RAG Development Services | LLM Knowledge Base
LLMs hallucinate when they don't know the answer. For general knowledge questions, that's manageable. For questions about your product, your policies, your contracts, or your procedures, it's a liability. A model trained on the internet doesn't know what your product does, what your SLA says, or what your compliance policy requires.
Retrieval-augmented generation (RAG) fixes this. Instead of relying on training data, the model retrieves the right document from your knowledge base before generating a response. It answers from your content, with citations, not from its best guess.
LLM responses grounded in your documents and knowledge base
90%+ retrieval accuracy on domain-specific knowledge across our deployments
15+ RAG systems built across support, compliance, and enterprise knowledge use cases
Fixed project cost, scoped before development starts
Recent outcomes
Enterprise knowledge search · SaaS platform
Built a multi-source RAG system over Confluence, SharePoint, and internal PDFs. Reduced help-desk ticket volume for policy questions by 40%.
40% ticket reductionConversational AI · Operations platform
RAG-powered chatbot handling routine queries from a 50,000-article knowledge base. 70% of queries resolved without human intervention.
70% automated resolutionCompliance assistant · Healthcare client
HIPAA-compliant RAG system over clinical policies and CMS billing rules. Cut compliance lookup time from 15 minutes to under 60 seconds.
15x faster lookupsRecognition
LLM giving plausible but wrong answers from your product documentation?
Employees asking the same questions because the AI can't find the right policy?
The short answer
RaftLabs builds custom RAG systems that ground LLM responses in your own data. We have shipped 15+ RAG deployments for enterprise clients in the US and UK, achieving 90%+ retrieval accuracy. Single-domain builds take 4-8 weeks. Fixed price, scoped before development starts.
AI Development · Updated July 2026
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A language model trained on public data knows a lot about the world in general. It knows almost nothing about your product, your contracts, your procedures, or your customers. When you ask it about your specific context, it fills the gap with plausible-sounding text from its training data, which is often wrong in ways that are hard to detect.
RAG changes this. Instead of generating from training data, the model retrieves the specific documents relevant to your question and generates a response from that content. If your policy document says one thing and the model's training data suggests another, the model uses your document. The response is accurate to your knowledge, not the internet's.
This matters most in high-stakes contexts: customer support (wrong policy information damages trust), legal and compliance (wrong clause interpretation creates liability), healthcare (wrong clinical information creates risk), and internal operations (wrong procedure information causes errors).
Capabilities
Internal search tools that let employees ask questions in natural language and receive accurate, cited answers drawn from company documentation, not from the LLM's general training. Connectors for Confluence, SharePoint, Notion, Google Drive, and internal PDFs preserve document structure and access permissions. Every response cites the exact passage with a deep link to the source. When retrieval confidence is low, the system says so instead of guessing. Teams typically cut help-desk tickets for policy questions by 30-50%.
Systems that answer questions from specific document sets where accuracy is non-negotiable: contracts, compliance manuals, technical specifications, and research reports. The ingestion pipeline handles PDFs, Word documents, and scanned files via OCR. Chunking follows semantic boundaries such as section headings and clause breaks, so retrieved passages carry complete thoughts. Answers include inline citations naming the exact document and section, with confidence displayed for contract questions a legal professional should verify.
RAG-powered support knowledge bases deployed in two modes: agent assist, surfacing relevant articles beside the ticket, and customer-facing chatbot with a human escalation path. The system ingests product documentation, FAQ articles, and resolved tickets from Zendesk, Freshdesk, or Intercom. Low-confidence or out-of-scope queries route to an agent with the conversation context pre-populated. Escalations are logged and reviewed to close knowledge gaps.
RAG pipelines that retrieve from multiple knowledge sources at once and synthesise one unified, attributed response, because enterprise questions rarely live in a single document. Queries fan out in parallel across vector search, full-text search, databases, and live APIs, then a cross-source re-ranking step scores passages regardless of origin. Hybrid retrieval merges dense vector and BM25 keyword results with Reciprocal Rank Fusion. Every claim in the response is attributed to its source.
Compliance and policy assistants for regulated industries, where a wrong answer creates legal exposure. Regulatory documents are ingested with version history and effective dates, so a rule can be answered against the version in force on a given date. Stricter guardrails apply: higher confidence thresholds, and side-by-side presentation when interpretations differ. Every query, source, and response is logged to an immutable audit record.
