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Hire a RAG Developer · Dallas, TX
Dallas-area insurance companies have adjusters who spend 60–90 minutes per claim searching for relevant policy language. An adjuster handling 15 claims per day loses 2–3 hours daily to document search. At 200 adjusters, that is 400–600 person-hours per day. At a fully-loaded cost of $40/hour, that is $8,000– $12,000 per day in search time.
AT&T, headquartered downtown, faces a different version of the same problem: 200,000+ employees generating internal technical documentation, customer service knowledge bases, and network engineering procedures that no search tool handles well at scale.
Pricing for a Dallas insurance or enterprise RAG engagement is scoped to your document volume, source systems, and integration needs.
Describe your policy library or knowledge base and we will scope a retrieval system.
HUB International manages insurance programs for thousands of commercial clients from its Dallas operations. USAA's regional operations and Hilltop Holdings both deal with policy language at scale. The common thread is a claims adjuster or underwriter who needs a specific piece of policy language fast and has no good way to find it.
Policy forms are not structured for search. An ISO Commercial General Liability form plus its stack of endorsements can run 80 pages. The language that governs a specific coverage question might appear in the base form, be modified by one endorsement, and then have an exception added by a second endorsement. Finding all three pieces requires knowing to look for all three, which requires domain expertise that new adjusters lack.
For AT&T, the problem is internal knowledge fragmentation. A company that size generates internal documentation in dozens of systems: Confluence wikis, SharePoint sites, Jira ticket histories, PDF technical standards, and email threads. No single search covers all of them. Network engineers troubleshooting an edge case spend hours finding whether the answer has already been documented somewhere.
Healthcare is a third vertical in Dallas: Tenet Healthcare, Baylor Scott & White, and UT Southwestern Medical Center all face clinical documentation retrieval challenges. Prior authorization workflows, clinical policy lookups, and payer contract searches are all document-retrieval problems at their core.
A 60-minute policy form search becomes a 5–7 minute interaction: submit the question, verify the cited source, apply the language. The adjuster's expertise goes into the coverage decision, not document search.
The system retrieves the base policy clause and all modifying endorsements in a single query result, ranked by relevance. Adjusters see the complete picture without needing to know which endorsements exist.
Domain expertise about where to look for specific policy language is encoded in the retrieval system. A new adjuster with 6 months of experience can search with the same accuracy as one with 6 years, because the search quality is consistent.
For AT&T-scale deployments, we build connectors to Confluence, SharePoint, Jira, and static document stores. A single query interface searches all of them simultaneously, deduplicates results, and surfaces the most relevant answer regardless of which system it lives in.
Every answer includes the policy form number, endorsement identifier, section and paragraph, and version date. Adjusters can click through to the full document without re-searching.
We build a test set of 100+ query-answer pairs from your actual policy questions and run precision and recall metrics before any adjuster uses the system. You get a number, not a demo.
ISO and proprietary policy forms are indexed at the clause level. Endorsements are indexed with pointers back to the base form provisions they modify. A retrieval query on a specific coverage provision returns the base language plus all relevant endorsements in a single ranked result set.
We build source connectors for Confluence, SharePoint, Jira, and PDF repositories. Incremental indexing means new documents appear in search within minutes of being published, not after a nightly batch job.
Policy language has precise technical terms ('occurrence basis', 'claims-made', 'per-occurrence limit') that benefit from exact keyword matching. Dense embedding search handles semantic equivalence. Running both and combining scores via Reciprocal Rank Fusion outperforms either alone on insurance document queries.
The RAG API integrates with Guidewire ClaimCenter, Salesforce, and ServiceNow via REST. The search interface appears inline within the claims workflow. Pre-population from the claim record (policy number, loss type, state) filters the search before the adjuster submits a question.
Tell us your document types, rough volume, and the workflow it needs to support. We respond within one business day with a scope estimate.