Intelligence, with evidence.
A clear account of what we have built, what we have measured, and what comes next.
What exists today
Pauram Intelligence is a Swift SDK combining a compact, locally trained intent classifier with app knowledge retrieval, confirmed tool execution, and governed learning. It is an integration candidate. The classifier is not a general-purpose foundation language model.
Native and HTTPS language-model adapters are implemented. Successful native generation still needs device validation; the local native probe fell back after a platform error. A live HTTPS provider has not been configured and verified.
Measured development results
- 100/100 synthetic app profiles, 900/900 checks: a shared export workflow across 100 distinct app configurations. Fifty injected a first-attempt failure. This is a post-fix regression run; the first run passed 75/100 profiles and 875/900 checks.
- 70 automated SDK tests: including a regression test for a documentation search affected by unrelated screen context.
- 47/48 on the separate fresh routing check: authored by the same development process, not independent reviewers. That remaining miss has not been trained away.
These results do not establish general AI accuracy, independent customer outcomes, performance on iPhone, or readiness across 100 real apps. The suite exercises confirmation, failure recovery, verified state, replay rejection, ambiguity, and foreign-session plan isolation.
Recorded SDK simulation
Look inside a development test.
Explore saved results from the shared export workflow. These are synthetic app profiles, not installed customer apps or a live AI demonstration.
Loading recorded results…
Read the underlying results →What learning means here
With consent, the SDK can collect bounded observations and accept approved corrections. Candidate intent classifiers are evaluated for regressions. Activation requires separate reviewer approvals through the host app’s authentication integration. Checkpoints are encrypted; learning can be paused or deleted. This is not local LoRA training or an always-running background process.
The next milestones
- Integrate one real app, with current records, screen context, and tools that verify actual outcomes.
- Validate native language-model generation, fallback, latency, and memory use on supported devices.
- Evaluate retrieval, answer support, and multi-step actions using frozen cases from independent reviewers.
- Release through TestFlight and staged rollout with consent, deletion controls, and rollback.
More advanced model specialization remains a research option. It will need its own dataset, model-version compatibility checks, and evidence that it improves the real app experience.
Product availability
App releases and capabilities will be announced separately. This site does not assert hardware-enclave inference, biometric-derived vector vaults, universal offline operation, or independent security certification for the current SDK.