Conversational AI chatbot built on Anthropic Claude
- 12 weeks
- from concept to launch
RAG Development Services | LLM Knowledge Base
RAG development that grounds your LLM in your own data.
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, with a source citation on every answer
Retrieval quality validated against a labelled test set before the answer layer is built
RAG systems across support, compliance, and enterprise knowledge use cases
Fixed project cost, scoped before development starts
0-delay insights Voice AI20k+ txns day one AI Automation1,062 users in 4 weeks Loyalty
The problem
Sound familiar?
LLM giving plausible but wrong answers from your product documentation?
Employees asking the same questions because the AI can't find the right policy?
Short answer
RaftLabs builds custom RAG systems that ground LLM responses in your own data, for enterprise clients across the US, UK, Europe, Canada, GCC, South Africa, and Southeast Asia. Retrieval quality is validated against a labelled test set before the answer layer is built. Single-domain builds take 4-8 weeks. Fixed price, scoped before development starts.
Key takeaways
- Single-domain RAG systems typically take 4-8 weeks from kick-off to production.
- Multi-domain enterprise RAG systems with custom connectors take 10-16 weeks.
- Single-domain builds run $15,000-$40,000; multi-domain enterprise systems run $45,000-$120,000.
- All projects are fixed price, scoped before development starts.
- HIPAA-compliant RAG builds are supported for regulated industries.
Trusted by
The help desk that stopped answering the same policy question.
An internal help desk used to run one way: an employee asks where a policy lives, someone hunts through Confluence, SharePoint, and a folder of PDFs, finds the passage, and answers. The same question comes back a week later from someone else.
Now a RAG system takes the question first. It retrieves the exact passage from the knowledge base, answers in natural language, and shows the source it drew from. The same question stops coming back to a human.
That system is not a chatbot guessing from training data. It reads your documents, grounds every answer in retrieved context, and cites the passage it used. The interface is the least interesting part. The retrieval underneath it is the product.
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.
According to a 2024 NAACL industry-track study published in the ACL Anthology, retrieval-augmented generation reduces hallucination rates by 70-90% compared to ungrounded LLM responses in enterprise knowledge tasks. For teams deploying LLMs against internal documentation, contracts, or compliance materials, that gap is the difference between a system you can trust and one that creates liability.
RaftLabs builds RAG systems across support, compliance, and enterprise knowledge use cases, with HIPAA-compliant builds for US healthcare clients and GDPR-compliant deployments for European markets. One team scopes the knowledge sources, builds the pipeline, integrates it, and hands it over.
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).
RAG pays off when accuracy matters and the knowledge is yours.
Everything on the left should already be true for your team. Even one thing on the right, and fine-tuning or a plain LLM call is the smarter first step.
A fit
01
A knowledge base (documents, wikis, tickets, policies) that your team or customers query the same questions against repeatedly.
02
Accuracy and citations are non-negotiable, and a plausible-but-wrong answer creates real liability in support, compliance, legal, or healthcare.
03
Your knowledge changes often, so retraining a model on it is impractical, and budget for a build from $15,000.
Not a fit
- You need to change the model's tone, style, or output format rather than ground it in facts, which is a fine-tuning job.
- Your knowledge fits in a single prompt and rarely changes, so a plain LLM call already covers it.
- The answers depend on judgment or context that lives in no document the system can retrieve.
What we build
What our RAG development service covers
- 01Enterprise knowledge searchInternal search tools that let employees ask questions in natural language and receive accurate, cited answers drawn from company documentation across Confluence, SharePoint, Notion, and Google Drive, not from the LLM's general training. Every response cites the exact passage with a deep link to the source.
- 02Document Q&A systemsSystems that answer questions from specific document sets where accuracy is non-negotiable: contracts, compliance manuals, technical specifications, and research reports ingested from PDF, Word, and scanned files via OCR. Answers include inline citations naming the exact document and section, with confidence displayed for contract questions a legal professional should verify.
- 03Customer support knowledge basesRAG-powered support knowledge bases across Zendesk, Freshdesk, and Intercom, deployed in two modes: agent assist, surfacing relevant articles beside the ticket, and a customer-facing chatbot with a human escalation path. Low-confidence or out-of-scope queries route to an agent with the conversation context pre-populated.
- 04Multi-source retrieval pipelinesRAG 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 dense vector search, BM25 full-text search, databases, and live APIs, merged with Reciprocal Rank Fusion, and every claim in the response is attributed to its source.
- 05Compliance and policy assistantsCompliance 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, and every query, source, and response is logged to an immutable audit record.
- 06Code and technical documentation searchRAG systems over codebases, API documentation, and runbooks that answer engineer questions from your actual source, not the LLM's general programming knowledge. On-call questions return the exact runbook steps, with retrieval over OpenAPI specs and optional IDE integration inside VS Code or JetBrains.
The stack we build RAG systems on
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.
Related RAG capabilities
- 01
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.
- 02
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.
What does your team need accurate answers from?
Tell us the knowledge sources and the query types. We'll design the RAG architecture and give you a fixed cost.
How it works
The RAG pipeline we build
- Step 0101
Ingestion and indexing
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.
- Step 0202
Retrieval and re-ranking
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.
- Step 0303
Generation with guardrails
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.
What clients say
What our clients say
Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.
Amer Abu Khajil
CanadaFounder, Peak Studios & Perceptional
I found RaftLabs to be the perfect partner for Perceptional, with their expertise in helping startup founders build MVPs, a free consultation, a prototype that matched my vision, and their unwavering support.
01 / 02
Proof
Case studies
What RAG development costs
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. Where you land depends on scope:
- Focused RAG system, $15,000-$40,000
- One or two knowledge sources and a single use case, scoped, built, and deployed, in 4 to 8 weeks.
- Multi-domain enterprise RAG, $45,000-$120,000
- Custom connectors, document-level access controls, and a product interface across multiple knowledge domains, in 10 to 16 weeks.
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.
What it costs
Custom RAG systems, starting at $15,000.
A working demo in the first 2 weeks, then a production RAG system with the connectors, access controls, and guardrails it needs to run reliably.
Starts at $15,000
Scoped to your data sources and access-control needs. You'll see a working demo in the first 2 weeks, before committing to the full build.
Start with one or two clean data sources to prove retrieval quality, then expand to the rest once the demo earns your trust.
No hourly billing
Once we scope the build, that price is locked in writing. A scope change is a priced change request, agreed before work begins, never quietly folded into the invoice.
Prove it first
A working demo in the first 2 weeks, tested for accuracy against a set of ground-truth question-answer pairs, so you validate retrieval quality before committing to the full build.
Stay on topic
More on RAG & knowledge management
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Cost to Build a Productivity App Like Notion: Timeline and What You Actually Need
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Why RAG Systems Fail (and How to Tell if Yours Will)
Most RAG projects that stall don't fail because the model is weak. They fail in retrieval, data quality, and evaluation. Here are the failure modes we see most, what each one costs, and how to fix them before launch.
Read moreFrequently asked questions
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
Tell us what you need. We'll tell you what it would take.
We scope RAG Development Services in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.
- Scope and cost agreed before work starts. No surprises. No obligation.
- Working prototype within 3 weeks of kickoff.
- Pay by milestone. You see progress before each invoice.
- 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
- All conversations are NDA-protected.