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
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LLM giving plausible but wrong answers from your product documentation?
Employees asking the same questions because the AI can't find the right policy?
In short
RaftLabs builds custom RAG (retrieval-augmented generation) systems that connect LLMs to your data. We deliver enterprise knowledge search, document Q&A, customer support knowledge bases, multi-source retrieval pipelines, and compliance assistants. The system is trained on your interaction data, achieves 90%+ retrieval accuracy across our deployments, and ships with source citations on every response. Single-domain RAG takes 4-8 weeks. Multi-domain enterprise systems take 10-16 weeks. Fixed cost, scoped before development starts.
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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, "What is the policy for expense reimbursement over £500?", "How do I configure the staging environment?", "What does our MSA say about liability caps?", and receive accurate, cited answers drawn directly from company documentation, not from the LLM's general training. The source corpus typically spans: Confluence wikis, SharePoint document libraries, Notion workspaces, Google Drive folders, internal PDFs, and HR policy systems. Each source requires a custom connector to extract content, preserve document structure (headings, tables, numbered lists), and map access permissions so retrieval respects the same visibility rules as the original system. The query interface supports conversational follow-up: "Who approved that policy?" following "What is the expense policy?" uses conversation history as context for the next retrieval step rather than treating each query in isolation. Source citations appear on every response, not just the document name but the exact passage, the section heading, and a deep link to the original document so the employee can verify the answer and read the full context. Quality threshold: if retrieval confidence is below a configurable threshold (typically cosine similarity < 0.75), the system returns "I couldn't find a clear answer in the knowledge base, here are the closest matches" rather than generating a low-confidence answer that might be wrong. Organisations that deploy internal knowledge search typically reduce help-desk ticket volume for policy and procedural questions by 30-50%, with the largest gains on questions that previously required a manager to locate the correct policy document.
Systems that answer questions from specific document sets where accuracy is non-negotiable: commercial contracts ("Does our MSA with Acme Corp allow sublicensing?"), compliance manuals ("What is the GDPR data retention requirement for marketing emails?"), technical specifications ("What is the maximum payload size for the v2 API endpoint?"), research reports ("What did the Phase 2 trial say about the primary endpoint?"). The document ingestion pipeline handles the range of formats that enterprise document sets contain: PDFs (with structure-preserving extraction using PyMuPDF or Azure Document Intelligence), Word documents (.docx via python-docx), PowerPoint slides, HTML pages, and scanned documents (OCR via AWS Textract or Tesseract for lower-quality scans). Large documents are chunked using a semantic boundary strategy rather than fixed-size chunking: chunk boundaries are placed at section headings, paragraph breaks, and logical clause boundaries so a retrieved chunk contains a complete thought rather than half a sentence from one clause and half from the next. Chunk overlap (typically 10-15% of chunk size) ensures context at chunk boundaries is not lost during retrieval. Document-level metadata attached to each chunk at ingestion: document name, section, author, last modified date, document version, and any custom taxonomy fields, enabling filtered retrieval where the user specifies "from our 2024 contracts" or "from documents approved by Legal." Answer generation with inline citations: the response identifies which passage from which document supported each claim, with the specific clause or section number where applicable. For contract Q&A use cases, answer confidence is displayed alongside the response and responses carry a disclaimer when the question covers an area where multiple clauses may be relevant and a legal professional should verify.
RAG-powered support knowledge bases deployed in two modes: agent assist (surfacing relevant knowledge to a human agent in real time as the customer's query comes in) and customer-facing chatbot (handling queries directly with the agent escalation path as the fallback). The knowledge base ingests your product documentation, FAQ articles, historical resolved support tickets (Zendesk, Freshdesk, Intercom ticket exports), and internal support runbooks, structured to capture not just the official answer but the phrasing and edge cases that actually appear in customer queries. Query understanding: before retrieval, the system normalises customer language to the product terminology used in documentation (a customer asking "why is my sync broken" maps to the same retrieval target as "connector status shows error") using a synonym expansion layer built from your product's vocabulary. Agent assist mode: when an agent opens a support ticket, the system retrieves the 3-5 most relevant knowledge base articles and displays them in a sidebar alongside the ticket, ranked by semantic similarity to the customer's query. The agent selects the relevant content, edits if needed, and sends, reducing average handle time by eliminating the time agents spend searching the knowledge base manually. Self-service chatbot mode: customer queries handled directly with responses drawn from the knowledge base and citations showing which help article supported the answer. When retrieval confidence is below threshold or the query type is outside the knowledge base scope (billing disputes, complaints, account-specific issues), the system routes to a human agent with the conversation context pre-populated in the ticket. Continuous improvement: queries that resulted in an agent escalation (indicating the self-service answer was insufficient) are logged and reviewed weekly to identify knowledge gaps, which are addressed by adding or updating documentation in the knowledge base.
