Retrieval-Augmented Generation (RAG) Services That Eliminate AI Hallucinations

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

• 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 (inject data at inference). 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 / 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.

Yes — 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 keep reliability high.

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 re-chunking 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.