LLM integration is a systems problem, not a prompt problem
Calling a model API takes an afternoon. Making LLM features hold up in a real product — predictable cost, measured quality, graceful failure when a provider has an outage — is systems engineering. DEV3LOPCOM, LLC, an Austin, Texas consultancy, builds that layer: one governed gateway between your application and the model providers, with routing, budgets, evaluation, and fallbacks designed in from the start. We run our own LLM-powered products, Bee and Canopys, so this architecture is something we operate daily, not just recommend.
What the integration layer includes
Model routing sends each task to the model that fits it: a frontier model for hard reasoning, a cheaper or open-weight model for classification and extraction where it scores just as well on your evals. Structured outputs and tool calling turn free-text generation into data your application can actually consume — typed JSON, validated against schemas, retried on parse failure. Streaming keeps the UI responsive. Fallback chains handle rate limits and outages by failing over to a second provider instead of failing your user.
Governance sits in the same layer: API keys in one place instead of scattered through the codebase, per-feature spend budgets, request logging, and PII redaction before data leaves your boundary.
How an engagement runs
We assess your product and pick the first features worth shipping — usually structured extraction, summarization, or assisted search, because they are easy to evaluate. We design the gateway and eval harness, then build: the integration, a test set drawn from your real data, and regression evals that run on every prompt or model change. Handoff gives your engineers the eval suite and the playbook for adding the next feature themselves.
Problems we fix
- LLM spend that nobody can attribute to a feature, growing 30% month over month.
- Hardwired single-vendor integrations that make every pricing change a hostage negotiation.
- Prompt changes shipped on vibes, with quality regressions discovered by customers.
- JSON parsing failures because the integration trusts the model to always format correctly.
- Sensitive data flowing to third-party APIs with no redaction or audit trail.
Why dev3lop
LLM features are only as good as the data behind them, and that is our home turf. We have built data systems for Nielsen (pipelines with trillions of records) and ExxonMobil (streaming data across 80+ campuses), and that discipline — validation, monitoring, documentation — is what we bring to model integration. The team is 100% onshore and deliberately senior. When your roadmap extends past single calls into multi-step autonomy, our AI agent practice picks up where integration ends; when the feature is question-answering over your own documents, that is RAG system development.
Ship your first measured LLM feature
Tell us what you want the model to do inside your product, and we will scope the integration, the eval set, and the cost envelope before any code is written. Contact us to start the conversation.