructure × AI Deception Resistance
TEE (Confidential Computing) cryptographic infrastructure on Google Cloud delivers a "zero-trust" architecture where neither cloud vendors, OS administrators, nor operators can read raw data. Next-generation cryptographic technologies including FHE (Fully Homomorphic Encryption) enable AI inference on encrypted data. Structurally prevents AI deception (hallucination, tampering, data leakage), building next-generation data clean rooms for confidential cross-company data federation analysis.
Results
Built an enterprise AI platform for a financial institution. Unified governance for all department AI usage, achieving 10x AI system deployment speed with AIOps and 50% infrastructure cost reduction. Realized company-wide AI democratization.
Case Studies
Challenge
Each department adopts AI independently. No governance, security risks materializing. No AI system version management.
Solution
AIOps infrastructure and unified governance for centralized AI management. ISO/IEC 42001 compliance ensures safety.
Result
50% infrastructure cost reduction, 10x deployment speed, zero security risks
Challenge
AI system deployment takes too long. Too much manual work causing human errors. Scaling up is difficult.
Solution
CI/CD-integrated AIOps pipeline. Auto-deployment, A/B testing, and monitoring automate operations.
Result
90% shorter deployment time, zero human errors, scalability achieved
Challenge
Peak traffic surges cause AI model response delays. Degraded customer experience and lost sales opportunities.
Solution
Elastic infrastructure for auto-scaling. Resource optimization based on demand maintains consistently fast responses.
Result
Stabilized response times, 15% peak sales improvement, 40% infrastructure cost reduction
Challenge
Want to deploy AI across 500 stores nationwide but AI systems are fragmented. Unified management and governance impossible.
Solution
Enterprise AI platform for centralized management of all store AI systems. Unified governance ensures security and compliance.
Result
Full store integration complete, 55% operational cost reduction, 8x AI deployment speed improvement