CodeXcelerate
CodeXcelerate
// ai service
Most RAG demos look impressive — until they hit your real data. The wrong chunks get retrieved, the model hallucinates, edge cases fail. CodeXcelerate builds production-grade RAG systems that work on your actual documents, databases and knowledge bases — using hybrid search, re-ranking, query rewriting and metadata filtering to achieve 85%+ retrieval accuracy from week one. We've shipped RAG systems for law firms, SaaS companies, e-commerce platforms and enterprise internal knowledge bases. RAG development starts from $5,000.
// sound familiar?
// who it's for
// what's included
// outcomes
// why codexcelerate
The most common RAG failures: chunks are too large or too small, retrieval returns tangentially related content, the model ignores the retrieved context, or edge case queries return nothing. We use hybrid search, re-ranking and query rewriting to handle all of these — and we build an evaluation framework from day one so we can measure when and where the system fails.
Pure vector search misses exact keyword matches — product codes, names, technical terms. Pure keyword search misses semantic equivalents. We combine both with weighted fusion, plus metadata filtering and re-ranking, so the right chunks surface for the widest range of query types. This is why our RAG systems perform well on production data where basic implementations fail.
Most RAG implementations are tested by hand — someone asks ten questions and the answers look okay. We build a test suite of representative questions with expected answers and run RAGAS evaluation metrics (context precision, context recall, answer faithfulness) automatically. You know your retrieval quality as a number, not as a gut feel.
Senior RAG engineering in the US costs $150–$250/hr. Our team — LangChain, LangGraph, pgvector, Pinecone, Weaviate, hybrid search, re-ranking — bills at $45–$65/hr with Western-standard delivery, English communication, NDA before work starts, and 3–4 hours daily timezone overlap.
// comparison
| What we compare | CodeXcelerate | Typical agency |
|---|---|---|
| Retrieval accuracy on production data | 85%+ via hybrid + re-ranking | Often 50–60% with basic vector search |
| Hybrid search (vector + keyword) | Yes — weighted fusion | Often vector-only |
| Re-ranking | Cross-encoder re-ranking included | Rarely included |
| Built-in evaluation (RAGAS) | Precision, recall, faithfulness metrics | Typically none or manual only |
| Citation and source tracking | Every answer cites source + page | Often omitted |
| Senior engineer rate | $45–$65/hr | $150–$250/hr (US/UK agencies) |
// tech we use
// process
We map your goals, users and constraints into a clear product brief and fixed scope.
We design the experience and architecture before a line of code ships.
We build in agile sprints with weekly demos and full transparency into progress.
We ship to production with testing, monitoring and a smooth rollout plan.
We stay on as your team — iterating, optimizing and scaling with you.
// client reviews
"CodeXcelerate took TradeBeep from just an idea to a launched product — the app, the admin panel, the landing page, and even our marketing. Having one team handle the build and the growth made everything seamless."
Oluwole Kayode
Founder, TradeBeep
"They built Snap To Let end to end, from concept to launch, and the quality was excellent. We trusted them so much that we're now building a second product with them."
Geetha Nath
Founder, Snap To Let
// related work
// faq
RAG is the technique that makes AI accurate on your proprietary data. Instead of relying on a language model's pre-trained knowledge (which may be outdated, hallucinated, or irrelevant), a RAG system first searches your documents for relevant content, then feeds that content to the LLM as context for generating an answer. The result: AI that answers from facts in your data rather than making things up. RAG is the foundation of every AI knowledge base, internal search assistant, or document Q&A product.
Tell us what you're building and get a free, no-obligation quote — we reply within 4 hours.
