CUSTOM RAG DEVELOPMENT SERVICES
We build retrieval-augmented generation (RAG) systems that give your teams and customers instant, accurate answers drawn directly from your internal documents, data, and policies. Our knowledge base solutions turn unstructured information into a competitive advantage – cutting response times, eliminating manual search, and grounding every answer in your own trusted, cited sources.
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RAG & Knowledge Base Services We Deliver
Knowledge Base Design & Data Ingestion
We audit your document repositories – PDFs, wikis, SharePoint libraries, CRMs, ticketing systems – and design an ingestion pipeline that chunks, embeds, and indexes content for high-recall retrieval, keeping your vector store current with your latest policies and data.
RAG Pipeline Development
Our engineers build end-to-end RAG pipelines combining semantic search, re-ranking, and prompt engineering. We select and fine-tune the embedding models and LLMs that fit your domain, balancing accuracy, latency, and cost so the system performs reliably at scale.
Conversational Q&A Interfaces
We create natural-language interfaces – chat widgets, Slack and Teams bots, internal portals – where users ask questions in plain English and receive context-rich answers, each linked back to the original document so they can verify and dive deeper.
Integration & Workflow Automation
RAG is most powerful when it's embedded in the tools your teams already use. We integrate knowledge base systems with helpdesks, ERPs, CRMs, and custom applications via secure APIs, webhooks, and SSO – so answers surface exactly where work happens.
Ongoing Optimization & Monitoring
After launch, we monitor retrieval precision, answer quality, and user satisfaction. We continuously tune chunking strategies, re-ranking models, and prompt templates to keep accuracy high as your knowledge base grows and evolves.
Citations, Security & Governance
Every answer is anchored to retrieved sources, with citation tracking and confidence scoring that flag when the system is unsure. We deploy on your cloud or on-prem with role-based access and encryption, supporting SOC 2, HIPAA, and GDPR requirements.
Technologies Behind Our RAG Systems
We choose embedding models, vector stores, and retrieval methods for each corpus based on accuracy, latency, and data-privacy needs – and keep every layer swappable as your knowledge base and the technology evolve.
LLMs & Orchestration Layer
OpenAI GPT, Anthropic Claude, and Google Gemini, or open-weight Llama and Mistral models where data residency calls for self-hosting; LangChain, LlamaIndex, and Haystack for retrieval orchestration, query rewriting, and citation-aware prompting; LiteLLM as a gateway for multi-model routing and fallbacks.
Security, Evaluation & Monitoring
SSO via SAML/OIDC with Okta or Microsoft Entra ID and document-level access control at query time; Ragas and TruLens for retrieval and answer quality; Langfuse for tracing. Deployable on AWS, Azure, Google Cloud, or on-prem.
Ingestion & Parsing
Unstructured, Docling, and LlamaParse for PDFs, tables, and scans; Apache Tika and Airbyte for bulk extraction and sync; connectors for SharePoint, Confluence, Notion, Google Drive, and Zendesk keep the index current.
Embeddings & Vector Stores
OpenAI, Cohere, and Voyage AI embeddings or open models such as BGE and E5 via sentence-transformers; stored in pgvector, Qdrant, Weaviate, Pinecone, Milvus, or Elasticsearch/OpenSearch, chosen by scale, filtering needs, and hosting constraints.
Retrieval & Re-Ranking
Hybrid search combining BM25 and dense vectors in Elasticsearch, OpenSearch, or Azure AI Search; Cohere Rerank or BGE cross-encoder re-rankers; and Neo4j for GraphRAG where relationships between documents and entities matter.
How We Build Your RAG System
Our delivery process mirrors the proven workflow we use across all AI engagements – adapted for the ingestion, retrieval, and relevance-tuning cycles knowledge systems demand.
Discovery & Knowledge Audit
We map your document landscape, identify high-value knowledge sources, and define success metrics – retrieval accuracy, answer relevance, and latency. You get a clear picture of what to ingest first and how success will be measured.
Proof of Concept & Validation
We design the retrieval pipeline, select embedding and LLM models, and deliver a working proof of concept against a representative data subset within three to four weeks, so your team can test accuracy before the full build.
Build, Integrate & Secure
We engineer the full solution – custom ingestion pipelines, vector databases, orchestration layers, frontend interfaces, and native API integrations with your existing stack – with security, observability, and scale built in from day one.
Launch & Continuous Improvement
After launch, we track retrieval precision, answer quality, and user satisfaction, then run continuous improvement cycles based on user feedback and retrieval analytics – keeping accuracy high as your knowledge base grows.
Industries & Use Cases
Our RAG systems serve organizations across sectors – wherever critical knowledge is buried in documents and teams or customers need accurate answers in seconds, not hours.
Legal & Professional Services
Attorneys and consultants search case files, contracts, and precedent libraries in natural language and get answers with pinpoint citations to the source passage.
Aerospace & Aviation
Maintenance technicians ask questions across manuals, service bulletins, and airworthiness directives and get cited, step-by-step procedures instead of paging through PDFs.
Public Sector
Citizen-facing assistants that answer questions about regulations, benefits, and permits from official documents – in multiple languages, with every answer linked to its source.
Frequently Asked Questions
Everything you need to know before building a RAG system or knowledge base.
How long does it take to build and launch a RAG system?
We can kick off within 1–2 weeks. A working proof of concept against a representative subset of your data follows within three to four weeks, so your team can test early. The production build depends mainly on how many data sources, integrations, and interfaces are involved. You get a clear timeline after discovery.
What data sources can a RAG system connect to?
Common sources include PDFs, wikis, SharePoint libraries, CRMs, and ticketing systems. We build an ingestion pipeline that chunks, embeds, and indexes the content and keeps the vector store current, then surface answers inside your helpdesk, ERP, CRM, Slack, or Teams via secure APIs, webhooks, and SSO.
How do you keep answers accurate and our data secure?
Every answer is anchored to retrieved source documents, with citation tracking and confidence scoring so users know where it came from and when the system is unsure. We deploy on your cloud (AWS, Azure, GCP) or on-premises, with role-based access and encryption at rest and in transit, supporting SOC 2, HIPAA, and GDPR requirements.