Retrieval-Augmented Generation (RAG) Services

RAG combines a large language model (LLM) with a vector database. At query time:

• Retrieves the most relevant company documents
• Injects that context into the prompt
• Generates a grounded answer with citations

This lowers hallucinations, improves accuracy, and builds trust for enterprise use cases.

Choose RAG when you need fresh, governed knowledge without retraining. Choose fine-tuning when you must teach new behaviors or a specific style that isn’t achievable with prompts. Many teams start with RAG for speed and add light fine-tuning later for tone or task-specific improvements.

Typical wins include self-serve support from manuals/knowledge bases (24×7), 30–60% faster ticket resolution with cited answers from policies/CRMs, higher CSAT, and reduced escalation volume — all while keeping proprietary data controlled.

Proven patterns: LLMs like GPT-4o, Claude 3, or Llama 3; vector DBs such as Pinecone, Weaviate, or Postgres+pgvector; orchestration via LangChain or LlamaIndex; and platform ops with Kubernetes + ArgoCD + Vault for scaling, CI/CD, and secrets. Choice depends on latency, scale, and cloud preferences.

Compliance is achieved through VPC or on-prem deployment, encryption in transit/at rest, RBAC + audit logs, PII redaction, and policy guardrails. Controls are aligned to GDPR, HIPAA, and SOC 2 requirements for your environment.

Costs vary by data size, users, and deployment. As a guide: MVP ≈ $75k for ~10 weeks. Scale-up costs are usage-based (vector storage, inference, ops). Many clients see 5–10× lower TCO vs repeated fine-tuning because knowledge updates don’t require retraining.

Yes. With multilingual embeddings (e.g., Cohere Embed-v3, BGE-Large) a single index can support 100+ languages, so users can query in their language and receive accurate, cited answers.

Monitor precision/recall, citation match, latency (p95), and cost-per-query. Use automated evaluation pipelines and dashboards that tie these metrics to business KPIs — many teams see 3–6× productivity gains in the first quarter.

Not fully eliminated, but grounding answers in vetted sources typically reduces hallucinations by ~80%. Add guardrails (confidence thresholds, policy checks) and human-in-the-loop review for high-risk actions to maintain reliability.

Typical cadence: Discovery & data prep 1–2 weeks → Pilot (MVP) 3–4 weeks → Production hardening & rollout 4–6 weeks. Most programs complete in ~10–12 weeks with targets for high availability.

Best: well-structured policies, knowledge bases, product docs, CRM cases, and wiki content. Avoid duplicative, outdated, or poorly governed content. De-dupe, version, and set permissions before indexing to keep answers clean and compliant.

Hot content often needs near-real-time updates; other content can be refreshed nightly or weekly. Use change-data capture and scheduled rechunking when document structures evolve to maintain recall without index bloat.

Pinecone — fully managed, low-latency; Weaviate — flexible OSS/managed hybrid; Postgres+pgvector — great if you already standardize on Postgres. Benchmark with your data and latency targets before committing.

Larger chunks reduce retrieval calls but risk irrelevant context; smaller chunks improve precision but increase token/call counts. Tune chunk size, overlap, and embedding model (e.g., BGE-Large vs smaller models) against an eval set to optimize F1 and cost-per-answer.

Yes. We can deploy in your VPC or on-prem (air-gapped if required) using open-source LLMs, local embeddings, and private vector stores so all traffic and storage stay inside your security boundary.

Pilot deliverables: data connectors, curated index, baseline eval suite, admin dashboard, and a limited-scope assistant. Success criteria: target precision/recall, p95 latency threshold, and user adoption/CSAT targets.

Our RAG specialists operate from California (Agoura Hills), Nodia (India), and Dubai (UAE). We assign teams based on your timezone and compliance needs. North American clients typically work with US-based data architects for discovery workshops and Indian engineers for vector database setup and deployment. All three offices deliver full RAG development, from document ingestion and chunking strategies to production retrieval pipelines with 24/7 monitoring.

We start with a two-week discovery to map your knowledge sources and define retrieval goals. Then we build the RAG infrastructure: Clean and chunk your documents (PDFs, wikis, CRMs) Set up vector databases (Pinecone, Weaviate, or Postgres+pgvector) Create embeddings using models like BGE-Large or Cohere Configure semantic search with hybrid retrieval Next comes integration. We connect your LLM (GPT-4, Claude, or Llama) with orchestration tools like LangChain, add citation tracking, and deploy via REST APIs on AWS, Azure, or GCP. Post-launch, we monitor retrieval accuracy, optimize costs, and retrain embeddings as your knowledge base grows.

Business application development costs are driven by the scope of functionality, system architecture, integration complexity, security compliance requirements, and scalability planning.

Typical investment ranges include:

• MVP: US $50,000 – $100,000 (core features to validate business value)
• Medium-scale applications: US $100,000 – $250,000 (advanced functionality, integrations, and scalability)
• Large / Enterprise-grade solutions: US $250,000 – $500,000 (complex architectures, high security, and enterprise integrations)

We collaborate closely with your team to fully understand your business goals and technical needs, enabling transparent pricing and a well-defined delivery plan. Our development approach prioritizes scalability, security, and performance to ensure your application delivers lasting value as your business grows. Feel free to schedule a call to discuss your requirements and define a customized development plan.