Open source vs hosted APIs?

Hosted when speed to a pilot matters and data policy allows it. Self-hosted when residency or cost at volume says so. We will not pretend one is always cheaper.

Do you guarantee a model?

We guarantee an architecture that can change models. We do not guarantee a vendor’s roadmap.

Can you build our own LLM from scratch?

No. That is a research org. We integrate and evaluate.

LLM Development | Integration & Evaluation

LLM Development

LLM development at AppsAura means making a large language model behave in a product: JobForge-style scoring explanations, drafts, routing. We do not train GPT-class models. We choose among APIs and self-hosted options when data residency requires it, then we evaluate like we would any other dependency.

Service overview

The model is a component. It has latency, cost, outages and a version you do not control. We isolate it behind our API, keep prompts in version control, and refuse to block checkout or a clinical alert on a model timeout.

Problems we solve

One vendor SDK sprinkled through the Flutter and React apps.
No evals, so ‘we improved the prompt’ is folklore.
Sending whole databases in context ‘just in case’.
Ignoring that model upgrades change behaviour overnight.

What we build

Gateway API with timeouts and fallbacks
Prompt and policy versioning
Offline eval on a frozen set before upgrades
PII minimisation and redaction
Cost dashboards per feature
Streaming where the UI actually benefits

Benefits

LLMs treated as infrastructure with an owner.
A path to swap providers without rewriting the app.

Use cases

Explanations next to a score (JobForge).
Draft copy that a human sends.
Classification of inbound text into existing enums.

Technologies

PythonNode.jsAWS

Industries this shows up in

No undiagnosed advice No invented balances