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Hire a RAG Developer · Los Angeles, CA
A production coordinator at a major studio needs to know whether a specific talent agreement includes sequel rights that have already reverted. The contract is in a library of thousands of active agreements. Searching manually takes 45 minutes per query. A RAG system on the contract library answers in under 10 seconds, with a citation to the exact clause.
At 20 queries per week, that is 15 hours of manual search time replaced. At the billing rates that entertainment law firms like Greenberg Glusker and Hansen Jacobson charge ($600–$800 per hour), the same 15 hours of attorney-level contract review costs $9,000–$12,000 per week. That is the math before even accounting for the internal staff time.
We build contract RAG systems for entertainment companies, studios, and healthcare organizations in Los Angeles, scoped to your document types and query needs.
Tell us what you need to search across, and we will scope a system.
NBCUniversal, Warner Bros. Discovery, and Sony Pictures each maintain libraries of thousands of active contracts: talent agreements, licensing deals, co-production agreements, location agreements, distribution contracts, and merchandising licenses. Most of these are in PDFs or Word documents spread across shared drives or legacy contract management systems with inadequate search.
A business affairs associate answering "does the Paramount format license for this property include sequel rights" is doing work that has a hard time cost. The answer is in a specific clause in a specific document. Finding it manually means opening the likely documents one by one, using Ctrl-F on individual files, and reading enough context to verify the answer is correct. That is 30-60 minutes if the person knows where to start, longer if they do not.
The same problem extends to healthcare. Cedars-Sinai, UCLA Health, and Kaiser Permanente Southern California manage payer contracts with hundreds of plan-specific reimbursement terms, authorization requirements, and billing rules. A patient financial services representative asking "does this Anthem contract permit balance billing for this procedure code in this setting" is solving an identical retrieval problem.
The cost framing matters for making the build-vs-continue decision explicit. If a team of five business affairs associates each spend two hours per week on contract search, that is 10 staff hours per week. At $75/hour fully loaded, that is $750 per week, or roughly $39,000 per year. A RAG build pays back within the first year on staff time alone, before factoring in the queries that currently do not get answered at all because the search cost is too high.
A question that currently requires reading 3-5 contracts manually returns in under 10 seconds. The answer includes a citation to the specific clause so the user can verify it immediately without re-reading the full document.
A query like 'find all talent agreements in this franchise library where sequel rights have already reverted' runs across the full contract corpus in a single search, not document by document. The result set is a list of matching contracts with the relevant clause excerpted from each.
A production coordinator sees location agreements and production budgets. A business affairs associate sees licensing deals and format rights. C-suite sees everything. Access scoping is enforced at the vector search layer before results are returned, not at the interface layer after the fact.
Entertainment contracts define critical terms (Net Profits, Holdback Period, Theatrical Release) at the start. A query about any of these terms retrieves both the usage clause and the definition that governs it.
Native PDFs, Word documents, and scanned legacy contracts are all handled. Scanned documents go through Textract OCR. Extraction failures are flagged explicitly so the team knows which documents need re-scanning.
Every answer cites the document name, page number, section heading, and paragraph index. An attorney reviewing the answer can jump directly to the source without re-reading the contract.
DOCX via python-docx with heading structure preserved. Native PDF via pdfplumber. Scanned PDF via AWS Textract or Google Document AI. Format detection is automatic at upload. Each document gets document_type, contract_date, counterparty, and deal_type metadata extracted at ingestion for pre-filter queries.
The definitions section of each contract is parsed at ingestion. Defined terms are stored as metadata linked to every chunk in that contract where the term appears. A retrieval request on any chunk that uses a defined term automatically co-retrieves the definition. Relevant for entertainment contracts where 'Gross Receipts' and 'Net Profits' have negotiated definitions that differ from standard industry usage.
Each chunk is tagged with an access_group field at ingestion based on document type and any explicit access rules you define. User identity (via SSO) maps to permitted access groups at query time. Pre-filter runs before the vector search. Unauthorized content is never retrieved, not filtered after the fact.
Queries that need to span the full contract library (e.g., 'find all co-production agreements with a distributor approval right') run as a single retrieval pass over the full indexed corpus with appropriate metadata filters. Results return as a ranked list of matching contracts with the relevant clause excerpted.
We build a test set of 80-100 questions from actual business affairs and legal queries your team has run. Recall@3, answer accuracy, and citation correctness benchmarks run before deployment. The benchmark results are part of the handoff package.
Tell us what contract types are in your library, roughly how many documents, and what questions your team needs to answer from them. We reply within one business day.