RAG systems over codebases, API documentation, and runbooks that answer engineer questions from your actual source, not the LLM's general programming knowledge. Code is chunked at function and class boundaries with docstrings and type signatures preserved. OpenAPI specifications and READMEs are indexed alongside the implementation. On-call questions return the exact runbook steps. Optional IDE integration puts the whole system inside VS Code or JetBrains.
Services
RAG pipeline development
End-to-end retrieval pipelines: ingestion, chunking, embedding, hybrid retrieval, re-ranking, and grounded generation, built and tuned against a ground-truth question set before launch.
Vector database development
Vector store selection, schema design, indexing, and metadata filtering on Pinecone, Weaviate, Qdrant, pgvector, or Milvus, sized to your scale and deployment context.
AI knowledge management
Internal knowledge systems that let employees ask questions in natural language and receive cited answers from Confluence, SharePoint, Notion, and your document stores.
LLM integration
Connecting GPT-4o, Claude, Gemini, or self-hosted Llama models to your products, with the retrieval layer, prompts, and guardrails that keep responses grounded in your data.
AI search and semantic search
Semantic and hybrid search over your content: dense vector retrieval combined with BM25 keyword search and cross-encoder re-ranking for relevance that keyword search alone cannot reach.
AI chatbot development
Customer-facing and internal chatbots backed by RAG so every response is drawn from your documentation, with citations and an escalation path when confidence is low.
Generative AI development
Full LLM application development beyond retrieval: agents, workflow automation, and generative features that use your RAG system as their knowledge layer.
Tell us the knowledge sources and the query types. We'll design the RAG architecture and give you a fixed cost.
Capabilities
Content extraction from each source type using the appropriate tool: PDFs via PyMuPDF (structure-preserving) or Azure Document Intelligence (for scanned documents requiring OCR); Word/Excel via python-docx/openpyxl; Confluence via REST API with recursive space export; SharePoint via Microsoft Graph API; Slack via Events API; databases via SQL query with row-level access control mapped at extraction time. Extracted content cleaned to remove navigation chrome, repeated headers/footers, and formatting noise before chunking. Chunking strategy selected by content type: hierarchical chunking for structured documents (preserve heading > section > paragraph hierarchy, with each chunk inheriting its parent headings for context); fixed-overlap chunking for unstructured prose (800 tokens, 10% overlap); code chunking at function/class boundaries for technical documentation. Each chunk enriched with metadata at index time: source name, URL or file path, section heading path, document date, version, access tier. Embeddings generated using OpenAI text-embedding-3-large, Cohere embed-v3, or an open-source model (e5-large-v2, bge-large-en) hosted on-premises for data privacy requirements. Vector store selected by scale and deployment context: Pinecone or Weaviate for managed cloud; pgvector on PostgreSQL for teams already on Postgres who want to avoid a separate vector service; Qdrant or Chroma for on-premises or private cloud deployments. The ingestion pipeline runs on a schedule (nightly for most document sources, near-real-time for support ticket feeds) so the knowledge base stays current as documents are updated. Deleted documents are detected via hash comparison on each ingestion run and removed from the index.
Query processing begins with intent analysis and query transformation: the raw user query is expanded with synonyms and domain-specific vocabulary to improve recall; for conversational interactions, the query is reformulated to include the relevant context from conversation history (HyDE, hypothetical document embedding, optionally used to improve retrieval of conceptual answers from definitional queries). The retrieval step uses hybrid search: dense vector retrieval (approximate nearest neighbour via HNSW index in the chosen vector store) returns the top-20 semantically similar chunks; BM25 sparse retrieval (Elasticsearch or Opensearch) returns the top-20 keyword-matching chunks. Reciprocal Rank Fusion (RRF) merges and re-scores the two ranked lists before re-ranking. Cross-encoder re-ranking (Cohere Rerank v3, or an open-source cross-encoder from HuggingFace such as ms-marco-MiniLM-L-6-v2) scores each candidate chunk against the full query using a more computationally expensive but more accurate model than the bi-encoder used for initial retrieval, re-ranking the top-20 results down to the top-5 that are most relevant to the specific query phrasing. Metadata filtering applied before or alongside vector retrieval to constrain results to the relevant source, time range, or access tier without sacrificing retrieval quality on the filtered subset. Access control enforcement: each retrieved chunk is checked against the requesting user's permissions before being passed to the generation step, chunks from documents the user is not authorised to read are silently excluded from context. The final context window is assembled from the top-5 to top-10 re-ranked chunks, with deduplication to remove near-identical passages that would waste context window tokens. Retrieval quality is monitored per query: retrieval scores and the final context are logged to an evaluation store, enabling regular RAGAS-based evaluation (context recall, context precision, answer faithfulness) against a ground-truth question set.
