Primary research for the answer-engine era, our most-cited piece.
Five constraint numbers locked before build. Six stages from discovery to hand-off.
RAG as a service is a managed way to build and run retrieval augmented generation, so your AI answers from your own documents, data and policies with inline citations and permission-aware retrieval, and only shows each user what they are allowed to see. As a RAG development company founded in 2017, with 200+ in-house experts and a 90-day median to first deployment, we ship a measured pipeline you can trust, not a demo that hallucinates.
Retrieval augmented generation (RAG) is a pipeline that, for each user question, searches a curated knowledge base, selects the most relevant passages, and passes them to a language model as grounding, so the answer is based on your real content rather than the model's memory. RAG as a service wraps that pipeline as a managed offering: ingestion, chunking, embeddings, a vector or hybrid search index, reranking, prompt assembly, citations, access controls and evaluation, built and run for you. You need it when answers must cite a trusted source, respect who can see what, and stay current as your data changes.
Done well, RAG grounds answers in private or fast-changing data without retraining a model every time that data changes. You update the index, and the system reflects it. That makes it the practical default for support assistants, internal knowledge copilots, policy and compliance lookups, and document question answering, where accuracy and traceability matter more than open-ended creativity.
Demand is real and rising: market researcher MarketsandMarkets values the RAG market at USD 1.94 billion in 2025 and projects USD 9.86 billion by 2030, a 38.4 percent CAGR (MarketsandMarkets, 2025).
Canonical numbers from our delivery history, not projections.
In our experience the demo is the easy 20 percent. Pilots stall on the unglamorous parts: messy source documents that chunk badly, retrieval that returns plausible but wrong passages, stale indexes after the data changes, no way to enforce who can see what, and no evaluation to prove the system is actually correct. We treat those as the core of the engagement, not an afterthought.
We engineer the pipeline to a measurable bar before anyone calls it done.
How we close the gap →Connectors for files, wikis, ticketing systems, databases and web sources, with layout-aware parsing for PDFs and tables, plus chunking strategies tuned per document type so passages stay coherent and retrievable.
We select and benchmark embedding models for your domain and stand up a vector index sized to your corpus and latency budget, with metadata filtering for scoped retrieval.
Dense vectors combined with keyword and BM25 search to catch exact terms and rare entities, then a cross-encoder reranker promotes the passages that truly answer the question.
Prompt assembly that constrains the model to the retrieved context, returns inline source citations, and abstains or escalates when grounding is weak instead of guessing.
Per-user and per-tenant access controls applied at retrieval time so the index never surfaces a document the requester is not entitled to see, with audit logging on every query.
Automated retrieval and answer evals on a curated test set, plus tracing of every query, retrieved chunks and token cost so regressions surface before users do.
Every question runs the same RAG pipeline. Every question runs the same governed path, from retrieval to a cited answer, with an evaluation loop that keeps quality from drifting.
A RAG answer is only as trustworthy as the sources behind it, so we make the grounding visible and checkable rather than asking users to take the model on faith.
See the method →Illustration of how this works in practice, under guardrails and human checkpoints.
The page tells the RAG story with motion: a scroll-scrubbed pipeline rail that ignites node by node, bespoke animated vizzes for each capability cluster, count-up stats that rest on real values, and 3D-tilt cards. Everything ships with a reduced-motion fallback and is verified for zero horizontal overflow from 390 to 2560 pixels.
Deflect repetitive tickets with an assistant grounded in your help center, policies and product docs, answering in your voice with citations and escalating cleanly when it is unsure.
Give employees one place to ask across wikis, runbooks and policy libraries, with permission-aware retrieval so each person only sees what their role allows.
Search long contracts, filings and regulatory texts and get answers tied to the exact clause, so reviewers verify in seconds instead of reading end to end.
The same operating discipline runs every build: the numbers locked before we start, an eval suite that has to pass, quality gates on every change, and a hand-off engineered from day one.
Read the full method →We map your sources, sample the documents, and define the questions the system must answer and the bar it must hit, so success is measurable before we build.
We build connectors, parse and chunk the corpus, generate embeddings, and stand up the vector or hybrid index with metadata for scoped retrieval.
