ScalaCode builds and deploys production retrieval-augmented generation systems, vector pipelines on Pinecone, Weaviate, Qdrant, and pgvector; hybrid retrieval over enterprise documents; LLM grounding on proprietary data , for clients across 45+ countries. With 13+ years of search and information-retrieval experience, our teams take RAG from notebook prototype to production knowledge platform, with the eval harnesses, citation guarantees, and update workflows that enterprise content demands.
Whether you need a customer support assistant that answers from 50,000+ pages of product documentation with sources cited, a contract intelligence system that retrieves relevant clauses across thousands of agreements, or a sales-enablement bot grounded in your full case-study library, our RAG engineers architect solutions that move the metrics that matter , answer accuracy, hallucination rate, time-to-first-resolution.
Our RAG development services cover the full lifecycle , from knowledge source auditing to production monitoring. We build both the retrieval layer and the generation layer as one integrated system, because in RAG, retrieval quality is the ceiling on generation quality.
Before a single vector is embedded, we audit your knowledge sources (docs, wikis, PDFs, SharePoint, Confluence, CRM, data lakes, structured DBs, APIs) and map them to retrieval strategies. We deliver a reference architecture, model selection matrix, cost-per-query forecast, and a phased rollout plan aligned with your compliance posture.
We build custom RAG systems in Python (LangChain, LlamaIndex, Haystack), Node.js (LangChain.js, LlamaIndex.TS), and Go. Every pipeline includes chunking strategy, embedding pipeline, vector storage, retrieval policy, reranker, prompt orchestration, response validation, and observability.
We design vector stores on Pinecone, Weaviate, Qdrant, Milvus, Chroma, and open-source pgvector/PostgreSQL. Includes chunking strategy (fixed-size, semantic, recursive, proposition-based), metadata schema, namespace partitioning for multi-tenant isolation, and hybrid search with BM25 + dense vectors.
Pure vector search rarely wins in production. We implement hybrid sparse-dense retrieval (BM25 + embeddings), query expansion, HyDE (Hypothetical Document Embeddings), Multi-Query Retriever, late interaction (ColBERT-style), and contextual retrieval , then route queries dynamically based on intent classification.
For complex, multi-hop reasoning, we build GraphRAG systems on Neo4j, Microsoft GraphRAG, or custom knowledge graphs , where retrieval traverses entity relationships instead of fetching isolated chunks. For autonomous workflows, we build agentic RAG where LLM agents plan their own retrieval strategies using tools and self-critique. See our AI agent development services for the agent layer.
We integrate cross-encoder rerankers (Cohere Rerank, bge-reranker, Jina Reranker, voyage-rerank, in-house fine-tuned models) to reorder retrieval results before they hit the LLM. Reranking typically lifts answer accuracy 15 to 40% over embedding-only retrieval at a marginal latency cost.
We build evaluation harnesses using RAGAS, TruLens, DeepEval, and custom LLM-as-judge pipelines , scoring faithfulness, answer relevance, context precision, context recall, and answer groundedness. Your RAG system gets nightly regression tests, golden-set benchmarks, and production drift alerts.
We integrate RAG into your existing enterprise systems , CRM, ERP, ticketing, DMS, HR, e-commerce , using our AI integration services patterns. Deployment options span AWS Bedrock, Azure AI, GCP Vertex AI, private cloud, on-premises, and air-gapped environments for regulated workloads.
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Chunk, embed, vector search, generate. Fine for demos, weak in production , no reranking, no metadata filtering, no evaluation. We use this only as a baseline to measure improvement from advanced patterns.
Hybrid sparse-dense search + metadata filtering + cross-encoder reranker + context compression. The default 2026 enterprise baseline. Typically 20 to 40% accuracy lift over naive RAG.
Retrieval, reranking, and generation split into swappable modules with their own routing logic, orchestrated via a control layer. Enables A/B testing at each stage and evolving models independently.
Knowledge graph replaces or augments the vector store. Retrieval follows entity relationships (who-knows-who, what-depends-on-what) instead of semantic similarity alone. Ideal for complex reasoning, contract analysis, org hierarchies, drug interactions, and multi-document synthesis.
