Data systems
// What we deliver

Three solutions, each with a clear scope and a fixed price

Voxelith doesn't offer undefined consultancy. Each service is built around a defined output, agreed success criteria, and a handover your team can actually use.

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// Our methodology

How every engagement is structured

01

Discovery

We spend time understanding your data, your domain vocabulary, and what you actually need to do with the output. This shapes every subsequent decision.

02

Scope Agreement

A written document defines what will be built, what success looks like, what's out of scope, and what the handover will include. Signed before work begins.

03

Build & Review

Development proceeds in milestones with written updates. You're involved at review points — not just at the end. Feedback is incorporated before final delivery.

04

Handover

A walkthrough session with your team, full documentation, and 30 days of follow-up questions at no additional cost. You leave knowing how to use what you've been given.

Knowledge Graph Construction
// Solution 01

Knowledge Graph Construction

We build knowledge graphs that map the relationships between entities in your organisation's data — connecting people, products, processes, documents, and concepts into an interconnected network. This structured representation enables more sophisticated querying, discovery, and reasoning than traditional databases allow.

Applications include enterprise search, regulatory compliance mapping, research acceleration, and customer relationship intelligence. We handle ontology design, data ingestion, graph construction, and provide a visual exploration interface.

What's included

  • Domain discovery and ontology design workshops
  • Data ingestion pipeline from your existing sources
  • Graph database construction and configuration
  • Visual exploration interface for end users
  • Query templates for common use cases
  • Full documentation and handover session

Typical process timeline

1

Week 1–2: Discovery and ontology design

Domain interviews, data audit, relationship mapping

2

Week 3–6: Pipeline and graph construction

Ingestion build, entity resolution, relationship population

3

Week 7–9: Interface and review

Exploration interface, query templates, client review rounds

4

Week 10–12: Testing and handover

Acceptance testing, documentation, walkthrough session

Project fee

HK$13,400

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// Solution 02

Text Mining and Extraction

Extract structured information from large volumes of unstructured text — contracts, emails, research papers, news articles, or regulatory filings. We build custom extraction pipelines that identify entities, dates, amounts, relationships, clauses, and other relevant data points specific to your domain.

The extracted information feeds into your existing systems or a dedicated dashboard. We focus on precision and recall metrics that match your tolerance for errors, and include a human review workflow for high-stakes extractions.

What's included

  • Extraction schema design and review
  • Custom pipeline build for your document types
  • Human review queue integration for edge cases
  • Benchmark testing against held-out documents
  • Dashboard or system integration as agreed
  • Documentation and handover session

Typical process timeline

1

Week 1: Schema design and samples review

Document type audit, entity definition, metric agreement

2

Week 2–3: Pipeline construction

Extraction rules, entity resolution, review queue setup

3

Week 4–5: Testing and integration

Benchmark runs, client review, integration or dashboard

Project fee

HK$7,800

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Text Mining and Extraction
AI Readiness Training
// Solution 03

AI Readiness Training

Prepare your team for a future that increasingly involves working alongside AI systems. This training programme covers practical topics — how AI models work at a conceptual level, how to evaluate AI-generated outputs, how to provide effective feedback to improve model performance, and how to recognise limitations.

Delivered over three sessions of ninety minutes each, the programme uses hands-on exercises with real AI tools. We adapt the difficulty and examples to your team's existing technical background. Designed to build practical competence and thoughtful scepticism.

Session breakdown

1

Session 1: How AI models actually work

Conceptual overview of how language models process information. No code required. Focus on mental models that help you work with AI tools more effectively.

2

Session 2: Evaluating and improving AI outputs

Hands-on practice evaluating AI outputs critically. Techniques for identifying errors, inconsistencies, and overconfident claims. How to give feedback that improves results.

3

Session 3: Recognising limits and building workflow

Where AI reliably falls short. How to design workflows that keep humans in the loop where it matters. Practical exercise applying principles to a real scenario from your domain.

Programme fee

HK$3,900

3 × 90-minute sessions

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// Comparison

Choosing the right solution

Not sure which service fits your situation? This matrix may help narrow it down.

Feature Knowledge Graph Text Mining AI Training
Structured output delivered
Works with existing documents
Suitable for complex entity relationships
Identifies specific data points in text
Builds team capability
Visual interface included
Human review workflow
Typical duration8–12 weeks3–5 weeks2 weeks (3 sessions)

Best for Knowledge Graph when...

You need to navigate complex relationships across many data sources — not just retrieve documents, but understand how entities connect across your organisation.

Best for Text Mining when...

You have a large body of documents and need to extract specific structured data from them — dates, parties, clauses, figures — at a scale your team can't manage manually.

Best for AI Training when...

Your team is starting to use AI tools but lacks the conceptual foundation to evaluate outputs critically or integrate them responsibly into their workflow.

// Shared standards

Professional standards across all solutions

Data Security

NDA before data sharing. Compliant with Hong Kong PDPO. On-premise deployment available for sensitive engagements.

Documentation Standard

All deliverables come with documentation that covers design rationale, not just usage. Written for the people who will maintain it.

Quality Metrics

Success is measured against criteria agreed before work starts. Acceptance testing is part of every delivery, not an optional extra.

Post-Delivery Support

30 days of follow-up questions answered at no additional cost. We want your team to actually use what we've built.

Not sure where to start?

Share a rough description of what you're trying to do — even if it's not fully formed. That's usually enough to have a useful first conversation.

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