Inventiple builds retrieval-augmented generation systems for teams that need their LLM to answer from real, current, private data — not from training data that's already 18 months stale. Hybrid search, reranking, evaluation harnesses, citation tracking, and observability — all in 3-8 weeks by senior engineers, fixed price.
RAG is the most demoed and least production-ready AI pattern of the last three years. The pitch is irresistible: connect a vector database, embed your documents, ask questions, get answers grounded in your data. The demo works. The pilot works. Then real users hit it and the wheels come off.
Generic retrieval misses relevant context. Off-the-shelf semantic search finds documents that are "kind of similar" to the query — not the documents that actually contain the answer. Users ask "what's our refund policy for B2B customers?" and the system returns the consumer refund policy because it's three semantic-distance points closer. Hybrid search, reranking, and query rewriting fix this. Most teams skip them.
No groundedness checks means hallucinations slip through. The LLM is happy to confidently answer questions even when retrieved context doesn't support the answer. Production RAG requires automatic verification that the generated answer is actually supported by the retrieved documents, and a fallback path when it isn't. Without this, the only QA loop is your customers finding wrong answers.
Citations are optional in most implementations. If users can't see which document an answer came from, they have no way to verify it — and you have no way to audit it. Citation tracking has to be architected into the pipeline from day one, not added later as a UI feature.
Evaluation is vibes-based. Most RAG teams change a chunk size, eyeball five queries, declare it "better," and ship. Production RAG requires a labeled regression set, automated evaluation runs on every change, and dashboards that track precision, recall, groundedness, and citation accuracy over time. Without this, you can't tell whether last week's tweak actually improved things or made them worse.
The result, for most teams: a RAG pilot that worked in the demo, an ambiguous middle phase where quality is "fine but not great," and a slow erosion of user trust as customers spot wrong answers. We exist because production RAG is a specialized discipline most teams underestimate.
Our RAG work is structured around one principle: every retrieved passage and every generated answer is measurable, citable, and auditable. The architecture flows from there.
Pure semantic search misses keyword-anchored queries (proper nouns, error codes, dates, IDs). Pure keyword search misses paraphrased queries. We combine both with a reranker on top, which routinely improves retrieval precision by 30-60% over semantic-only baselines on real client data.
A dedicated reranker model (Cohere Rerank, Voyage AI, or open-source equivalents) runs on the top-50 retrieved documents to identify the 5-10 actually relevant ones passed to the LLM. Query rewriting expands ambiguous user queries into multiple search variations. Both are standard in our pipelines and rare in most implementations.
Every retrieved chunk carries a stable document reference through the pipeline. Every generated answer cites the specific passages it drew from. Users see citations inline. You get audit logs showing exactly which document fragments influenced each response. This is non-negotiable for regulated industries and a trust accelerator for everyone else.
Before we tune anything, we build a labeled question set with your team and a regression harness that runs it on every change. Ragas, TruLens, or our internal framework, depending on your stack. Every prompt change, retrieval setting tweak, or model upgrade gets scored. You see actual quality movement, not vibes.
Helicone or LangFuse instrumentation on every retrieval and generation. You see which queries are slow, which retrievals returned weak top-K, which generations got low groundedness scores. When a customer complains about a wrong answer, you can find the exact trace in under a minute.
RAG engagements vary by data complexity, source count, and compliance scope. Here are the shapes we run.
One knowledge base or document store (e.g., your help center, product docs, internal wiki). Hybrid search, reranking, citation tracking, basic evaluation harness, observability dashboard. Best for: teams adding their first production RAG to an existing product or internal tool.
Multiple knowledge bases or data sources with different access controls. Custom retrieval logic, per-source ranking weights, query routing, full evaluation suite, admin dashboard for content team review. Best for: B2B SaaS adding AI-powered search/Q&A across diverse customer data.
Multi-tenant RAG with per-customer data isolation, compliance scaffolding (HIPAA, SOC 2), audit logs, fine-grained access controls, on-prem or VPC deployment. Best for: enterprises rolling out RAG-powered features across business units or regulated industries.
Every engagement starts with a 1-week paid discovery ($5K-$10K, credited against the project price if you proceed). For RAG specifically, discovery includes a data audit — we look at sample documents, sample queries, and existing retrieval pain points before we quote.
