RAG Development
AlphaCorp AI builds retrieval‑augmented generation (RAG) pipelines that give your language models live access to your private knowledge—so answers are factual, fresh, and safe to share.
Our RAG Development Services are end-to-end: we design your data + chunking strategy, build secure ingestion from your sources (docs, PDFs, databases, web), implement high-accuracy retrieval (hybrid search, reranking, filtering), and ship production-ready APIs with evals, observability, and guardrails—so your team can trust every response.
- Why you should choose RAG
Why RAG Development Services?
If your staff, customers, or partners need trustworthy information fast, RAG is the gold‑standard architecture. Classic large language models guess based on fixed training data. A RAG system first retrieves the latest
information from your documents, database, or API, then generates a response that cites those sources. The
result:
- Features
What We Build With RAG
We wrap the whole stack in monitoring and analytics so you see hit rates, latency, user feedback, and improvement opportunities.
| Component | What it does | Your benefit |
|---|---|---|
| Ingest pipeline | Parses PDFs, Word docs, web pages, and databases. | Unified knowledge base |
| Vector store | Stores text chunks as embeddings for fast similarity search (e.g., Pinecone, Weaviate). | Millisecond retrieval |
| Retriever | Finds the top-N chunks most relevant to the user query. | Precise context |
| Generator | Large language model that crafts the final reply. | Human-level answers With citations |
| API / UI layer | Chat widget, Slack bot, or REST endpoint. | Easy access For users & systems |
- Use Cases
Typical RAG Use Cases
Our RAG Development Process
We handle everything end‑to‑end—code, infrastructure, and ongoing improvements—so your team can focus on the core business.
1. Discovery Workshop
Identify data sources, user goals, and success metrics.
2.Data Ingestion & Cleaning
Convert PDFs, spreadsheets, and websites into clean text chunks.
3.Embedding & Storage
Choose the best vector database for scale and security
4. Prompt & Model Tuning
– Craft prompts and system messages that use retrieved context effectively.
5. Evaluation Loop
Measure accuracy, latency, and hallucination; refine until targets are hit.
6. Deployment & Support
Ship to cloud (AWS, GCP, Azure) with CI/CD, alerts, and dashboards.
- Industries we serve
Roles We Automate
Need something unique? We can create a multi‑agent workforce where each AI employee specializes yet collaborates—as if you just added a whole new department overnight.
Department
- Customer Service
- Sales & Marketing
- Finance & Accounting
- HR & Ops
- Research & Strategy
Sample AI Employee
- 24/7 chat & email agent
- Lead qualifier, content writer
- Invoice auditor, trend forecaster
- Onboarding assistant, shift scheduler
- Data‑gathering analyst
- Security
- Security & Compliance
Built for Your Stack
📥 Ingestion: LangChain, LlamIndex, bespoke scrapers
🧠 Embeddings: OpenAI, Gemini, or Sentence-Transformers
🗄️ Vector DB: Pinecone, Milvus, or pgvector on Postgres
🤖 Model Serving: OpenAI GPT 5.2, Anthropic Claude, or local derivatives
🔀 Orchestration: LangGraph for multi-step flows
🖥️ Backend: FastAPI + Docker + Kubernetes (optional)
Next Steps
📞 Book a free call – Tell us what knowledge your users need.
🗺️ Get a roadmap – We outline data sources, timeline, and cost.
🚀 Launch your RAG system – Go live in weeks, measure impact in days.
Ready to give your users trustworthy answers—fast?
Contact us or schedule a call and let’s build your RAG solution.