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Hire a RAG Developer · Houston, TX
An engineer on an offshore platform operated by Shell or ConocoPhillips needs the correct non-standard shutdown procedure for a specific valve configuration. The procedure manual is 3,000 pages. The wrong procedure means equipment damage or a safety incident.
The Texas Medical Center is the largest medical complex in the world. Houston Methodist, MD Anderson, and Memorial Hermann each deal with clinical documentation at a scale that makes manual search untenable for any time-sensitive workflow.
Both problems require the same core capability: precise document retrieval with strict citation requirements and confidence-bounded answers. Scope and pricing are tailored to your document library and workflow.
Describe your technical documentation library or clinical document challenge.
ExxonMobil operates a massive technical documentation library covering hundreds of facility types, equipment configurations, and process units. Maintenance engineers, HSE professionals, and operations staff all need to locate specific technical procedures quickly. The library grows continuously as new procedures are approved and old ones are revised.
The challenge with energy sector documentation is specificity. A procedure that applies to a 6-inch gate valve in a high-pressure steam system does not apply to the same valve size in a natural gas system. A search that returns a similar-sounding document from the wrong service category is worse than no search result at all, because it creates false confidence.
The Texas Medical Center's scale is different: 61 institutions, 21 hospitals, 106,000 employees. Clinical documentation at this scale includes clinical practice guidelines, formulary documents, payer contract libraries, and prior authorization criteria from dozens of insurers. A prior authorization coordinator at Houston Methodist might need to answer "does Aetna's current clinical policy support authorization for this gene therapy in this diagnosis", and needs the answer in minutes, not hours.
Both verticals share a requirement: the system must know when it does not know. An energy RAG system that generates a plausible answer when no relevant procedure exists is dangerous. A clinical RAG system that cites the wrong payer policy version creates authorization denials. Confidence-bounded retrieval is not a nice-to-have in Houston's industry context. It is required.
Queries filter by equipment tag, service type, operating pressure, and valve configuration before the semantic search runs. The result is the procedure for this exact equipment type in this service, not a generic procedure that might not apply.
Below a defined similarity threshold, the system responds with a specific fallback message rather than generating an answer. For safety-critical energy procedures, that fallback is 'consult your engineering supervisor.' It never fabricates.
Clinical policies change quarterly. Each payer document version gets an effective_date metadata field. The system defaults to the current version, but staff can query historical versions. Stale chunks are marked superseded, not deleted.
We build connectors to SAP Plant Maintenance to surface prior maintenance history alongside current procedure documentation. An engineer querying a procedure can see what was done the last time this maintenance was performed on this equipment tag.
PDF procedure manuals are parsed with table-aware extraction. Equipment tags, model numbers, valve specifications, and service classifications are extracted from document headers and section titles into structured metadata fields. This metadata drives the pre-filter step before vector search.
Step-based procedures require chunking that keeps a multi-step sequence together. We use parent-child chunking: each step is a child chunk, the full procedure is the parent. Retrieval returns the specific relevant step plus its surrounding procedure context.
Payer clinical policies, formulary documents, and prior authorization criteria are indexed with payer, policy_number, effective_date, and superseded_date fields. The ingestion pipeline handles both PDF and HTML source formats from payer portals.
Where the document corpus includes PHI, we configure the vector database and embedding API pipeline within a HIPAA-compliant boundary: a BAA with the vector store vendor, VPC-isolated embedding calls, role-based access controls, and encrypted storage at rest and in transit.
A held-out test set of 100+ query-answer pairs from your actual documents. Recall@3, answer accuracy, citation precision, and confidence calibration metrics. You get benchmark numbers before any user query hits the system.
Tell us your document types, any safety or compliance requirements, and the queries your team needs to answer. We respond within one business day.