Below are fixed-price ranges for each engagement type. We quote after discovery — never hourly. If we underestimate, we eat the cost.
One knowledge base, production-ready accuracy.
Multiple data sources, per-source ranking logic.
Multi-tenant, compliance, on-prem.
Provider-agnostic by design. We pick by fit, not vendor relationship.
RAG (Retrieval-Augmented Generation) is the technique of letting a large language model answer questions using your private data — documents, databases, internal knowledge bases — without retraining the model. A RAG pipeline is the production system that handles ingestion, chunking, embedding, retrieval, ranking, and generation. You need a custom one if your data is large, proprietary, or specialized — off-the-shelf RAG tools fail at scale because they don't understand your data's structure, your accuracy requirements, or your security boundaries.
You can — and the result will work in a demo and fail in production. Default RAG settings produce mediocre retrieval, hallucinations on out-of-context queries, and no observability when things go wrong. Production RAG requires hybrid search (semantic + keyword), reranking, query rewriting, evaluation harnesses, citation tracking, and guardrails. Frameworks like LangChain give you the lego blocks. Knowing how to assemble them for your specific data is the actual job.
Provider-agnostic. We've shipped production RAG on Pinecone, Weaviate, pgvector (Postgres), Qdrant, and Milvus. For embeddings: OpenAI text-embedding-3, Cohere Embed v3, Voyage AI, and open-source models via sentence-transformers when self-hosting is required. We pick based on your data volume, query patterns, latency requirements, and compliance constraints — not because of a vendor relationship.
Five hardened layers on every pipeline we ship: (1) hybrid search combining semantic and keyword retrieval so relevant documents aren't missed, (2) reranking with a dedicated reranker model so the top-K passed to the LLM is actually relevant, (3) citation tracking so every answer points to source documents, (4) groundedness checking that flags answers not supported by retrieved context, and (5) an evaluation harness that runs regression tests on a labeled question set with every prompt or pipeline change. Hallucinations don't disappear, but they become detectable, measurable, and reducible.
A single-source RAG pipeline (one knowledge base, hybrid search, eval harness, basic observability) typically costs $30,000-$50,000 over 3-5 weeks. A multi-source pipeline (multiple knowledge bases, custom retrieval logic, citation tracking, dashboards) ranges $50,000-$100,000 over 6-8 weeks. Enterprise RAG with compliance scaffolding (HIPAA, SOC 2), multi-tenancy, audit logs, and on-prem deployment ranges $100,000-$200,000.
Yes. We routinely build RAG pipelines on top of: Postgres, MongoDB, Elasticsearch, Snowflake, BigQuery, S3 document repositories, SharePoint, Notion, Confluence, Salesforce knowledge bases, and proprietary internal CMS systems. For each source, we build ingestion connectors that respect your existing auth and access controls.
We architect every RAG pipeline so it can run inside your VPC, with no data egress to third parties. For regulated industries we use private model deployments — Azure OpenAI Service, AWS Bedrock with appropriate configurations, or self-hosted open-source models via vLLM. Audit logs of every retrieval and generation, PII redaction at ingestion, and per-user access scoping are standard.
Every pipeline ships with an evaluation harness using Ragas, TruLens, or a custom framework, depending on your needs. We track: retrieval precision and recall, answer relevance, groundedness (how well answers are supported by retrieved context), citation accuracy, and latency. These run on a labeled regression set with every change. You see a dashboard, not a vibes-based 'looks good to me' from your engineering team.
When the data justifies it, yes. For highly specialized domains (legal, biomedical, financial) where off-the-shelf embeddings miss critical context, we fine-tune embedding models on your corpus. This typically adds 1-2 weeks to the timeline and 15-30% retrieval improvement on domain queries. We never recommend it as a first step — most RAG quality problems are fixed by better chunking, hybrid search, and reranking before embedding fine-tuning is justified.
30 days of post-launch support included. Most clients then move to a monthly engineering retainer ($15K-$40K/mo) for ongoing pipeline refinement — eval set expansion, retrieval tuning, new data source integration, model upgrades. RAG quality is never 'done' — it evolves with your data and your users' queries. The retainer keeps it sharp without a full re-engagement.