gaik 0.5.16

General AI Kit - Reusable AI/ML components for Python

GAIK – Generative AI Knowledge Management Toolkit

This is a generative AI toolkit of the GAIK project (gaik.ai). It provides a complete set of components and guidance for building knowledge-centric GenAI solutions, from strategic directions to deployable implementations.

Project Documentation

Project documentation is available at:

https://gaik-project.github.io/gaik-toolkit/

Live Demo: https://gaik-demo.2.rahtiapp.fi/

Why the toolkit is needed

Generative AI has significant potential to increase the productivity of knowledge work

• Example experiments: consultants using AI were significantly more productive – they completed 12.2% more tasks on average, and completed tasks 25.1% more quickly (Dell'Acqua, 2023)
• Example cases from practice: Customer-support agents at a large firm selling business-process software demonstrated a 15% increase in productivity when assisted by generative AI (Brynjolfsson, 2025).

However, tangible business value from Generative AI implementation projects is still limited

• “only 26% of companies have advanced beyond the proof-of-concept stage to generate value” Source: BCG’s report (de Bellefonds et al, 2024).
• “Despite $30–40 billion in enterprise investment into GenAI, 95% of organizations are getting zero return.” Source: MIT report (Challapally et al, 2025).

Adopting Generative AI and creating value from it is especially challenging for small and medium-sized enterprises (SMEs), which lack the technical expertise and capabilities to implement GenAI solutions effectively. The literature review of Oldemeyer et al. (2024) identified the following three most frequent challenges for SMEs in the AI implementation in the industrial sector: knowledge, costs, and the low maturity level in digitalization.

Overall approach

Companies can deal with GenAI challenges by combining reusable building blocks with clear guidelines. Instead of designing solutions from scratch, teams assemble existing components and follow proven ways of working. This makes it easier to turn ideas into real results, while reducing implementation time, risk, and required resources, and improving overall solution quality.

Toolkit Focus

The knowledge management perspective for structuring GenAI development and implementation activities.

The toolkit focuses on three core knowledge processes in organizations:

Knowledge process	Description	Illustration
Knowledge capture	Extract needed information from business documents, videos, voice recordings, emails, and meeting recordings
Knowledge access	Intelligent access to organizational knowledge (document repositories, databases, wikis, CRMs)
Knowledge synthesis	Automatic generation of business reports, sales proposals, marketing materials, project proposals

The following generic use cases are defined as the top priority at the moment:

Knowledge process	Generic use cases
Knowledge capture	A. Incident reporting in industry (e.g., for equipment, buildings) B. Creating construction site diaries C. Creation of transcripts and closed captions in various languages for instructional videos and podcasts D. …
Knowledge access	A. Customer assistant for complex products and services B. Semantic audio and video search for medical instructions C. Learning assistant
Knowledge synthesis	A. Sales proposal generation B. Report preparation C. …

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Layer-Based Architecture

The GAIK Toolkit is organized into a layer-based architecture that spans from strategic planning to implementation and security:

Layer	Purpose	Contents
Strategy Layer	Identification and selection of use cases, GenAI adoption readiness assessment and preparation, business value evaluation	Use case selection framework, Value evaluation framework, AI maturity assessment tool, GenAI success canvas
Requirements Layer	Requirements capture and specification	Requirement templates, test cases
Business Layer	Use case definition, workflow and work system analysis and redesign	GenAI product canvas, Workflow templates, Work systems definitions
Implementation Layer	Solution development either via no-code or code-based approach, solution performance evaluation, integration, and monitoring	Reusable software components and modules for system development, (gaik code package), no-code assets, evaluation methods, unit tests, deployment packages, connectors
Security Compliance Layer	Security policies and compliance frameworks	Security guidelines, compliance checks, audit trails
Guidance Layer	Guides and automates the process of solution development and implementation for KM (how to select and assemble building blocks)	Process and guide for GenAI solution implementation, Configuration wizard, Glossary

This architecture ensures that GenAI solutions are built with proper governance, clear requirements, and comprehensive implementation support.

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Solution Configuration Wizard

The GAIK Solution Configuration Wizard is the Guidance Layer in action. It takes a plain-language description of a business problem and guides you — through a structured, multi-turn conversation — all the way to a validated, deployable GenAI solution.

What the wizard produces

Deliverable	Description
use_case.blueprint.json	Executable source of truth — components, artifacts, workflow steps, schemas, model settings
workflow.bpmn	BPMN 2.0 visual blueprint with swimlanes, gateways, and data objects for stakeholder review
workflow.mmd	Mermaid diagram (quick developer flow view)
poc/	Runnable proof of concept: run_poc.py, requirements.txt, schema, prompt, eval script
docs/	Documentation suite: GenAI product canvas, technical specification, user guide, developer guide, evaluation plan

Everything is written to a directory you choose — nothing is written into the toolkit repository.

How to run it

Option 1 — Claude Code or Claude Desktop (recommended)

/solution-wizard

Type this in a Claude Code session or Claude Desktop chat. The wizard guides you through the full workflow conversationally.

Option 2 — Web chat (demo website)

Open the live demo and navigate to Solution Configuration Wizard. Token-by-token streaming, generated files appear in the sidebar, downloadable as a .zip.

To run the demo locally:

cd implementation_layer/toolkit_demo_app
bun dev:all   # starts Next.js frontend + FastAPI backend
# open http://localhost:3000/solution-wizard

Option 3 — Individual scripts (CLI)

The wizard's deterministic scripts can be run independently:

cd implementation_layer/solution_wizard

# Validate an existing blueprint
python scripts/validate_blueprint.py --blueprint ~/my-use-case/use_case.blueprint.json

# Generate BPMN visual blueprint
python scripts/generate_bpmn.py --blueprint ~/my-use-case/use_case.blueprint.json --output-dir ~/my-use-case

# Scaffold a PoC from a validated blueprint
python scripts/scaffold_poc.py --blueprint ~/my-use-case/use_case.blueprint.json

Full documentation

See implementation_layer/solution_wizard/README.md for the complete walkthrough, output directory layout, component registry, validation rules, and test suite.

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License

This project is licensed under the MIT License – see LICENSE for details.