Capability 01
AI Use Case Assessment
Assess specific AI use cases for business value, feasibility, data readiness and risk before development begins.
- Use case feasibility review
- Data availability check
- Value and risk scoring
- Solution approach
data ai
Move AI from experiments to everyday use.
We help you choose the AI use cases worth pursuing, then build models, assistants and agents that work with your data and systems. Each solution is tested, governed and ready for production.
Overview
AI, ML & Generative AI
AI, ML & Generative AI helps organizations apply artificial intelligence to defined business problems and run it reliably in production.
We help you prioritize use cases, then build machine learning models, generative AI assistants and AI agents that work with your data and systems. Every solution is evaluated against agreed quality criteria, deployed with monitoring and governed so data, accuracy and risk remain under control.
The result is AI that delivers practical value in daily work, not experiments that stop at the pilot stage.
Who this is for
- Business leaders who need to turn AI interest into measurable use cases
- Organizations with AI pilots that have not reached production
- Teams that want AI assistants grounded in their own documents and data
- Businesses that need prediction or forecasting from existing data
What gets in the way
Challenges this service addresses
Too many ideas, unclear value
AI ideas come from every team, but few are assessed for feasibility, data readiness or business value.
Pilots that never reach production
Proofs of concept work in demonstrations but lack the integration, monitoring and ownership needed to run in production.
Unreliable AI answers
Generative AI tools give confident but incorrect answers when they are not grounded in trusted business data.
Data and privacy concerns
Teams are unsure which data AI can use, where it is processed and how sensitive information is protected.
No way to measure quality
Without defined tests and monitoring, it is hard to know whether a model or assistant is accurate and improving.
What we do
Capabilities
Each capability can be scoped on its own or combined into a larger programme of work.
Capability 02
Machine Learning Solutions
Build predictive models for forecasting, classification, recommendation and anomaly detection using your business data.
- Demand and sales forecasting
- Classification and scoring
- Anomaly detection
- Recommendation models
Capability 03
Generative AI Assistants
Build assistants that answer questions, summarize and draft content using your approved documents and data.
- Retrieval-augmented generation
- Knowledge assistants
- Content summarization and drafting
- Copilot extensions
Capability 04
AI Agents & Application Integration
Build AI agents that complete multi-step tasks and integrate AI into your applications and workflows.
- Agent design and orchestration
- Tool and system integration
- Human approval steps
- AI features in applications
Capability 05
MLOps, Evaluation & Responsible AI
Test, deploy, monitor and govern AI solutions so they remain accurate, secure and compliant in production.
- Evaluation and testing
- Model deployment and monitoring
- Guardrails and access control
- Responsible AI practices
What changes
Business outcomes
- AI investment focused on proven use cases
- Faster access to knowledge across the organization
- Better forecasts and earlier detection of issues
- AI solutions that operate reliably in production
- Clear controls over data, quality and risk
How we work
Our approach
- 1 Assess Confirm the business problem, success measures, data availability and risks for each use case.
- 2 Design Choose the right model and architecture, and define evaluation criteria, guardrails and human oversight.
- 3 Build Develop and integrate the solution in iterations, using your data within agreed security boundaries.
- 4 Test Evaluate accuracy, safety and performance against agreed criteria, with business users reviewing results.
- 5 Launch Deploy to production with monitoring, then hand over to your team or our support service.
Why Hinkor
How we back that up
Use cases before technology
We start with the business problem and success measures, and recommend AI only where it is the right solution.
Evaluation built in
Every solution is tested against agreed quality criteria before launch and monitored after it.
Your data stays protected
Solutions run within your security boundaries, with access controls and clear rules on how data is used.
Built for production
Our engineering, data and cloud teams make sure AI integrates with your systems and can be operated reliably.
Engagement options
Starting points
Start with a defined piece of work. If the scope is not clear yet, begin with an advisory engagement and decide the delivery shape afterwards.
- Advisory Engagements Time-boxed expert advice, assessments and roadmaps that help you make confident decisions before you invest.
- Fixed-Scope Projects Defined scope, deliverables and timeline, delivered end to end by a Hinkor team accountable for the result.
- Dedicated Teams & Team Extension Hinkor engineers and specialists who join your team, follow your processes and work toward your roadmap.
AI Use Case Feasibility Review
Assess one or more candidate use cases for feasibility, data readiness and value before you invest.
AI Proof of Value
Build and evaluate one use case with your data over a fixed period, with a clear path to production.
Platforms
Technologies we use
Related services
Often delivered together
- Data Platforms & Engineering Build modern data platforms and reliable pipelines that bring trusted, governed data to analytics and AI.
- Analytics & BI Turn trusted data into dashboards, reports and insights that help people make better decisions every day.
- Intelligent Automation & RPA Automate repetitive, rule-based work with bots, workflows and document processing that are governed and easy to maintain.
- Custom Software & Product Engineering Design and build new web, mobile and cloud applications and digital products, from idea to production.
Insights
Related reading
- Article ·
How to Write an AI Use Policy Employees Will Follow
What an acceptable AI use policy should cover, a simple data rule and how to roll it out.
FAQ
Questions about AI, ML & Generative AI
If your question is not here, ask it directly.
Ask a direct questionIs our data ready for AI?
We assess data availability, quality and access for each use case. Many use cases can start with existing data. Where foundations are missing, we address them through Data Platforms & Engineering.
How do you prevent incorrect AI answers?
Assistants are grounded in your approved sources, tested against agreed evaluation sets and limited to defined tasks. Where answers affect decisions, we include citations and human review steps.
Is our data used to train public AI models?
We use enterprise AI services and deployment options that keep your data within agreed boundaries. Data handling, retention and access are reviewed and agreed before any data is used.
How is this different from Intelligent Automation & RPA?
Intelligent Automation uses pre-built AI services, such as document extraction, inside rule-based processes. AI, ML & Generative AI designs and builds custom models, AI assistants and agents for problems that need prediction, language understanding or reasoning.
Who maintains AI solutions after launch?
Each solution is handed over with monitoring and documentation. Your team can maintain it, or Hinkor can provide ongoing monitoring, evaluation and updates as data and models change.
Next step
Ready to put AI to practical use?
Talk to our AI team about your use cases, data and priorities.