RAG Development That Grounds AI in Your Actual Data
Our team builds RAG pipelines to retrieve context from your data so your AI delivers grounded answers instead of confident guesses.
👋 Talk to a RAG engineer.
Trusted and top rated tech team
When your AI makes things up
Your LLM gives confident answers that aren’t grounded in anything real. It hallucinates facts, misses internal context, and can’t cite where its answers came from. RAG fixes this by retrieving relevant content from your own documents before the model generates a response. Curotec builds the ingestion, retrieval, and generation pipeline that makes that work reliably in production.
Our capabilities include:
- Document ingestion and chunking strategy
- Embedding model selection and vector storage
- Semantic search and hybrid retrieval configuration
- Reranking and context relevance tuning
- Evaluation frameworks for accuracy and hallucination
- Access controls and permission-aware retrieval
Who we support
We build RAG systems for teams that need AI to respond from their own knowledge base with answers that are accurate, traceable, and scoped to what each user is allowed to see.
Teams Whose AI Chatbot Hallucinates
You launched an AI feature but users don't trust it. Responses sound plausible but contain fabricated details, outdated information, or answers pulled from the wrong context. You need a retrieval layer that grounds every response in verified source material.
Enterprises With Scattered Knowledge
Your documentation lives across wikis, PDFs, support tickets, and shared drives. Employees waste hours searching for answers that exist somewhere in the organization. You need a system that ingests those sources and makes them queryable through a single AI interface.
Product Teams Adding AI to Their Platform
You want to embed AI-powered search, Q&A, or document analysis into your product but your team hasn't built a RAG pipeline before. You need engineers who've handled chunking, embedding, retrieval tuning, and evaluation so the feature ships accurate from day one.
Ways to engage
We offer a wide range of engagement models to meet our clients’ needs. From hourly consultation to fully managed solutions, our engagement models are designed to be flexible and customizable.
Staff Augmentation
Get access to on-demand product and engineering team talent that gives your company the flexibility to scale up and down as business needs ebb and flow.
Retainer Services
Retainers are perfect for companies that have a fully built product in maintenance mode. We'll give you peace of mind by keeping your software running, secure, and up to date.
Project Engagement
Project-based contracts that can range from small-scale audit and strategy sessions to more intricate replatforming or build from scratch initiatives.
We'll spec out a custom engagement model for you
Invested in creating success and defining new standards
At Curotec, we do more than deliver cutting-edge solutions — we build lasting partnerships. It’s the trust and collaboration we foster with our clients that make CEOs, CTOs, and CMOs consistently choose Curotec as their go-to partner.
Why choose Curotec for RAG development?
Every RAG tutorial gets you a working demo in an afternoon. Production is a different problem. Chunking strategies that lose context across document boundaries, retrieval that returns irrelevant results, embeddings that miss domain-specific meaning, and no way to measure whether answers are actually accurate. Our engineers build RAG pipelines past the demo stage because we’ve already solved the problems the tutorials don’t cover.
1
Extraordinary people, exceptional outcomes
Our outstanding team represents our greatest asset. With business acumen, we translate objectives into solutions. Intellectual agility drives efficient software development problem-solving. Superior communication ensures seamless teamwork integration.
2
Deep technical expertise
We don’t claim to be experts in every framework and language. Instead, we focus on the tech ecosystems in which we excel, selecting engagements that align with our competencies for optimal results. Moreover, we offer pre-developed components and scaffolding to save you time and money.
3
Balancing innovation with practicality
We stay ahead of industry trends and innovations, avoiding the hype of every new technology fad. Focusing on innovations with real commercial potential, we guide you through the ever-changing tech landscape, helping you embrace proven technologies and cutting-edge advancements.
4
Flexibility in our approach
We offer a range of flexible working arrangements to meet your specific needs. Whether you prefer our end-to-end project delivery, embedding our experts within your teams, or consulting and retainer options, we have a solution designed to suit you.
What our RAG implementations cover
Document Ingestion & Chunking
Embedding & Vector Storage
Retrieval & Reranking
Context-Aware Prompting
Accuracy Evaluation
Permission-Scoped Retrieval
Tools and frameworks in our RAG builds
Embedding Models & Vector Databases
Your embedding model and vector store determine how well your system matches queries to the right content in your knowledge base.
- OpenAI Embeddings – text-embedding-3 models that convert documents and queries into high-dimensional vectors for semantic similarity matching
- Cohere Embed – Multilingual embedding models with compression support for reducing storage costs while maintaining retrieval accuracy
- Pinecone – Managed vector database with real-time indexing, metadata filtering, and namespace isolation for multi-tenant applications
- Weaviate – Open-source vector database with hybrid search combining dense vectors and BM25 keyword matching in a single query
- pgvector – PostgreSQL extension that adds vector similarity search to your existing relational database without a separate infrastructure layer
- Qdrant – Vector search engine with payload filtering, on-premise deployment, and snapshot-based backups for enterprise requirements
- Elasticsearch – Hybrid search engine combining BM25 keyword matching with vector similarity for retrieval pipelines that need both lexical and semantic precision
Orchestration & Pipeline Frameworks
Chaining ingestion, retrieval, and generation into a reliable workflow requires frameworks built for multi-step AI coordination.
