Education
Automated Essay Scoring Dashboard for Educational Institutions
Development of an automated essay scoring system combining text processing, scoring, and a results dashboard to support distance learning.
Machine Learning & Artificial Intelligence
Applying machine learning and AI as part of clear, measurable, and supervised work processes.
NLP, automated assessment, recommendation systems, intelligent decision support, workflow automation, and AI-assisted applications.
What is this Service?
Development of intelligent systems based on Natural Language Processing (NLP), Computer Vision, and recommendation systems to automate cognitive tasks.
Who is it for?
Organizations with high text data volumes, user personalization needs, or operational processes requiring advanced automation.
Business Value
Drastically improving efficiency by automating repetitive work, extracting information from unstructured data, and delivering highly personalized user experiences.
Expected Outcome
Automated scoring systems, recommendation engines, intelligent chatbots, sentiment analysis, or AI-integrated applications (GenAI/LLM).
Challenges
Problems We Solve
Assessment, classification, or document review processes are done repeatedly by hand.
Important information is hidden inside large volumes of text or documents.
AI initiatives stop at prototypes and are not yet connected to business processes.
Organizations need automation that remains reviewable by humans.
Scope
Scope of Work
1
NLP and text intelligence
2
Automated assessment and scoring
3
Recommendation system
4
Pattern detection and anomaly identification
5
AI-assisted application
6
Intelligent decision support
7
Workflow automation and model monitoring
Output
Deliverables
Model or analytical service
AI-assisted application
Automated assessment system
Recommendation or decision-support module
Documentation, testing, and monitoring
Use Cases
Sectors and Use Contexts
- Education
- Government
- Health
- Business
- Research
Engagement Process
Engagement Process
- Problem definition
- Data readiness
- Model development
- Application integration
- Human review design
- Monitoring and improvement
Methodology
Structured Approach
01
Understand
Understanding the organizational context, the problem at hand, and the desired outcomes.
02
Audit
Evaluating available data — its quality, structure, completeness, and readiness.
03
Engineer
Building reliable, clean, and analysis-ready data pipelines.
04
Analyze & Model
Transforming data into relevant insights and usable models.
05
Build & Deploy
Building systems, dashboards, or AI solutions and deploying them to production.
06
Transfer & Improve
Documenting the process and transferring knowledge to internal teams.
Portofolio
Related Projects
Education
Machine Learning Training Course for UII Statistics Students
Training on data analysis software (SPSS/R) with direct consultation to help UII Statistics students understand and analyze their research data.
FAQ
Frequently Asked Questions
Are all processes suitable for AI automation?
No. The early stage assesses risk, workload volume, data quality, explanation needs, and the human role in decisions.
Can databaik build LLM-based solutions?
Yes, when the use case, knowledge sources, evaluation, access control, and validation mechanisms are clear. An LLM is not the goal; it is one component of the solution.
How do you reduce the risk of incorrect AI output?
Solutions are designed with measurable evaluation, source restrictions, logging, human-in-the-loop, and monitoring according to the risk level of use.
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Consult your needs with our expert team to map out the most appropriate approach for your organization.