Hire a RAG Developer · Miami, FL

Hire a RAG developer in Miami.

A Miami real estate attorney billing at $450 per hour spends 30 minutes per transaction searching a bilingual document library. HOA declarations, condo documents, title searches, and investor communications exist in both English and Spanish. Standard search tools do not cross language boundaries.

Firms handling 50 transactions per month lose 25 hours of attorney time per month to document search alone. At $450 per hour, that is $11,250 in unbillable time. A cross-lingual RAG system reduces document retrieval to under 2 minutes per query.

The same problem appears at Burger King's global HQ, World Fuel Services, and the Latin American-focused financial firms along Brickell Avenue managing cross-border legal documents, and pricing is scoped to the engagement.

How we work

Tell us about your bilingual document challenge.

The bilingual document search problem.

Miami's real estate and financial markets are structurally bilingual in a way no other US market is. A single condominium development in Brickell may have its master declaration in English, its amendments negotiated in Spanish, its HOA correspondence in both, and its investor communications in Spanish for the buyers who purchased from Latin America.

Standard keyword search fails across languages. A query for "right of first refusal" does not retrieve a Spanish document using "derecho de preferencia" even when the two phrases mean the same thing in the context of the transaction. Attorneys either search twice (once per language) or miss documents.

The problem extends beyond real estate. World Fuel Services manages supplier contracts across Latin America. Brickell's private equity and family office firms manage investor agreements and reporting in multiple languages. A cross-border dispute requires retrieving the relevant clause from whichever language version governs.

This is not a translation problem. It is a retrieval problem. The documents already exist. The right answer is in the library. The firm needs a system that can find it regardless of which language the attorney uses to ask the question.

What cross-lingual RAG requires.

A bilingual RAG system is not a standard RAG system with a translation layer bolted on. Several architectural choices must be made correctly at the start.

Multilingual embedding models

English-only models like text-embedding-ada-002 cannot be used. A cross-lingual system requires multilingual-e5 or LaBSE, which embed both languages into a shared vector space. This allows a Spanish query to retrieve an English document when the meaning matches.

Language-aware chunking

Mixed-language documents must be chunked at clause boundaries, not character counts. Each chunk gets a language tag stored as metadata. The reranking step uses this tag to boost language-matched results slightly without excluding the other language.

Jurisdiction metadata for legal content

Miami real estate documents reference multiple legal jurisdictions. Florida HOA law, Miami-Dade county regulations, and the civil codes of buyer origin countries can all appear in the same transaction file. Document-level metadata flags jurisdiction so retrieval can filter when relevant.

Cross-lingual evaluation set

Accuracy must be measured on paired questions in both languages against the same underlying document. We build this test set from your actual attorney queries before deployment. A system that scores 92% on English queries and 71% on Spanish queries is not ready.

What we build.

Document ingestion pipeline for mixed-format libraries

HOA libraries typically include PDFs, scanned documents, Word files, and email attachments. We build an ingestion pipeline that normalizes all formats, runs OCR on scanned pages using Tesseract or AWS Textract, detects language at the chunk level, and loads everything into a vector store with structured metadata.

Cross-lingual semantic retrieval

The retrieval layer uses multilingual-e5-large-instruct for embedding. We run a comparison against LaBSE on a sample of your documents during scoping and select the model that performs better on your specific document types. Performance varies by legal register and document origin.

Source citation with document, section, and page

Every answer includes the source document name, the section or clause reference, and the page number. A Miami attorney handling a title dispute can cite the exact clause to the opposing party in under 10 seconds.

Reranking for legal precision

First-pass retrieval returns 20-30 candidate chunks. A cross-encoder reranker (trained on legal text) re-scores them for relevance before the top results are passed to the language model. This step reduces hallucination on dense legal documents significantly.

Evaluation against your real queries

Before deployment, we run a benchmark using real attorney queries from your firm (or representative queries you provide). You see recall@3, answer accuracy, and citation accuracy numbers, not a product demo.

How we work.

Scoping call

One hour. You describe the document library: formats, volume, languages, how attorneys currently search, what queries matter most. We identify the right model and architecture.

Pilot on a document sample

We build a working retrieval system on a subset of your documents, typically 500-1,000 pages. You ask real questions. We measure accuracy and show you where the system fails before committing to full build.

Production build and handoff

Fixed-scope delivery, typically 4-8 weeks. You get the running system, the ingestion pipeline to add new documents, and a written evaluation benchmark. No retainer required.

Full process overview →

Questions about bilingual RAG systems.