The assembled context and the user's query are passed to the LLM (GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, or a self-hosted open-source model such as Llama 3.1 70B via vLLM for on-premises deployments) with a system prompt that instructs the model to answer exclusively from the provided context, not from prior knowledge. The prompt explicitly instructs the model to state when it cannot find a sufficient answer in the context rather than inferring or extrapolating, the single instruction that most reduces hallucination in RAG systems. Source attribution: the model is instructed to cite which chunk (by document name and section) supported each factual claim in the response; these citations are displayed inline or as footnotes in the response interface. Confidence scoring applied at two levels: retrieval confidence (the maximum re-ranking score across the retrieved chunks) and generation confidence (assessed by prompting the model to rate its own certainty on a 1-3 scale given the available context). When retrieval confidence is below threshold (cosine similarity < 0.75 on top chunk) or generation confidence is self-rated low, the response is prefixed with a visibility indicator that the answer may be incomplete and alternative sources are suggested. Fallback chain: no relevant retrieval result → "I couldn't find an answer to this in the knowledge base, here are the most related topics"; retrieval confidence marginal → provide answer with lower-confidence flag; off-topic query → graceful decline with scope explanation. Response length calibrated to the query type: factual lookups produce concise direct answers; how-to queries produce numbered steps; complex policy questions produce structured responses with sections. Conversation memory maintained for multi-turn interactions using a sliding window of the last N exchanges (configurable per deployment) summarised before being appended to the system context to prevent context window overflow on long conversations.
We are not tied to one model, one vector store, or one framework. We pick each layer to fit your accuracy targets, your data privacy requirements, and your deployment context, then document every choice so any competent engineering team can maintain it. The technologies we reach for most often:
| Layer | Technologies we use | Where it fits |
|---|---|---|
| Embeddings | OpenAI text-embedding-3-large, Cohere embed-v3, sentence-transformers (e5-large-v2, bge-large-en) | Turning documents and queries into vectors; open-source models when data must stay on-premises |
| Vector databases | Pinecone, Weaviate, Qdrant, pgvector, Milvus | Storing and searching embeddings; managed cloud, Postgres-native, or self-hosted by deployment context |
| Orchestration | LangChain, LlamaIndex | Ingestion, chunking, retrieval, and re-ranking pipelines wired end to end |
| Models | GPT-4o, Claude, Llama | Grounded answer generation; self-hosted Llama via vLLM for private deployments |
| Retrieval and re-ranking | BM25 (Elasticsearch, OpenSearch), Reciprocal Rank Fusion, Cohere Rerank v3, cross-encoders | Hybrid dense plus sparse search with a re-ranking pass for relevance |
| Backend and cloud | Python, FastAPI, AWS, GCP, Docker | APIs, connectors, scheduled ingestion, and production deployment |
The rule holds at every layer: no proprietary frameworks that lock you in, and no stack we cannot hand to your team on day one.
We price by project, not by the hour. After a scoping session you get a fixed quote with a defined scope, timeline, and price, so you know the number before development starts.
| Project type | Typical timeline | Cost range |
|---|---|---|
| Focused RAG system, one or two knowledge sources, single use case | 4–8 weeks | $15,000–$40,000 |
| Multi-domain enterprise RAG, custom connectors, access controls, and product interface | 10–16 weeks | $45,000–$120,000 |
What pushes cost up: complex or heterogeneous data sources requiring custom connectors, document-level access controls across multiple visibility tiers, strict compliance requirements such as HIPAA and GDPR, and a custom UI rather than API-only access. What keeps it down: a narrow first scope over one or two clean sources, managed vector and cloud services rather than self-hosted infrastructure early on, and API access instead of a bespoke interface. We scope every project before pricing it.
