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Hire a RAG Developer · Austin, TX
Austin SaaS companies are not building RAG for internal tooling. They are building it as a product feature: an "ask your data" mode inside their platform, visible to paying customers. That changes the engineering requirements significantly.
Customer-facing RAG needs per-customer document isolation so tenant A's content never leaks into tenant B's results. It needs usage metering so you can bill by query volume or token count. It needs rate limiting per subscription tier. And it needs reliability at SLA, beyond demo quality. Q2 Holdings, Procore, and the SaaS startups in Austin's tech corridor are building exactly this.
We build multi-tenant RAG systems designed for production SaaS products. Pricing depends on tenant count, document volume, and metering complexity, and we scope it with you before any work starts.
Tell us about the document feature you are building for your customers.
Internal RAG (a tool your own team uses to query your own documents) is a contained problem. You have one document corpus, one set of users, and you control the environment. Accuracy matters, but a 15% error rate means 15% of the time your team reads the source document instead.
Customer-facing RAG runs inside a product with an SLA. An answer that is wrong 15% of the time generates support tickets, damages trust, and in some industries creates liability. Each customer has their own document library that must stay isolated. Query volume is unpredictable and must be metered for billing. The system must degrade gracefully when a customer uploads a malformed PDF, not silently return bad results.
Austin's SaaS companies understand this distinction better than most. The fintech infrastructure companies (Q2 Holdings processes banking workflows for hundreds of community banks) and construction SaaS companies (Procore has customers who upload project documentation daily) both have customers who would notice immediately if document search returned stale or cross-tenant content.
The semiconductor and manufacturing companies near the Austin-Round Rock corridor face a related problem: equipment documentation that varies by configuration per customer site, where the wrong document version creates a safety issue. Multi- tenant isolation and version management are the same technical challenge regardless of industry.
Metadata pre-filtering on tenant_id before the vector search runs. Not a post-retrieval filter, the search never touches another tenant's chunks. For regulated industries, per-tenant namespaces with row-level security policies.
Each query writes a metering event: tenant_id, tokens consumed (embedding + completion), and query_id. Your billing system reads from this event stream to compute monthly usage. Token bucket rate limiting per subscription tier prevents overages from becoming your infrastructure cost.
Customers upload PDFs that are scanned at low resolution, Word documents with embedded images, or spreadsheets with merged cells. The ingestion pipeline needs to detect extraction failures and surface them to the customer (not silently ingest empty chunks) so they know why search quality is low on a specific file.
We build a per-tenant evaluation harness using sample documents from each of your beta customers before production launch. Recall@3 and answer accuracy benchmarks run automatically on each model or retrieval configuration change. The threshold for customer-facing deployment is higher than for internal tools.
A webhook-compatible ingestion endpoint your product calls when a customer uploads a document. Handles PDF, DOCX, XLSX, and plain text. Extraction errors are returned synchronously so your product can show the customer a status message. Chunk metadata includes tenant_id, document_id, upload_timestamp, and file_hash for deduplication.
We provision and configure either pgvector with RLS policies or Pinecone namespaces depending on your scale requirements. The access layer abstracts the vector store so migration between backends requires a configuration change, not a code rewrite. At 10 customers, pgvector is sufficient. At 1,000, Pinecone serverless is cheaper.
A thin middleware layer between your API and the RAG pipeline that writes query events (tenant_id, query_id, token counts, latency) to your event stream or a metering table. Includes a token bucket rate limiter configurable per subscription tier. Integrates with Stripe metered billing or your existing billing system via webhook.
A typed REST or tRPC endpoint your frontend calls. Returns: answer text, confidence score, cited chunks with document_id and page references, and a request_id for support debugging. On low confidence, returns a structured 'no confident answer found' response rather than a hallucinated answer. Latency target: p95 under 3 seconds on standard customer document sets.
A test framework that runs recall@3 and answer accuracy against sample documents from multiple tenants before deployment. Automated re-evaluation on retrieval config or model changes. Results written to a benchmark store so you can track quality over time as your customer document volumes grow.
Tell us what your customers upload, what queries they need to run, and what your current customer count and growth target look like. We reply within one business day with a rough scope and price range.