RAG pipelines that retrieve from multiple knowledge sources simultaneously and synthesise a coherent, unified response, because complex queries in enterprise contexts rarely have their answer in a single source. A question like "What is our standard response to a customer asking about GDPR data portability?" requires retrieving from the legal policy document, the customer-facing FAQ template, the support team runbook for handling portability requests, and possibly a Slack channel where the legal team clarified the process last month. The multi-source retrieval architecture routes each query to the relevant sources in parallel: vector search against document stores, full-text search against ticket systems (Elasticsearch, Opensearch), API calls to real-time data sources (live product status, pricing APIs, CRM account data), and structured database queries for factual lookups. Results from each source are retrieved independently, then fed through a cross-source re-ranking step that scores passages by relevance to the query regardless of which source they came from. The synthesised response draws from whichever sources provided the most relevant content, with each claim in the response attributed to its specific source. Hybrid retrieval strategy: dense vector search (OpenAI text-embedding-3-large, Cohere embed-v3, or open-source models via HuggingFace depending on privacy requirements) combined with sparse BM25 keyword search and the results merged using Reciprocal Rank Fusion (RRF), because different query types (keyword-specific lookups vs. semantic concept questions) are served better by different retrieval strategies, and the hybrid approach performs better than either alone on most enterprise query sets. Source priority configuration: for safety-critical domains, the pipeline is configured to prefer verified regulatory documents over derived internal summaries when both return relevant results.
Compliance and policy assistants built for regulated industries where a wrong answer to a compliance question creates legal exposure, not just a bad user experience. Use cases: financial services compliance teams asking about FCA/SEC/FINRA rule interpretations; healthcare compliance teams querying HIPAA policy requirements, CMS billing rules, and Joint Commission standards; legal teams asking about contractual obligation tracking across an active contract portfolio; HR teams answering employee questions about employment law, leave policy, and benefits entitlement. The knowledge base for a compliance assistant is structured differently from a general document Q&A system: regulatory documents are ingested with their version history preserved and effective dates tracked, so a query about a rule can be answered against the version that was in force on a specific date. Internal policy documents are mapped to the regulatory requirements they satisfy, creating a bidirectional traceability layer: "which regulation does this policy satisfy?" and "which of our policies addresses this regulation?" are both answerable. Response generation for compliance queries applies stricter guardrails: confidence threshold is set higher (> 0.85 cosine similarity required before generating a direct answer); for queries where multiple interpretations are plausible, the system presents the relevant regulatory text and the internal policy position side by side rather than synthesising an answer that might smooth over a genuine ambiguity. Audit trail: every query, the retrieved sources, and the generated response are logged to an immutable audit record, so if a compliance decision was made partly based on the assistant's output, that output and its sources are preserved for audit purposes. Integration with regulatory update feeds (Thomson Reuters Regulatory Intelligence, Wolters Kluwer, or jurisdiction-specific regulators) to flag when source documents are updated and trigger a knowledge base refresh review.
RAG systems built over codebases, API documentation, and technical runbooks that answer developer and engineer questions from the actual source, not from the LLM's general programming knowledge, which reflects patterns in public code rather than the specific architecture choices in your system. Code corpus ingestion handles the structure of code differently from prose documents: functions, classes, and methods are chunked at semantic boundaries (function definitions, class boundaries) rather than by line count; docstrings and inline comments are indexed alongside the code they describe; type signatures and imports are preserved in each chunk to provide the context needed for accurate answers. Git history integration: code chunks can be annotated with commit messages and PR descriptions from git log, adding the "why" context that the code alone doesn't explain. Query types supported: "How does the user authentication flow work in this service?", "What does the processPayment function do and what are its failure modes?", "Show me all the places where we make external API calls to Stripe", "What environment variables does the deployment process require?". API documentation RAG: OpenAPI/Swagger specifications, README files, and Postman collections indexed alongside the implementation code so questions about endpoint behaviour, parameter constraints, and response formats are answered from the authoritative specification, not from a developer's memory. Technical runbook Q&A: runbooks for on-call engineers ingested so "how do I restart the payment worker process?" or "what do I do when the Redis memory alarm fires?" returns the exact runbook steps, not a generic recommendation. IDE integration option: the RAG system exposed via a VS Code extension or JetBrains plugin so developers query the codebase knowledge base within their editor context without context-switching to a separate tool.
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.
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.
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.
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