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RAG Development · Austin, TX
Austin builds software for a living. The product-doc surface area at Dell, Oracle Austin, Q2 Holdings, Indeed, Bumble, and the long tail of Series B through D companies along Congress and East 6th is the actual business asset, and most of it lives in Confluence pages and Notion databases that nobody has pruned in three years.
We build RAG systems for Austin product and engineering teams that turn the existing internal-knowledge sprawl into an actually-queryable surface. Internal product-docs assistants that respect your Confluence permissions, sales-enablement retrieval that runs fast enough for a live call, KYC document search for fintechs that handles PII the way your security review expects.
Pricing is scoped per engagement depending on source count, corpus volume, and how strict your latency and security posture needs to be.
Tell us what knowledge surface your team actually needs to query.
The dominant Austin shape is a B2B SaaS company that has doubled headcount twice, has 4,000 to 40,000 Confluence pages, a Notion that the marketing team uses as a CMS, a Zendesk macros library with 1,200 entries, and a Slack channel graveyard nobody searches. The product-doc maintenance cost shows up as new-hire ramp time, repeated questions in #ask-eng, and a slow customer-support response cycle. None of this is a model problem. It is an information-architecture problem that retrieval over the existing corpus actually fixes.
Q2 Holdings and Charles Schwab build banking software that community and regional banks across the country deploy. The product documentation is several thousand pages deep, the customer-facing implementation guides are denser still, and the support team handles questions that mix product behavior with the specific compliance posture of a given bank. Retrieval pipelines that respect the bank-level segmentation are the difference between a useful internal tool and a tool that leaks one customer's configuration into another customer's answer.
Dell sells enterprise hardware and software to governments and Fortune 500 buyers from Round Rock. Oracle's Austin campus runs the cloud-infrastructure business. Both operations carry technical knowledge bases that are large enough to defeat traditional faceted search. A solutions architect at either company answering an RFP needs to pull from the product documentation, the prior responses library, the certification matrix, and the legal-cleared messaging doc. That is four corpora and three permission models.
Consumer-app companies like Bumble and Indeed have a different shape. The retrieval problem is less about internal docs and more about trust-and-safety, policy enforcement at scale, and surfacing the relevant precedent decision when a moderator is reviewing a flagged account. The pipelines look different but the underlying mechanics are the same: ingest the corpus with metadata that matters, retrieve with hybrid search, rank with a cross-encoder, generate against a model that respects citations.
Six components shaped by how Austin product, engineering, and sales teams actually use retrieval in production.
Atlassian OAuth scopes for Confluence, Notion API search with bot capability checks, Google Drive Workspace scopes, and Slack search-export pipelines. Hierarchical metadata (space, parent page, label, last-modified date) preserved so filters run before vector search.
HNSW index on managed Postgres tuned against your actual query distribution. halfvec for memory-sensitive deployments. Migrations and backups in your existing toolchain so the vector store does not become a separate operational surface.
Row-level security in Postgres or per-tenant index partitioning so a Q2 Holdings or Schwab-shaped deployment cannot return one customer's configuration in another customer's answer. Audit logs scoped per tenant.
Sub-500-ms end-to-end retrieval path for battlecard, win-loss, and competitor-doc lookup. Chrome extension or Slack /command interface so the AE does not switch tabs. Feedback signals on deal stage and competitor exposed to the sales-enablement team.
Two-stage pipeline where the retrieval index is redacted and the cleartext document is stored encrypted with customer-managed KMS keys. Designed against typical SOC 2, GLBA, and Texas Department of Banking review.
Test set of 100 to 200 real questions from your team, benchmarked for recall, MRR, and answer accuracy. OpenTelemetry spans on every stage exported to Datadog, Grafana, or whatever your team already runs. Production debugging happens in your existing tool.
Austin is a software-product town. The economic base here is not in the historical financial-services or legal corpora that drive RAG demand in New York. It is in internal knowledge, product documentation, sales-enablement libraries, and the compliance-adjacent document piles that fintechs accumulate. The buyer is a head of engineering, a VP of product, or a CISO who wants the retrieval surface to live next to the rest of the stack rather than as a vendor lock-in.
The fintech and banking-software cluster is heavier than people outside the city realize. Q2 Holdings, Self Financial, Kasasa, Brex's Austin office, and the Charles Schwab Westlake campus all carry real regulated-data obligations. Texas does not have a state-level financial regulator on the scale of NY DFS, but GLBA, BSA, and the patchwork of state banking-charter rules apply just as forcefully. Our default architecture for this segment isolates PII at the index layer rather than relying on prompt-level redaction, which fails under any serious security review.
The hospitality and consumer-app cluster is the second axis. Bazaarvoice, Indeed, Bumble, Vrbo (via Expedia), and a long list of smaller players run review-content, listing-content, and user-generated-content corpora that are perfect retrieval targets but introduce trust-and-safety questions that a naive RAG pipeline does not address. We typically pair the retrieval layer with a content-policy classifier in the pipeline so the system filters out policy-violating content before generation rather than after.
Tell us what stack you are on, what your team actually wants to query, and what your security review will care about. We reply within one business day with a rough scope and price range.