Retrieval-Augmented Generation

Smarter answers, faster workflows.

AI / Retrieval Augmented Generation

RAG systems bridge the gap between large language models and your actual data. Instead of relying on static training or hoping the model "guesses right," RAG pipelines retrieve live, relevant content from your private knowledge base — before handing it off to the LLM to generate a response.

Smarter answers, faster workflows with context that actually matters.

We help teams design, build, and deploy RAG architectures that are fast, accurate, secure, and production-grade. Whether you’re using LangChain, LlamaIndex, or your own orchestration layer, CONFLICT delivers retrieval workflows that drive real business value.

What We Build

RAG Architectures

From basic pipelines to full-stack solutions with context-aware fetching, ranking, and formatting.

LLM App Scaffolds

Chat interfaces, API endpoints, and tools built on LangChain, LlamaIndex, Semantic Kernel, or custom stacks.

Vector Database Integrations

Pinecone, Weaviate, Qdrant, PGVector, ChromaDB, Redis, OpenSearch, or hybrid models.

Embeddings Pipelines

Batch and real-time embedding generation with OpenAI, HuggingFace, Cohere, or open-source models.

Prompt Engineering

Dynamic prompt templates, retrieval formatting, citation injection, and response optimization.

Data Prep

Chunking, deduplication, metadata tagging, and ingestion pipelines from file systems, databases, or APIs.

Security & Access Control

Permissions, filtering, audit trails, and role-aware context delivery.

Monitoring & Evaluation

Latency tracking, hallucination mitigation, ranking feedback loops, and system refinement.

Use Cases We Power

Internal chatbots with real-time access to SOPs, support articles, docs, and knowledge bases.
Customer-facing Q&A systems powered by your documentation, product manuals, or proprietary data.
Legal, compliance, or research assistants built on private corpora.
Multi-source aggregators (email + Slack + Notion + CRM) with intelligent query interfaces.
Document summarization and semantic search interfaces for internal teams.

What Makes Us Different

We’ve worked with LangChain and LlamaIndex since the early days — and know where they shine (and where they break).
We treat RAG like a software engineering problem, not a novelty. That means reliable uptime, deterministic outputs, and performance that scales.
We’re API-first: whether it’s embedding into your frontend or triggering from a Slack bot, we design for flexibility and reuse.

Want to Build It Right?

Let’s architect a RAG system that doesn’t just sound smart — it is smart.

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