LLM agents plan their own retrieval: decompose the question, decide which tools to call, re-query if confidence is low, and synthesize across sources. Uses MCP (Model Context Protocol), function calling, and ReAct loops. Best for open-ended research, multi-source synthesis, and workflows that need to escalate to humans on uncertainty.
A lightweight retrieval evaluator scores retrieved context before generation. On low-relevance retrievals, the system triggers web search, query rewriting, or decomposition. Reduces hallucinations in long-tail queries.
The LLM emits reflection tokens to decide whether retrieval is needed, how many documents to fetch, and whether the final response is grounded. Produces higher-quality answers on tasks where retrieval overhead should be conditional.
Chunks are augmented with LLM-generated context (a one-sentence summary of what the chunk is about relative to the whole document) before embedding. Anthropic’s 2024 benchmark showed 35 to 50% reductions in retrieval failure rate.
RAG is a building block, not the full stack. These capabilities compose naturally with RAG systems we build.
When RAG powers user-facing chat experiences that need dialog management on top.
Need RAG engineering talent on your own roadmap? We staff dedicated specialists who plug into your workflow , with minimum 18 months of RAG-in-production experience each.
Most RAG prototypes fail in production for the same reasons: poor chunking, weak evaluation, no reranker, no metadata filtering, and no observability. Our engineering method addresses all five before a single user ever hits the system.
We catalog every knowledge source , volume, velocity, update frequency, access control, PII density, jurisdictional constraints. Structured data (tables, CRM records) and unstructured data (PDFs, wikis, email) are handled through different pipelines. Skipping this step is the single biggest predictor of RAG failure.
Chunking decisions , fixed-size, semantic, recursive, proposition-based, hierarchical , are made per knowledge source, not one-size-fits-all. Embedding models (OpenAI text-embedding-3-large, Cohere embed-v4, Voyage, open-source bge-m3, E5, Arctic, Nomic) are benchmarked on your domain before selection.
Metadata is the difference between “semantic fuzziness” and “precise retrieval”. We design rich metadata schemas (source, author, department, date, confidentiality level, revision ID, entity tags) and use metadata filtering at query time to constrain retrieval before similarity search runs.
Based on query patterns, we route traffic between vector-only retrieval, hybrid sparse-dense, multi-query expansion, HyDE, small-to-big retrieval, parent-document retrieval, and graph traversal. Query classifiers decide the route in <50ms.
Top-K candidates pass through a reranker (cross-encoder or LLM-based), then a context compressor (LLMLingua, LongLLMLingua, or structured extraction) before being fed to the generation model , improving signal-to-noise and reducing token costs.
Prompts are structured with citation requirements, format constraints, and refusal triggers (“if you don’t know, say so”). Responses are validated for groundedness (every claim cites retrieved evidence) and hallucination is detected via entailment scoring before the user sees the output.
Every query is logged with retrieval scores, reranker scores, final context, and LLM response. Dashboards surface retrieval failures, low-confidence responses, and semantic drift. A weekly regression suite runs golden-set benchmarks , and alerts when faithfulness or answer relevance drops below threshold.
Most RAG vendors stop at the Jupyter notebook. We start by asking “what does 99.5% uptime at 500 QPS look like?” , then engineer backward. Every system ships with observability, evaluation harnesses, and runbooks on day one.
Our engineers specialize in retrieval: BM25, cross-encoder rerankers, HyDE, ColBERT, GraphRAG traversal, late interaction. The generation layer is downstream of retrieval quality , and we optimize the ceiling, not the floor.
Legal RAG is not retail RAG is not medical RAG. We adapt chunking, embedding selection, reranker fine-tuning, and prompt structure to your domain vocabulary, query patterns, and defensibility requirements.
Private cloud, on-premises, air-gapped, and BYO-key deployments. SOC 2 Type II, HIPAA, GDPR, DPDP-ready architecture. Your data never trains a foundation model unless you explicitly opt in.
Every answer from our RAG systems shows its sources. Users can click through to the underlying chunk, document, or graph node. This is non-negotiable for regulated workloads and builds user trust in every domain.
