Business-First LLM Strategy
An LLM implementation should solve a measurable business problem. We begin by understanding your workflows, users, data, systems and desired outcomes before selecting the appropriate model and architecture.
Large Language Model (LLM) Development Services
概要
Build intelligent applications that understand language, retrieve knowledge, generate useful responses and automate complex workflows with Large Language Models. Obrive designs and develops LLM-powered solutions for businesses looking to integrate generative AI into products, platforms and enterprise workflows.
From LLM application development and RAG systems to AI assistants, intelligent search, document intelligence and enterprise automation, we build practical solutions around real business requirements. We combine AI strategy, software engineering, data architecture, model integration, retrieval systems and user experience to turn large language models into reliable business applications.
Build LLM Applications Around Your Data
Obrive designs retrieval and integration architectures that connect LLM applications with relevant business information while keeping the experience grounded in the data your organization controls.
Intelligent, Context-Aware Experiences
We build applications that can understand context across conversations, documents, workflows and business systems. This can include conversational assistants, enterprise search, document analysis, and workflow automation.
LLM Engineering Beyond the Model
An effective LLM product requires application architecture, data pipelines, retrieval, prompts, evaluation, security, monitoring, observability and a user experience designed around the task.
Reliable AI for Real Business Workflows
Enterprise AI needs measurable performance. We design evaluation and monitoring approaches around response quality, groundedness, latency, cost, safety and task completion.
Our LLM Services
Our LLM capabilities cover strategy, application development, RAG, AI agents, model integration, fine-tuning, prompt engineering, enterprise knowledge systems, document intelligence, AI search, automation, evaluation and production deployment.
LLM Strategy & Consulting
Every successful LLM project starts with the right problem, architecture and success criteria. We turn business requirements into a practical LLM adoption strategy.
AI use-case discovery
LLM readiness assessment
Model selection
Architecture planning
Data strategy
Cost and latency planning
Security and privacy considerations
Build-versus-buy analysis
Implementation roadmap
LLM Application Development
We develop custom applications that use language models to deliver intelligent user and employee experiences, integrating LLM capabilities into web, mobile and enterprise applications.
AIアシスタント
Conversational applications
Enterprise copilots
Intelligent search
Content generation platforms
Knowledge assistants
Customer support AI
Internal productivity tools
AI-powered SaaS features
Retrieval-Augmented Generation (RAG)
RAG enables an LLM application to retrieve relevant information from approved knowledge sources before generating a response. We design RAG architectures for grounded, context-aware enterprise applications.
Document ingestion
Chunking strategies
Embeddings
Vector search
Hybrid search
Metadata filtering
ナレッジベース
Source attribution
Retrieval evaluation
Context optimization
AI Agent Development
AI agents can combine language models with tools, APIs, business systems and workflows to complete multi-step tasks. We build agents around clearly defined tasks, controls and measurable outcomes.
Agent architecture
Tool calling
API統合
Workflow orchestration
Task planning
Multi-agent systems
Human-in-the-loop workflows
Permission controls
Agent evaluation
モニタリング
LLM Integration
We integrate leading language-model capabilities into existing products and platforms according to performance, cost, privacy and product requirements.
Model API integration
Cloud AI platforms
Open-source models
Multi-model architectures
Model routing
Fallback strategies
Streaming responses
Context management
認証とアクセス制御
Fine-Tuning & Model Adaptation
Where appropriate, model adaptation can improve performance for specific tasks, terminology, formats or domain requirements.
Dataset preparation
Instruction tuning
Supervised fine-tuning
Domain adaptation
Evaluation datasets
Model comparison
Inference optimization
Version management
Prompt Engineering
Well-designed prompts help applications provide consistent outputs for defined tasks. We develop reusable prompt strategies and evaluate them against real application requirements.
System prompts
Task prompts
Few-shot examples
Structured outputs
Prompt templates
Context management
Prompt testing
Prompt versioning
Failure handling
Enterprise Knowledge AI
Organizations hold valuable knowledge across documents and internal systems. We build controlled AI experiences around business knowledge and information access requirements.
Enterprise knowledge assistants
Policy assistants
Technical knowledge systems
Employee copilots
Document Q&A
Knowledge search
Cross-document reasoning
Permission-aware retrieval
Document Intelligence
LLMs can be combined with document processing to analyze large volumes of unstructured information. We design document workflows that connect extraction, reasoning and downstream business actions.
Document extraction
Classification
要約
Information extraction
Contract analysis
Report analysis
Invoice and form processing
Document comparison
Structured output generation
LLM-Powered Search
Traditional keyword search can struggle with natural-language questions and complex information needs. We build search experiences that help users find and understand relevant information faster.
Semantic search
Conversational search
Hybrid retrieval
Natural-language queries
Query rewriting
Result summarization
Source attribution
Enterprise search
LLM Automation
We identify repetitive language-intensive tasks where LLMs can support employees and workflows, connecting LLM capabilities with existing applications.
Email automation
Content classification
Ticket summarization
Lead qualification
Report generation
Knowledge extraction
Workflow routing
Customer communication
LLM Evaluation & Optimization
AI applications need continuous evaluation rather than one-time testing. We create evaluation frameworks that help teams understand where an LLM application succeeds and where it needs improvement.
Response quality
Groundedness
Factuality
Task completion
Retrieval quality
Latency
Token usage
Cost monitoring
Safety testing
Regression testing
AI Security & Governance
Enterprise AI requires appropriate controls around data, access, model usage and application behavior. We incorporate security and governance requirements into the architecture.
Access controls
Data handling
PII considerations
Prompt injection defenses
Output validation
Audit logging
Model governance
Human review
Usage monitoring
Multimodal LLM Applications
Modern AI applications can work across more than text. We design multimodal experiences where the use case benefits from combining language with visual or document inputs.
Image understanding
Document vision
Visual question answering
Product analysis
Multimodal assistants
Image-to-text workflows
Structured extraction
LLM Deployment & MLOps
Production AI requires reliable infrastructure, monitoring and controlled deployment. We help teams operate LLM applications reliably as usage grows.
クラウド展開
API architecture
Containerization
Scaling
Observability
Logging
Version control
Model monitoring
Cost monitoring
パフォーマンス最適化
Why Choose Obrive for LLM Development?
Business-First AI: Start with the business problem and measurable outcome.
End-to-End Engineering: Combine AI strategy, application development, data, retrieval, integrations and deployment.
Production Focus: Design for reliability, monitoring, security, scalability and operational cost.
Model Flexibility: Select models and architectures according to the specific requirements of the use case.
Data-Aware Architecture: Connect AI applications to relevant enterprise information.
Scalable AI Foundation: Build architectures that can evolve as models, data, users and business requirements change.
Discover
Understand your business, users, data, workflows, systems and AI objectives.
Assess
Evaluate use cases, data readiness, model options, technical constraints, privacy and expected ROI.
Architect
Design the LLM, application, retrieval, integration, security and infrastructure architecture.
Prototype
Build and test a focused proof of concept against real business scenarios.
Develop
Build the production application, integrations, evaluation framework and user experience.
Evaluate
Measure quality, groundedness, latency, cost, safety and task performance.
Deploy & Optimize
Launch, monitor and continuously improve the system as usage and requirements evolve.