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Hire an LLM Engineer, San Diego
If you are the research informatics lead at Illumina, Neurocrine Biosciences, or Retractable Technologies, your team manages a library of thousands of internal experimental reports and scientific publications. The task: extract structured data (compound names, assay results, statistical outcomes) from unstructured research documents into a queryable database. Manual extraction by research associates at $65,000 to $80,000 per year handles 20 to 30 records per day.
An LLM extraction pipeline with schema validation and confidence scoring extracts 85 to 90% of records automatically. The ambiguous 10 to 15% go to a human review queue with the extraction pre-filled. For San Diego defense contractors including SAIC and Leidos, the same pipeline runs on-premise using open-weight models, no external API calls with CUI data.
Fixed price, scoped after we see your documents and extraction schema.
Tell us your document type, your extraction schema, and whether you need on-premise deployment.
You manage a growing library of experimental reports from your internal research teams and curated external publications. Key data elements (compound IDs, assay results, IC50 values, target proteins) need to enter a structured database for cross-study querying and downstream analysis.
Your research associates spend 30 to 45 minutes manually extracting each report. The backlog grows because extraction speed does not scale with publication volume.
An LLM extraction pipeline with a compound assay schema handles 85 to 90% of records in under 60 seconds each. The pipeline uses the public API (OpenAI or Anthropic) because internal research data does not have CUI restrictions.
You need to extract specifications from technical documents for a parts database or a systems requirements registry. The documents are classified or CUI, which means no external API calls are permitted.
An on-premise pipeline running Mistral Large or Llama 4 via vLLM on your own servers achieves extraction performance close to GPT-5 for well-defined structured extraction tasks. All data stays on your infrastructure.
The extraction schema is typically: part number, specification category, requirement text, source section, and applicable standard. Schema validation and confidence scoring are the same as the cloud deployment.
Tables in scientific papers require structure extraction before LLM processing. We use pdfplumber to extract column-row relationships, then pass structured table content to the model.
JSON Schema with your field definitions: compound identifiers, assay types as enum values, result fields with unit validation. The model output is validated before writing.
Per-field confidence combining model scores and rule-based checks (IUPAC format validation, range checking, format consistency). Triggers review queue for ambiguous records.
Review interface showing the source document alongside the extraction with flagged fields highlighted. Associates correct and approve from the queue.
vLLM or Ollama running open-weight models on your servers. No external API calls. Designed for SAIC, Leidos, and other CUI-constrained environments.
Approved records write to your ELN, LIMS, or custom research database. Field mapping documented. RESTful API or direct insert.
Start with your extraction schema: what fields your database needs and where they typically appear in the documents. We then look at 25 to 50 sample documents to assess format variation and identify the hard cases. Hard cases in research documents are usually: multi-table records (where one logical record spans multiple tables), conflicting values between text and tables, and non-standard compound identifiers.
For on-premise deployments, the discovery call includes a technical requirements review: your server specifications, security constraints, and the network isolation requirements. We assess which open-weight model is appropriate for your extraction task and document the inference hardware requirements.
Build time: 5 to 8 weeks for cloud deployment, 8 to 12 weeks for on-premise deployment due to infrastructure setup and testing in your environment.
Share 5 to 10 sample documents and your target extraction schema. We will assess the automation rate, flag the hard cases, and send a scope within two business days.