Lucis / PRODUCT

Private inference
for using AI with judgment.

Inference is the operational layer between your product and the model. It lets you decide where prompts run, what data enters, who can use each capability and what evidence remains on every call.

Talk about your architecture
CONTROL LAYERAPI → AIprivate / observable / evaluable

HOW IT WORKS

One clear path
for every call.

Your application keeps one compatible interface. Inference handles routing, authentication, limits, observability and evaluation without forcing you to rewrite the whole product.

01Application
02Private gateway
03Model / provider

Control stays on the path your product already uses.

TECHNICAL DETAIL

Operational control,
not promises.

01

Compatible API

Endpoints compatible with the format your team already knows. Change models or providers without rewriting the whole application.

  • Stable contract
  • Streaming and structured responses
  • Separate keys per environment
02

Authentication and permissions

Every request is identified and validated before it reaches the model. Define who can use each model, tool or dataset.

  • API keys and service accounts
  • Scopes by team or application
  • Rotation and revocation
03

Data and retention

Choose what is logged, how long it is kept and what must never leave your infrastructure. Configure policy by environment and use case.

  • Sensitive-data redaction
  • Configurable retention
  • Separate logs from prompts
04

Limits and cost

Apply limits per user, team, model or route. Measure tokens, latency, errors and cost to make decisions with real data.

  • Rate limits and quotas
  • Budgets per project
  • Usage alerts
05

Continuous evaluation

Turn known risks into repeatable test cases and run them before changing a prompt, model or tool.

  • Security regressions
  • Adversarial cases
  • Model comparison
06

Observability

Follow the complete request: prompt version, model, tools called, latency and evaluation result.

  • Traces per request
  • Metrics and dashboards
  • Audit-ready evidence

PUT IT INTO PRACTICE

Start with
one concrete path.

You do not need to migrate everything at once. Pick an existing AI flow, define the control it needs and measure the result before expanding it.

01

Choose

One use case and its most sensitive data.

02

Agree

Who can access it, with which limits and for how long.

03

Instrument

The route with authentication, logs and minimum metrics.

04

Test

Normal and adversarial cases before opening access.

NEXT STEP

Want to know if it
fits your stack?

Share how you call models today and what you need to control. We will help you draw a first architecture.

hello@lucis.pro