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Hire an LLM Engineer, Austin
Austin SaaS companies including Homeward, Procore's regional teams, and Dell subsidiary software groups embed LLM features directly in their products. The engineering job is not to make the feature work in a demo. It is to make it work reliably at the product's SLA, at a cost-per-user that is viable at scale, with consistent output format that users can depend on.
Getting those four things right simultaneously is where Austin product teams get stuck. Latency is too high, cost per user doesn't make sense at 10,000 seats, or the output format varies enough that users stop trusting the feature. We build the production layer that resolves each of these.
Fixed price, scoped per feature set.
Describe the LLM feature you are building and the SLA it needs to meet.
The three most common LLM features in Austin SaaS products right now: smart form auto-fill (using context from the user's history or uploaded documents to pre-populate form fields), document summarization (condensing uploaded contracts, reports, or specs into structured summaries), and intelligent search (retrieval-augmented generation over the user's own data).
Each of these looks straightforward in a prototype. Each has specific engineering requirements that make the difference between a feature users adopt and one they ignore. For smart auto-fill, latency under 800ms is the threshold for users perceiving it as helpful rather than slow. For document summarization, output format consistency determines whether a downstream workflow can consume the summary programmatically.
Cost-per-user economics are the most common reason an LLM feature gets deprioritized after the prototype works. A summarization feature that costs $0.50 per document is fine for an enterprise customer processing 10 documents per month. At 500 documents per month, that is $250 in API costs for a single user, which often exceeds the revenue from that seat.
We calculate cost-per-user at target scale before writing integration code. If the economics don't work with the frontier model, we benchmark smaller models or build a caching layer. The goal is a feature that is economically viable at your pricing tier, not just technically possible.
Streaming where the user reads incrementally. Batch where format consistency matters. Response caching for repeated queries. Target: under 800ms p95 for interactive features.
Token cost at your target MAU and average usage volume, calculated before build starts. If the economics require a cheaper model, we benchmark it first.
JSON schema validation for structured outputs. Format-locked prompts with few-shot examples for prose. Format regression tests in CI.
Primary provider, secondary provider, degraded mode. Core product function stays available when the LLM provider is down.
Per-user and per-feature token usage tracked in your database. Required for per-seat cost allocation and for enforcing usage limits by pricing tier.
Prompts in Git, not in env variables. Every change goes through the evaluation suite before deploying. Rollback takes one command.
Discovery call
One hour. You walk us through the feature, the SLA, the current prototype state, and the user behavior you are trying to drive.
Written scope and fixed price
Within 48 hours we send a scope document: deliverables, timeline, and a fixed price. You approve it before any work starts.
Build in two-week sprints
Weekly check-ins with your product team. You see working code at the end of each sprint, not a progress report.
Handoff with documentation
Codebase, prompt registry, evaluation suite, and a runbook for your engineers. Post-launch support window included.
Tell us what feature you are building, your current user count, and the SLA you need to hit. We will scope it and send a price within 24 hours.