Business examples
Synthetic instruction and tool-call examples are grounded in KaziAI’s routing, handlers, schemas, and operating tasks.
- Department routing
- Tool arguments and clarification
- Plans and approval behavior
KAZILITE · THE DOMAIN-MODEL WORKSTREAMIntelligence shaped
Intelligence shaped
around business work.
KaziLite connects the model workstream to the operating system: business-language examples, exact tool contracts, planning tasks, approval behavior, and evaluations that reflect the work KaziAI is designed to coordinate.
A KAZIAI MODEL WORKSTREAM
KaziLite.
DATA · ADAPTERS · EVALUATION · SERVINGBusiness language.
Tool contracts.
Operational decisions.
WHY MODEL SPECIALIZATION BELONGS HEREA business request
A business request
has a business contract.
Selecting a tool, recovering a missing argument, respecting a review boundary, and preparing a valid plan are concrete operating tasks.
Train around the system’s language
Training examples are built around departmental routing, intent classification, ERP reasoning, tool selection, clarification, approval workflows, planning, and verification.
Keep structured work explicit
The business expects defined arguments and reviewable outcomes. Tool schemas and planning structures make those expectations part of the model workstream’s training and evaluation.
Connect research to deployment choices
The KaziLite provider gives the backend an OpenAI-compatible serving interface. Cloud, local, and hybrid strategies remain workload decisions that need capability and operating tests.
FROM BUSINESS CONTRACT TO MODEL WORKSTREAMA deliberate path
A deliberate path
from data to evaluation.
Model adaptation
LoRA and QLoRA training configurations adapt an existing instruction model to those domain tasks.
- Existing foundation models
- Versioned adapter artifacts
- Reproducible training configuration
Business evaluation
Held-out evaluation assets define how to inspect routing, arguments, plans, safety behavior, and execution outcomes.
- Separate evaluation cases
- Schema and policy checks
- Representative workload review
Serving integration
The provider interface connects a compatible endpoint to model routing and the KaziAI request lifecycle.
- Configured model identifiers
- Endpoint and health checks
- Capacity and failure testing
THE ARTIFACTS IN THIS PROJECTA model workstream
A model workstream
with a concrete starting point.
The adjacent KaziLite package includes datasets, training configurations, benchmark tooling, and adapter exports. The serving adapter sits alongside the backend’s other provider implementations.
Artifact presence establishes what can be inspected. It does not establish checkpoint quality, production capacity, or performance on a live business workload. Benchmark targets and synthetic reference runs are separate from measured model results.
INSPECTABLE ADAPTER CONFIGURATION
KaziLite 1.5B adapter
- BASE MODEL IN THE EXPORT
- Qwen / Qwen2.5-1.5B-Instruct
- ADAPTATION METHOD
- LoRA · causal language modeling
- ADAPTER RANK / ALPHA
- 16 / 32
- SERVING BOUNDARY
- Configured OpenAI-compatible endpoint
These details come from the exported adapter configuration and backend provider. Evaluate the checkpoint you intend to serve.
THE LONG-TERM DIRECTIONBring the model closer
Read the broader KaziAI vision Bring the model closer
to the operating reality.
KaziLite makes domain specialization part of KaziAI’s ambition: intelligence evaluated against business work, integrated with the platform’s context and tool boundaries, and deployed according to the needs of the organization.
PUT THE VISION TO WORKBegin with a workflow.
Plan an implementation Begin with a workflow.
Build toward a system.
Choose a real business problem. Connect its context, define the decision owners, and evaluate what useful intelligence looks like for your team.