★★★★★ 4.99 / 5 · 18 reviews · 100% Job Success · Upwork Top Rated

Hire a RAG developer who ships real-world retrieval, not demos.

Everyone's built a RAG demo. Almost nobody's built one that holds up on scanned PDFs, corporate jargon, and users who actually expect correct answers. For Wondercall AI I built the ingestion pipeline (OCR, layout-aware chunking, dedup), hybrid dense + BM25 retrieval with cross-encoder reranking, grounded refusals, and per-tenant isolation of data and model-call budgets.

Hybrid
Dense + keyword + rerank
Grounded
Refusals below floor
Multi
Tenant isolation
Eval
Regression fixtures
Why me specifically

Four concrete reasons.

Ingestion dominates

Most wins in production RAG are in the pipeline before the model. OCR quality, layout-aware chunking, deduplication — each moves retrieval precision more than any prompt change I've tried.

Hybrid retrieval, not pure vector

Vector search loses to keyword search on specific queries. Hybrid dense + BM25 merge, cross-encoder rerank, confidence floors. Shipped, tuned, eval-tracked.

Refusals are a feature

An AI that answers confidently-and-wrong burns user trust forever. Wondercall's grounded refusals reject below a confidence floor. One good refusal is worth ten correct answers.

Citations to the span, not the doc

Useful citations link to the paragraph, not the filename. That means keeping document offsets through chunking, storing them with the vectors, rendering them in the UI — infra work that demos skip and production requires.

What it costs

Real ranges, no back-and-forth.

$20k – $60k

Production RAG pipeline: ingestion, retrieval, reranking, eval fixtures, grounded generation. 6–12 weeks. More for multi-tenant overlay or custom OCR / parser work.

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Client words

What they said after shipping.

“Imad Rashid is not only the best technical professional I've come across in my extensive experience within the technology startup and advisory sphere, but he also consistently exceeds expectations on freelancing and contracting platforms. His impeccable work ethic and attention to detail ensure that every project is completed with the highest standards of testing and quality.”

★★★★★ · Locyal founder · Locyal — Rewards Platform (MVP)

“Working with Imad and his team on our app development was an outstanding experience. Every milestone was met with exceptional attention to detail, and they went above and beyond to ensure the highest quality in their deliverables. Their ability to bring a vision to life and exceed expectations makes them a go-to for any future projects.”

★★★★★ · Startup founder · Full-stack Flutter app
Questions

Asked often enough.

Which vector store?
pgvector for most projects — lives next to the rest of your Postgres data, no separate service to operate, fine up to 10M vectors in practice. Pinecone / Weaviate / Milvus when scale or feature needs demand it.
Which embedding model?
Depends on the corpus and budget. OpenAI text-embedding-3-large for general English, Cohere or Voyage for specific domains, self-hosted (e.g. bge, jina) when cost / privacy matters. I test on your actual data before committing.
MCP?
Yes. Model Context Protocol servers for local and hosted workflows. I have several in production and open-source experiments.
Eval?
Retrieval-first evals: fixtures of queries → known-good source spans, recall@k tracked over time. This catches regressions before users do. End-to-end LLM evals layered on top.
Related

Read deeper.

Next step

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This page targets: RAG developer · RAG pipeline developer · AI tooling developer · LLM + retrieval engineer · vector database developer · MCP server developer
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