- LangChain – Python and JavaScript framework for chaining prompts, managing memory, and building multi-step workflows with retrieval integration
- LlamaIndex – Indexing and query framework that connects LLMs to external sources with chunking strategies, retrievers, and response synthesizers
- Haystack – Open-source framework for building search and question-answering pipelines with modular components and evaluation tools
- Semantic Kernel – Microsoft SDK for integrating LLMs into .NET and Python applications with prompt templating and plugin architecture
- Instructor – Structured output extraction from LLMs with Pydantic validation for type-safe responses in production pipelines
- Flowise – Low-code builder for LLM workflows with visual chaining, LangChain integration, and API endpoint generation
Document Processing & Ingestion
How you parse, clean, and chunk source material defines whether your retriever returns complete answers or fragmented noise.
- Unstructured – Library for parsing PDFs, HTML, Word docs, and emails into clean text with element-level metadata for structured chunking
- LangChain Document Loaders – Pre-built connectors for ingesting from S3, Google Drive, Notion, Confluence, and dozens of other sources
- Recursive Text Splitters – Chunking strategies that respect document structure by splitting on headings, paragraphs, and semantic boundaries
- Docling – IBM’s document parser with table extraction, layout analysis, and OCR for complex PDFs and scanned materials
- Apache Tika – Content detection and extraction framework supporting over a thousand file formats for large-scale ingestion pipelines
- Metadata Enrichment – Adding source, date, section, and permission tags to chunks so retrieval can filter by context, not just similarity
LLM Providers & Generation
Which model generates your responses affects answer quality, latency, token cost, and how well it handles retrieved context.
- OpenAI API – GPT-5 and GPT-4o models with function calling, structured output, and long-context support for complex RAG responses
- Anthropic Claude API – Long-context models suited for analysis, summarization, and nuanced generation where retrieved documents are extensive
- AWS Bedrock – Managed access to foundation models from Anthropic, Meta, and Cohere with AWS VPC integration and enterprise security
- Azure OpenAI Service – Enterprise deployment of OpenAI models with Azure compliance, regional availability, and private networking
- Google Vertex AI – Gemini models with grounding API that connects generation directly to your search corpus for source-attributed responses
- Open-Source Models – Llama, Mistral, and other self-hosted options for teams requiring on-premise deployment or full control over inference
Evaluation & Quality Measurement
Measuring whether your pipeline returns accurate, relevant, grounded answers requires tooling purpose-built for RAG assessment.
- RAGAS – Framework for evaluating faithfulness, answer relevance, and context precision across your RAG pipeline outputs
- TruLens – Feedback functions that score groundedness, relevance, and harmfulness with tracing across each pipeline step
- LangSmith – LangChain’s tracing and evaluation platform with prompt versioning, test datasets, and regression detection
- DeepEval – Unit testing framework for LLM outputs with metrics for hallucination, bias, toxicity, and answer correctness
- Phoenix by Arize – Observability for LLM applications with retrieval analysis, embedding drift detection, and response quality tracking
- Custom Eval Pipelines – Domain-specific accuracy benchmarks built against your source material to measure real-world performance, not generic scores
Deployment & Monitoring
Running a RAG system in production means tracking retrieval quality, generation latency, and token spend alongside your application metrics.
- Docker – Containerized pipeline deployment for consistent behavior across local development, staging, and production environments
- Kubernetes – Orchestrated scaling for ingestion workers, retrieval services, and generation endpoints with independent resource allocation
- Datadog – APM tracing for retrieval latency, generation duration, token usage, and error rates across your full RAG pipeline
- Helicone – LLM observability with request logging, cost tracking, and latency analytics across providers and prompt versions
- Portkey – Gateway for managing multiple LLM providers with automatic fallbacks, caching, and spend monitoring per feature
- Redis – Semantic caching layer that stores frequent query responses to reduce redundant API calls and lower per-request costs
FAQs about our RAG development services
How is RAG different from fine-tuning?
Fine-tuning changes the model itself. RAG leaves the model untouched and feeds it relevant context at query time. RAG is faster to implement, cheaper to maintain, and easier to update when your source material changes.
What types of documents can RAG ingest?
PDFs, Word docs, HTML pages, Confluence wikis, Notion databases, support tickets, Slack exports, and structured data from APIs or databases. We build ingestion pipelines that handle whatever formats your knowledge lives in.
How do you prevent hallucinations in RAG?
Better chunking, retrieval tuning, and reranking reduce hallucinations by getting the right context into the prompt. We add evaluation frameworks that measure faithfulness against source material so accuracy is tracked, not assumed.
Can RAG respect our document permissions?
Yes. We configure permission-aware retrieval so users only get answers from content they’re authorized to access. Metadata tags on each chunk map back to your existing role and access control structure.
How do you measure RAG accuracy?
We use evaluation frameworks like RAGAS and TruLens that score faithfulness, context relevance, and answer correctness against your source material. These run as automated checks in your pipeline, not one-time audits.
Can you add RAG to our existing AI features?
Yes. If you already have an LLM integration, we add the retrieval layer without rebuilding your existing setup. Ingestion, vector storage, and retrieval sit between your application and the model as a new pipeline stage.
Ready to have a conversation?
We’re here to discuss how we can partner, sharing our knowledge and experience for your product development needs. Get started driving your business forward.
