Noetis Career Explorer · Personal engine for app builders

Career Exploration Engine for Apps

Turns interests, demonstrated skills and constraints into career options worth exploring.

Answers: "Can an app suggest career options from a user's interests and skills?" For students and people considering a career change.

Labour market data is used only when the app supplies a source.

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Implemented

Implemented: runs on the shared runtime with supplied material. One bounded development check on 2026-10-12 took 130s with 15 model calls and ended "incomplete". That is a development signal, not an evaluation. Customer access is by approved account.

What you send

Question
Supplied text material
Text material up to 150,000 characters per run

What comes back

Career options with an actionable exploration plan.
Findings with the located quote and a support status from claim review
Release status: released, incomplete with reasons, or failed
Executed roles, usage and measured cost

Sample

Authored illustration. Written by Encevra to show the workflow. Not customer data and not a recorded model output.

What careers should I explore?

I enjoy explaining things and organizing events. Built a volunteer schedule for 40 people using spreadsheets. Studied biology. Cannot relocate for 2 years. Can study part time, about 6 hours a week.

The real response shape is documented in the API reference.

How the workflow operates

Each role is a responsibility in the workflow, served by a third party language model. More roles are not automatically better; depth and routing decide which run.

Built with

Models this engine uses: OpenAI (GPT), Anthropic (Claude), Kimi (Kimi (Moonshot AI)). Specialists run on OpenAI models and the claim review and final brief on Anthropic models. If a provider has a temporary failure, the step can fall back to an OpenAI model, and the run records that.

Interests Analyst

Clarify the preferences the person states and separate them from assumptions.

Skills Mapper

Identify abilities demonstrated in the supplied material, not merely claimed.

Career Researcher

Describe role requirements using supplied role information only, and flag what must be checked.

Constraints Reviewer

Consider time, money, location and other practical limits the person states.

Action Planner

Propose concrete, low cost exploration steps with what each would reveal.

Evidence and deployment

Evidence

This engine reads the text you send in the request. Each released finding carries the exact quote and its character span in that text, located mechanically before release. Quotes that cannot be located are labelled Insufficient evidence and never shown as source verified. Interpretation is reviewed as a separate step, and gaps such as a missing clause describe what was searched instead of citing text that is not there.

Deployment

Runs on Encevra hosted infrastructure using the third party models listed above. Every model call and fallback is checked against the deployment policy first. Private cloud and air-gapped deployment are planned; discuss deployment requirements.

What it does not handle

Hiring or ranking decisions
Salary or placement guarantees
Authorized source retrieval is not available: Not built. Engines read only supplied material.
Uses only supplied material. No freshness guarantee beyond the documents provided.
Required for every output during trials.

How to integrate

Send "engine": "career-explorer" with your question and material from your server. Keys stay server side.

How to evaluate it

No published result for this workflow yet. Build a small answer key from your own work and compare against a strong single model baseline. See the evaluation checklist and method.

Cost and rights: pricing and licensing. Showing outputs to your own customers needs a written agreement.

Schedule an engine setup call

Related engines

Content reviewed 2026-10-12 by the Encevra engine team. Engine version 0.1.0 on noetis-core-1.