Kaarjo Adaptive Neural Framework
One layer that decides how intelligence gets used.
KANF is not a model or a chatbot. It takes a complex objective, works out what needs doing, splits it into tasks, sends each task to the right model, agent or calculation engine, checks the results, and only then returns an answer.
Verify can send work back to Refine. Nothing reaches Output until it passes.
// the seven stages
What happens between the request and the answer
Each stage is a real handoff, not a prompt template. Pick one to read what it does.
Context understanding
The request is read before anything is generated. KANF works out the intent behind it, the context it sits in, the information still missing, the constraints that apply and the shape of output expected. Input can arrive from a person, an API, an application, a document, a database or another system.
// capabilities
What the framework provides
Eight functions that every product built on KANF inherits, rather than rebuilds.
AI orchestration
Coordinate several models and agents inside one workflow, sequentially or in parallel.
Decision intelligence
Read structured and unstructured information together and return a recommendation or a decision.
Human evaluation
Score skills, knowledge, behaviour and performance against structured parameters.
Workflow automation
Turn a stated objective into executable multi-step work with owners for each step.
Model routing
Choose a model per task by complexity, capability, speed or cost.
Verification
Check generated output against rules, calculations, datasets or an independent agent before it ships.
Domain adaptation
Reuse the same core with industry-specific data, rules, tools and evaluation frameworks.
API layer
Expose all of the above to third-party applications.
// where it runs
The same core, six kinds of work
Swap the data, rules and engines; the orchestration stays the same.
HR tech
Recruitment and human evaluation that reads more than CV keywords. Several evaluation signals combine into one structured candidate profile.
Application → Skill assessment → AI interview → Behavioural analysis → Capability score → Job match → Hiring recommendation
- Candidate assessment
- Skill verification
- AI interviews
- Candidate–job matching
- Behavioural analysis
- Career-path recommendations
// model-agnostic by design
No single provider holds the system up
Models are connected per task and can be replaced, upgraded or combined without touching the application above them.
KANF stays the control layer
It holds the plan, the routing rules, the verification logic and the record of what each source returned. Everything below it is interchangeable.
// the difference
Two architectures
Typical AI application
User → Model → Answer
One source, one pass, no check. Whatever comes back is the product.
Built on KANF
User → KANF → Planning → Multiple intelligence sources → Execution → Verification → Refinement → Result
Many sources, checked against rules and calculations, corrected before release.
A model generates intelligence. KANF manages how that intelligence is used.
// long-term direction
One framework that understands complex objectives, coordinates specialised intelligence, validates execution, and turns AI output into reliable real-world action.