GenAI Product Accelerator
A build track that ships a production RAG feature grounded in your own data, with accuracy evals, safety gates, and cost-per-query tracking.
- Timeline
- 4 weeks (MVP), 6 weeks (production-ready with full evals and monitoring)
- Team
- architect, MLE, FE, BE, QA; security reviewer for prod
- Typical stack
- Vector stores: Pinecone/Weaviate/pgvector with hybrid search. Models: OpenAI, Anthropic Claude, Google Gemini, or open-source (Llama, Mistral). Chunking: semantic splitting with overlap; metadata enrichment. Retrieval: BM25 + dense embeddings; reranking with Cohere/cross-encoders. Observability: LangSmith/Phoenix/custom; cost tracking per query. Deployment: Cloud (AWS/GCP/Azure) or on-prem; API Gateway + auth (OAuth2/API keys). Safety: PII redaction, content filters (Azure Content Safety/Llama Guard), rate limits.
What you get
- Vector pipeline + knowledge ingestion (automated re-indexing)
- RAG orchestration layer with prompt versioning and fallbacks
- Evals suite: accuracy (exact-match + semantic), hallucination gates, toxicity filters
- CI/CD integration with regression gates (accuracy thresholds)
- Observability dashboard: usage, cost per query, latency p95
- Safety monitoring: PII detection, content filters, rate limits
Outcomes
- Working RAG feature in prod with accuracy ≥ target
- Evals dashboard and CI check for regressions
- Usage analytics and safety monitoring
- Measurable user satisfaction or task completion rate
- Cost per query optimized and tracked
Selected work
What we need from you
- 10-50 sample Q&A pairs for evaluation
- Source documents or knowledge base
- Accuracy targets and success metrics
- PII/compliance requirements (HIPAA, SOC2, etc.)
- Existing APIs or systems to integrate
Proof points
- Accuracy scorecard with baseline → production deltas
- Hallucination rate chart (pre-launch vs. 30-day avg)
- Cost per query breakdown and optimization report
- User satisfaction survey results (NPS or task completion)
- Retrieval precision/recall metrics by document type
- Performance SLA adherence report (latency p50/p95/p99)
- Production RAG hallucination rate < 3%
Frequently asked questions
Retrieval-Augmented Generation (RAG) grounds large language model responses in your actual data instead of relying on the model's training data alone. This reduces hallucinations, keeps answers current, and lets you control exactly which documents the model can reference. It is the difference between a chatbot that makes things up and one that cites your internal knowledge base.
We set up eval dashboards that track retrieval precision (did the system find the right documents), answer faithfulness (does the response match the source), and relevance scoring (does the answer address the question). These metrics run continuously against a golden dataset of known-good Q&A pairs specific to your domain.
It means the system handles real user traffic with acceptable latency, has safety gates preventing harmful outputs, includes usage analytics for cost monitoring, provides observability into model behavior, and has a tested rollback path. A demo that works on 10 queries is not production-ready. A system that handles 10,000 queries per day with 99.5% uptime is.
Allerin's GenAI Accelerator delivers a production-ready RAG system in 4 to 6 weeks. That includes data pipeline setup, retrieval tuning, eval framework, safety gates, and deployment with monitoring. The first working prototype is typically ready in week 2.