RAG development company — grounded LLM answers, on your data.
Off-the-shelf LLMs hallucinate. A production RAG system retrieves the right context from your knowledge base — docs, tickets, database rows, code — and grounds every answer in sources. We design, build, and monitor RAG systems that actually hold up in the real world.
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What production RAG looks like
A working RAG system is much more than "pull top-k from Pinecone, stuff into prompt". Real production systems need: a sensible chunking strategy that does not cut context mid-thought; hybrid retrieval (semantic + BM25 keyword + metadata filters) because pure embeddings miss exact-match queries; re-ranking with a cross-encoder to promote the most relevant of the top-50; grounded answer generation that refuses to answer when context is thin; source citations so users can verify; and continuous eval + drift detection. We build all of it, not just the notebook demo.
Stack we use
Vector DBs: Pinecone, Weaviate, Qdrant, pgvector on Postgres. Embeddings: OpenAI text-embedding-3, Cohere, Voyage, BGE. LLMs: Claude (Opus / Sonnet / Haiku), GPT-4/4o, Gemini, open-weights (Llama, Qwen) on Fireworks/Together/Bedrock. Orchestration: LangGraph, LlamaIndex, or custom — depends on project complexity. Eval: Ragas, TruLens, custom test sets. Monitoring: Langfuse, Langsmith, or our own lightweight logger. We pick per use case, not per framework fashion.
Common RAG projects we ship
Customer-support RAG (support agent answers from your docs + past tickets with source links). Internal knowledge search (engineers and sales find the right wiki / Notion page + confluence article instantly). Document Q&A (legal / compliance / research teams query large doc sets). Code RAG (answer questions about your codebase + its history). Product-catalog RAG (semantic + spec-filtered product search for ecommerce). Healthcare / legal / finance RAG with strict source attribution and compliance-grade logs.
Why evaluation is where most RAG projects fail
Most RAG POCs work on a dozen cherry-picked questions and fall apart at 1,000. We invest heavily in evaluation up front: a labelled test set from real user queries, retrieval metrics (recall@k, MRR), answer-quality metrics (faithfulness, answer relevance), and regression tests on every deploy. This is the difference between "impressive demo" and "system that runs in production".
What clients say
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“I worked with them on Upwork. It was about solving a huge problem in my e-comm mobile app. They did a great job by not only solving it but also updating the codebase. I loved it. I highly recommend.”
Reza Babu , CEO, Buynow (Germany) — via Trustpilot
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“I found this company full of energy and packed with passionate developers who served with the highest quality of product, which I exactly wanted. Crafted the solution like their own product. Once will love the collaboration with them.”
Ariful Islam — via Trustpilot
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“Professional Developers, Excellent experience! Thanks for completing the project in time”
Raha — via Trustpilot
Pricing
We offer flexible packages — all through Upwork with payment protection. One-off design tasks from $35/screen (app screens, logos, landing pages — no monthly commitment). Monthly design from $750/month. App development from $1500/month. Full-service (research + design + dev + QA) from $3000/month.
FAQ
Frequently asked questions
- What is RAG?
- RAG (retrieval-augmented generation) is a pattern where an LLM answers a user question by first retrieving relevant context from your own data (docs, database, knowledge base), then generating a grounded answer with source citations — instead of relying purely on the LLM's training data. It is the dominant pattern for "AI on private data" because it is faster, cheaper, and safer than fine-tuning.
- RAG vs fine-tuning — which should I use?
- RAG first, almost always. Fine-tuning is great for style, tone, or structured-output consistency. RAG is better for "answer from current information" — which is what 90% of business AI projects actually need. Fine-tuning on facts is a trap: it bakes stale data into a model that is expensive to re-train.
- How much does a production RAG project cost?
- Simple internal RAG (1 data source, 1 interface): $5,000–$15,000. Production customer-facing RAG (multiple sources, eval, monitoring, guardrails): $15,000–$50,000. Enterprise RAG with strict compliance (audit logs, PII redaction, role-based retrieval): from $50,000. Upwork escrow on everything.
- Can you host it or does it have to be in our cloud?
- Your choice. We can deploy to your AWS/GCP/Azure account (most common for enterprise), or we can run it on our infrastructure with a data-processing agreement. For regulated industries we recommend in-your-cloud deployment with a VPC and private endpoints.
- How do you prevent hallucinations?
- Grounded prompting (the LLM is explicitly told to only answer from provided context and to say "I do not know" when context is thin), source attribution (every answer includes the source chunks so users can verify), re-ranking to surface the most relevant context, and continuous evaluation to catch drift. We test this on real queries before shipping, not just demos.
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