We ship a golden-set evaluation use with every project , typically 200 to 500 Q&A pairs graded by your subject-matter experts. No system goes to production without passing your acceptance bar, measured quantitatively.
RAG delivers the highest ROI in industries with large, regulated, frequently-updated knowledge bases. Below are the segments where we have production deployments live today.
Research co-pilots over 50k+ pages of market analysis, compliance Q&A against FINRA/SEC/MiFID rulebooks, credit memo generation from 10-K filings, and internal audit assistants. Paired with strict citation requirements for regulatory defensibility.
Medical literature co-pilots over PubMed and internal clinical libraries, protocol adherence checks, pharmacovigilance signal surfacing, and HCP content compliance. All deployments are HIPAA-aligned with PHI isolation.
Contract analysis and drafting assistants, matter-specific research co-pilots, and policy Q&A over internal legal libraries. GraphRAG is standard here , legal reasoning is inherently relational (precedents, citations, clauses).
Technician co-pilots over equipment manuals, root-cause analysis over maintenance logs, and SOP guidance in the field. Edge RAG deployments for offline shop-floor access.
Internal knowledge search over Confluence, SharePoint, Google Drive, Notion, and Slack archives. External support assistants that resolve tier-1 tickets with citation-backed answers , reducing ticket volume 30 to 55%.
Product discovery assistants over catalogs, review synthesis, visual + text hybrid search, and merchandising copilots for buyers. See our AI recommendation engine services for complementary personalization.
Claims processing assistants, policy Q&A, underwriting co-pilots, and fraud pattern surfacing. Tight integration with enterprise AI integration patterns for policy administration systems.
Fixed-scope audit of your knowledge sources, competitive benchmark, reference architecture, cost model, and phased roadmap. Typical starting investment: $15k-$40k. Deliverable: an implementation-ready blueprint, whether you build with us or in-house.
Production-grade RAG pilot on one narrow use case, with evaluation use, observability, and stakeholder acceptance testing. Includes 2 iterations based on SME feedback. Outcome: a working system you can demo to your board with real metrics.
End-to-end RAG system for enterprise-scale knowledge bases. Includes ingestion pipelines for multiple sources, multi-tenant isolation, production hardening, SOC 2 alignment, runbook documentation, and on-call coverage for the first 90 days.
A dedicated squad (RAG architect, retrieval engineer, MLOps engineer, prompt engineer, QA) embedded with your team for 6+ months. We scale up or down based on your roadmap. Ideal for organizations building RAG as a platform capability, not a point solution.
We operate your RAG system post-launch: model upgrades, index refreshes, evaluation monitoring, retrieval drift detection, cost optimization, security patching. SLA-backed.
We are model- and vendor-agnostic. The stack below represents the full production-grade toolkit we deploy from , specific selections are driven by your latency, cost, compliance, and sovereignty requirements.
Representative anonymized outcomes from recent ScalaCode RAG engagements.
Compliance Q&A assistant over 80k+ pages of regulation. Answer accuracy 91.4% on golden-set benchmark. 62% reduction in compliance analyst research time.
Medical literature co-pilot over PubMed + 40k internal study reports. Retrieval precision lifted from 52% to 88% after switching from naive RAG to hybrid + rerank + GraphRAG.
Policy Q&A assistant deflecting tier-1 support tickets. 47% ticket deflection in month 3, rising to 58% by month 6 after reranker fine-tuning.
Technician co-pilot over equipment manuals and maintenance logs. Mean time to resolution down 34%; first-time fix rate up 22%.
GraphRAG-based product knowledge assistant for internal sales and CS. Sales rep ramp time cut by 40%, deal-desk response time cut by 65%.
RAG is an architecture that pairs a large language model with a retrieval system over your own data. At query time, the system fetches relevant chunks from your knowledge base, passes them to the LLM as context, and generates an answer grounded in your sources. Enterprises use RAG to make LLMs accurate on proprietary data, reduce hallucinations, enable citation-backed responses for regulated use cases, and avoid the cost and complexity of fine-tuning large foundation models.