We benchmark embeddings, add hybrid search and reranking, and tune chunking and filters against a curated test set until retrieval quality clears the target.
We assemble prompts that constrain the model to retrieved context, wire inline citations, and add abstain and escalation paths for weak grounding.
We automate retrieval and answer evals, add tracing, cost controls and permission enforcement, and red-team the system against edge cases.
We ship to production behind your SSO and infrastructure, set up monitoring and a refresh pipeline, and hand over runbooks and the eval suite.
Start small and prove value, or bring in a full pod. Either way the same engineers who scope the work lead the build.
A focused engagement to take one high-value use case from corpus to a cited, evaluated assistant in production, on a fixed scope.
A cross-functional team building a reusable retrieval platform across multiple corpora and use cases, with shared ingestion, evals and governance.
Senior retrieval and LLM engineers who join your team to accelerate an in-flight build, with the option to scale up or down by sprint.
Tell us your use case and we will scope the right engagement. Or hire AI engineers for your own roadmap.
Answered the way we would on a scoping call.
A RAG development company designs and builds retrieval augmented generation systems: software that answers questions by first retrieving relevant passages from your own data and then having a language model respond from those passages, with citations. The work spans data ingestion, chunking, embeddings, vector or hybrid search, reranking, prompt design, access controls and evaluation, which is more engineering than wiring a single API call.
Fine-tuning changes a model's weights to adjust its style or teach narrow tasks, and it must be repeated when your data changes. RAG leaves the model as is and instead retrieves your current data at query time, so updating the index updates the answers. RAG is the better fit when facts change often or must be cited; fine-tuning suits stable tone or format needs. Many production systems use both together.
RAG sharply reduces hallucination by grounding answers in retrieved sources and showing citations a reviewer can check, but no system is perfect. We add safeguards that matter: reranking to improve which passages reach the model, prompts that constrain it to the retrieved context, and an abstain-or-escalate path so the assistant says it is unsure instead of guessing when grounding is weak.
Yes. We apply permission-aware retrieval, meaning access controls are enforced at search time so the index never returns a document the requesting user is not entitled to see. We implement this with role-based access control, per-tenant indexes or row-level security depending on your setup, and we log every query for audit.
We choose per project based on your corpus size, latency budget and infrastructure. Common choices include pgvector for teams already on Postgres, and Pinecone, Weaviate, Qdrant or Milvus for dedicated vector search, often paired with Elasticsearch or OpenSearch for hybrid keyword retrieval. Orchestration uses LangChain or LlamaIndex, with Cohere or cross-encoder rerankers and Ragas or TruLens for evaluation.
RAG is model-agnostic. We work with frontier models from OpenAI, Anthropic and Google through their APIs, and with open-weight models such as Llama or Mistral when you need on-premises or private deployment. We select the model per use case based on accuracy, latency, cost and data-residency needs, and the retrieval layer stays the same regardless of which model you choose.
We build a curated test set of questions with known good answers and source passages, then score retrieval quality and answer quality with tools such as Ragas and TruLens. We track these metrics through development and after launch, and we trace every production query, including the retrieved chunks and token cost, so regressions surface in monitoring before users notice them.
Yes. A core advantage of RAG over retraining is that you update the index rather than the model. We build a refresh pipeline that re-ingests new and changed documents on a schedule or via event triggers, re-embeds them, and updates the search index, so the assistant reflects your latest content without any model retraining.
Yes. We deploy in your cloud or on-premises environment, behind your single sign-on and network controls, and we can run fully with open-weight models and self-hosted vector search when data cannot leave your environment. Where managed services are acceptable, we use them to reduce operational overhead. The architecture is chosen around your security and compliance requirements.
Our median time from kickoff to a first feature in production is 90 days. A focused proof of value on one use case can reach production faster, while a multi-corpus platform with broad governance takes longer. Timeline depends mainly on the state of your source data, the number of systems to connect, and the accuracy bar the system must clear before launch.
A senior engineer on the call, not a sales pitch. Thirty minutes, your actual use case, a straight answer on feasibility.
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