Can a RAG system handle documents in both English and Spanish in the same query?: Yes, with the right embedding model. Multilingual-e5 and LaBSE both embed English and Spanish text into a shared vector space, which means a query in Spanish can retrieve a relevant English document and vice versa. This is called cross-lingual retrieval. The key constraint: you cannot use an English-only model like text-embedding-ada-002 for this task. Those models produce embeddings that do not align across languages, so Spanish queries will fail to retrieve English documents and vice versa. We test cross-lingual recall explicitly before deployment using a held-out set of paired questions in both languages.
What embedding models work best for bilingual real estate and legal documents?: Multilingual-e5-large-instruct performs well on legal and real estate content and supports 100 languages. LaBSE (Language-agnostic BERT Sentence Embeddings) was trained specifically for cross-lingual retrieval tasks and handles legal terminology well. For Miami real estate specifically, we have seen multilingual-e5 outperform LaBSE on mixed-language documents where the same sentence contains both English and Spanish terms, which is common in Miami commercial lease addendums. We run a model comparison on a sample of your documents during the scoping phase before committing to a model.
Does RAG accuracy drop for Spanish-language legal terminology compared to English?: There is a measurable gap, but it is smaller than most teams expect and can be managed. The main cause is training data distribution: legal Spanish is underrepresented in most multilingual model training corpora compared to legal English. In practice, for standard real estate terms (declaracion de condominio, asociacion de propietarios, clausula de rescision), retrieval accuracy is close to English parity. The gap widens for highly specialized Argentine or Colombian legal terminology that appears in investor documents from those markets. The mitigation is document-level metadata: tag each document with its source jurisdiction so the retrieval pipeline can prefer jurisdiction-matched documents when available.
How do you handle documents where some clauses are in English and others are in Spanish, common in Miami commercial leases?: Mixed-language documents require a chunking strategy that preserves clause boundaries rather than splitting on character count. We chunk at the clause level, detect the language of each chunk using fastText's language identification model, and store the language as a metadata field. At query time, if the query is in Spanish, we boost chunks tagged as Spanish slightly in the reranking step but do not exclude English chunks. If a specific clause exists only in English in a mixed document, the system still retrieves it and can pass it to a translation step before displaying it to the user. The choice of whether to translate at display time is a product decision, not a RAG architecture decision.

Scope your Miami bilingual document project.

Describe your document library, the languages it covers, and the queries your team spends the most time on. We reply within one business day.

Hire a RAG developer in Miami.

The bilingual document search problem.

What cross-lingual RAG requires.

A bilingual RAG system is not a standard RAG system with a translation layer bolted on. Several architectural choices must be made correctly at the start.

Multilingual embedding models

Language-aware chunking

Jurisdiction metadata for legal content

Cross-lingual evaluation set

What we build.

Document ingestion pipeline for mixed-format libraries

Cross-lingual semantic retrieval

Source citation with document, section, and page

Reranking for legal precision

Evaluation against your real queries

How we work.

Scoping call

One hour. You describe the document library: formats, volume, languages, how attorneys currently search, what queries matter most. We identify the right model and architecture.

Pilot on a document sample

Production build and handoff

Fixed-scope delivery, typically 4-8 weeks. You get the running system, the ingestion pipeline to add new documents, and a written evaluation benchmark. No retainer required.

Questions about bilingual RAG systems.

Can a RAG system handle documents in both English and Spanish in the same query?

Yes, with the right embedding model. Multilingual-e5 and LaBSE both embed English and Spanish text into a shared vector space, which means a query in Spanish can retrieve a relevant English document and vice versa. This is called cross-lingual retrieval. The key constraint: you cannot use an English-only model like text-embedding-ada-002 for this task. Those models produce embeddings that do not align across languages, so Spanish queries will fail to retrieve English documents and vice versa. We test cross-lingual recall explicitly before deployment using a held-out set of paired questions in both languages.

What embedding models work best for bilingual real estate and legal documents?

Multilingual-e5-large-instruct performs well on legal and real estate content and supports 100 languages. LaBSE (Language-agnostic BERT Sentence Embeddings) was trained specifically for cross-lingual retrieval tasks and handles legal terminology well. For Miami real estate specifically, we have seen multilingual-e5 outperform LaBSE on mixed-language documents where the same sentence contains both English and Spanish terms, which is common in Miami commercial lease addendums. We run a model comparison on a sample of your documents during the scoping phase before committing to a model.

Does RAG accuracy drop for Spanish-language legal terminology compared to English?

There is a measurable gap, but it is smaller than most teams expect and can be managed. The main cause is training data distribution: legal Spanish is underrepresented in most multilingual model training corpora compared to legal English. In practice, for standard real estate terms (declaracion de condominio, asociacion de propietarios, clausula de rescision), retrieval accuracy is close to English parity. The gap widens for highly specialized Argentine or Colombian legal terminology that appears in investor documents from those markets. The mitigation is document-level metadata: tag each document with its source jurisdiction so the retrieval pipeline can prefer jurisdiction-matched documents when available.

How do you handle documents where some clauses are in English and others are in Spanish, common in Miami commercial leases?

Mixed-language documents require a chunking strategy that preserves clause boundaries rather than splitting on character count. We chunk at the clause level, detect the language of each chunk using fastText's language identification model, and store the language as a metadata field. At query time, if the query is in Spanish, we boost chunks tagged as Spanish slightly in the reranking step but do not exclude English chunks. If a specific clause exists only in English in a mixed document, the system still retrieves it and can pass it to a translation step before displaying it to the user. The choice of whether to translate at display time is a product decision, not a RAG architecture decision.

Scope your Miami bilingual document project.

Describe your document library, the languages it covers, and the queries your team spends the most time on. We reply within one business day.