Fine-tuning modifies a model’s weights to teach it a domain or behavior. RAG keeps the model as-is and instead retrieves relevant context at query time. RAG is faster to iterate (no retraining), cheaper at low-to-moderate scale, keeps knowledge current (just re-index), and avoids catastrophic forgetting. Fine-tuning wins when you need domain vocabulary, consistent tone, or low-latency inference at massive scale. Most production systems use both , fine-tuned models inside RAG pipelines.
GraphRAG retrieves from a knowledge graph (entities + relationships) instead of , or in addition to , a vector store. Use it when answers require multi-hop reasoning (e.g., “which regulations affect this contract given this counterparty’s jurisdiction?”), when your domain is inherently relational (legal precedents, org hierarchies, drug interactions), or when chunk-level retrieval loses context that only entity relationships preserve. GraphRAG is often hybridized with vector search for best results.
Discovery and architecture sprints start at $15k-$40k. A production pilot on one use case typically runs $60k-$150k over 6 to 10 weeks. A full enterprise-scale RAG platform ranges $200k-$800k+ depending on number of knowledge sources, compliance requirements, and expected query volume. Ongoing run costs (embedding, vector storage, generation, reranking, observability) typically range $0.005-$0.05 per query at scale, with heavy optimization opportunities as volume grows.
No single answer , it depends on scale, sovereignty, hybrid search needs, and existing stack. Pinecone is the lowest-operations managed option for teams that want zero infra work. Weaviate offers strong hybrid search and self-hosting flexibility. Qdrant is excellent for self-hosted deployments with rich filtering. pgvector on PostgreSQL is ideal when your data is already in Postgres and query volume is moderate. Milvus handles the largest deployments. We benchmark your query patterns across candidates before recommending.
Hallucination prevention is a layered defense: (1) strong retrieval quality via hybrid search + reranking so the LLM gets the right context, (2) prompt engineering that requires citations and permits “I don’t know” refusals, (3) entailment checks that validate every claim against retrieved context, (4) groundedness scoring via RAGAS or LLM-as-judge, (5) context compression to remove noise, and (6) production monitoring that flags low-confidence responses. No single layer is sufficient , hallucination is a systems problem.
Yes. We routinely deploy RAG in private cloud, on-premises, and air-gapped environments using open-source embedding and generation models (Llama, Mistral, Qwen, bge-m3), self-hosted vector stores (Weaviate, Qdrant, Milvus, pgvector), and internal observability stacks. All components can run without any egress to third-party APIs. This is standard for financial services, healthcare, defense, and government customers.
We measure four core dimensions: (1) context precision , what fraction of retrieved chunks are relevant; (2) context recall , what fraction of needed information was retrieved; (3) faithfulness , do answers stay grounded in retrieved context without fabrication; (4) answer relevance , does the response address the actual question asked. Tools: RAGAS, TruLens, DeepEval, LangSmith, and custom LLM-as-judge evaluators. A production-quality RAG system typically scores >0.85 on faithfulness and >0.80 on context precision against a domain-specific golden set.
Standard RAG follows a fixed retrieve-then-generate flow. Agentic RAG gives an LLM agent the ability to plan retrieval: decide whether retrieval is needed, which tools to call, how to decompose complex questions, when to re-query with better parameters, and when to stop. It uses function calling, MCP (Model Context Protocol), and ReAct-style reasoning loops. Agentic RAG shines on open-ended research, multi-source synthesis, and workflows that must escalate to humans under uncertainty. It is more expensive per query but dramatically better on complex tasks.
A focused, single-use-case RAG pilot typically reaches production in 8 to 12 weeks: 2 weeks discovery and architecture, 4 to 6 weeks build, 2 weeks evaluation and hardening. Enterprise-scale platforms with multiple knowledge sources, compliance certification, and multi-tenant architecture typically run 4 to 6 months end-to-end. The highest-velocity teams we’ve worked with moved from kickoff to first business value (not full rollout) in 5 weeks by scoping tightly and accepting a v1 that intentionally excluded the long tail.
We had unique integration needs, ScalaCode handled them expertly and delivered a seamless, functional product.
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