Why us
The engineers who assess your problem also build the solution. No bait-and-switch, no offshore handoff after the contract is signed. The team you meet in week 1 ships in week 12.
We scope the work, calculate the cost, and lock it in writing before any development starts. A scope change is a change request: priced, agreed, or dropped. It never absorbs into the project and appears on the final invoice.
Clients include Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin. Track record across AI, SaaS, mobile, automation, and enterprise platforms across healthcare, fintech, logistics, and hospitality.
GDPR, HIPAA, SOC 2 compliance requirements are scoped in week 1, not retrofitted before launch. We have shipped HIPAA-compliant RAG systems for US healthcare clients and GDPR-compliant deployments for European markets.
RAG Pipeline Development
End-to-end ingestion, retrieval, and generation pipelines.
Vector Database Development
Embedding stores and semantic indexes for retrieval.
Generative AI Development
Full LLM application development beyond retrieval.
AI Chatbot Development
Chatbots powered by RAG for accurate customer-facing responses.
AI Agent Development
Agents that use RAG as their knowledge layer.
AI Document Intelligence
Extracting structured data from unstructured documents.
LLM Integration
Connecting LLMs to your existing products.
Tell us what's broken. Within one business day you get a straight take on cost, timeline, and the right first step. No deck, no pressure.
RAG is an architecture where a language model retrieves relevant context from a knowledge base before generating a response. Instead of relying on what the model learned during training, it reads the specific documents, passages, or records that are relevant to the question, and generates a response grounded in that content. The result is accurate, citation-backed answers from your specific knowledge, not hallucinated outputs from the model's general training.
Use RAG when your knowledge changes frequently, when accuracy and citations are critical, or when your knowledge base is too large to fit in context. Fine-tuning is better when you need to change the model's tone or style, teach it a specific format, or improve performance on a narrow task. For most enterprise knowledge applications, internal search, customer support, document Q&A, RAG gives better accuracy at lower cost than fine-tuning, and updates to the knowledge base don't require retraining.
We connect RAG systems to documents (PDFs, Word files, HTML), databases (SQL, NoSQL), ticketing systems (Zendesk, Jira), wikis (Confluence, Notion), SharePoint, Slack, email, and custom data stores. We handle the extraction, chunking, embedding, and indexing pipeline for each source type. If your data is in a structured format we haven't mentioned, we can write a custom connector.
The core RAG architecture grounds responses in retrieved context, which eliminates most hallucination. We add further guardrails, confidence scoring on retrievals, fallback responses when retrieval quality is low, source attribution in every response, and conversation monitoring that flags anomalous outputs. We also test accuracy against a set of ground-truth question-answer pairs before launch. If the retrieval doesn't find relevant context, the system says so rather than guessing.
A focused single-domain RAG system, connecting one or two knowledge sources and building a query interface, typically takes 4–8 weeks. A multi-domain enterprise RAG system with custom connectors, access controls, and an analytics dashboard takes 10–16 weeks. We build a working demo in the first 2 weeks so you can test accuracy before committing to the full scope.
A focused RAG system for a single use case typically runs $15,000--$40,000. A multi-domain enterprise RAG system with custom connectors and a full product interface typically runs $45,000--$120,000. Cost depends on data source complexity, the number of domains, access control requirements, and whether you need a custom UI or API-only access. We scope every project before pricing it.
Yes. We implement document-level access controls so users can only retrieve content they're authorised to see. This is critical for enterprise deployments where the knowledge base contains content with different access tiers, HR documents visible only to managers, client-specific content visible only to the relevant account team, or regulated data with compliance restrictions. The access control layer is designed as part of the retrieval architecture, not bolted on after.
Work with us
We scope RAG Development Services in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.
Healthcare
Clinical knowledge retrieval, medical literature search, and care protocol AI.
Legal
Case law retrieval, contract analysis, and regulatory compliance AI.
FinTech
Financial document retrieval, risk analysis, and regulatory reporting AI.
Insurance
Policy retrieval, claims research, and underwriting knowledge AI.
RAG by sector
Healthcare
Clinical knowledge retrieval grounded in proprietary medical literature and protocols.
Legal
Case law retrieval, contract analysis, and regulatory knowledge AI for law firms.
FinTech
Financial document retrieval, risk analysis, and regulatory reporting grounding.
Insurance
Policy retrieval, claims research, and underwriting knowledge AI.