How every engagement runs
Each project follows the same four-phase structure. The phases scale in duration depending on complexity — a computer vision prototype runs through all four phases in three weeks, while a speech pipeline might take twelve.
Scoping
Define objectives, data requirements, and acceptance criteria in a written document.
Data Preparation
Assess, clean, and structure your data. Build collection pipelines where needed.
Build & Evaluate
Iterative model development against agreed metrics. Bi-weekly progress updates.
Handoff
Documented delivery, walkthrough session, and full IP transfer to your team.
Speech-to-Insight Pipeline
We build end-to-end systems that convert spoken audio — calls, meetings, interviews — into structured, searchable insights. The pipeline includes speech recognition, speaker diarisation, topic segmentation, and summary generation, all customised to your domain vocabulary. Outputs integrate with your CRM, knowledge base, or analytics platform.
Suitable for call centres, research teams, and legal firms that handle high volumes of recorded audio and need to extract value from it without manual review.
What's included:
- Automatic speech recognition tuned to your vocabulary
- Speaker diarisation (who said what)
- Topic and sentiment segmentation
- Structured summary generation
- API or webhook integration with your existing systems
- Full documentation and handoff session
Process steps
Audio ingestion
Batch or real-time audio pipeline setup
Domain adaptation
Vocabulary and acoustic model fine-tuning
NLP enrichment
Topic, sentiment, entity extraction
Output integration
Connect to CRM, dashboard, or knowledge base
Computer Vision Prototyping
A rapid prototyping service that takes your visual recognition idea from concept to working proof-of-concept within a compressed timeline. We handle data collection strategy, quick-iteration model training, and a functional demo deployed to a staging environment.
Ideal for teams that need to validate a vision-based hypothesis before committing to full-scale development. Common use cases include product defect detection, document classification, inventory counting, and retail shelf analysis.
What's included:
- Data collection and labelling strategy
- Baseline and fine-tuned model training
- Working demo on staging environment
- Performance evaluation report
- Recommendations for production path
- Full code ownership on handoff
Process steps
Problem definition
Clarify recognition objective and success criteria
Data strategy
Collection, labelling, and augmentation plan
Iterative training
Rapid model iterations with evaluation
Demo deployment
Functional prototype on staging environment
AI-Powered Search Relevance Tuning
We improve the relevance and ranking quality of your existing search infrastructure by layering machine-learned ranking models on top of your current engine. The service includes query analysis, relevance judgement collection, feature engineering, and model training with offline and online evaluation.
Suitable for e-commerce platforms, publishing sites, and internal enterprise search where poor relevance is measurably affecting user satisfaction or conversion.
What's included:
- Query log and intent analysis
- Relevance judgement collection and annotation
- Feature engineering for your catalogue
- Learning-to-rank model training
- Offline NDCG/MRR evaluation report
- Online evaluation setup and handoff
Process steps
Search audit
Analyse query logs and current ranking behaviour
Judgement collection
Gather and annotate relevance data
Model training
Learning-to-rank with feature engineering
Evaluation & deploy
Offline metrics + online experiment setup
Which solution fits your situation?
| Feature | Speech Pipeline | Vision Prototype | Search Relevance |
|---|---|---|---|
| Typical timeline | 8–12 weeks | 3–5 weeks | 5–8 weeks |
| Price (MYR) | 6,400 | 2,800 | 4,700 |
| Needs audio data | |||
| Needs image data | |||
| Needs query/click logs | |||
| Integrates with existing systems | Staging demo | ||
| Good for hypothesis validation | |||
| Multilingual support | N/A |
Not sure which fits? Ask us — we'll tell you honestly.
Technical standards across all solutions
Data security
All data handled under PDPA 2010 requirements. Encryption at rest and in transit. Data processing agreements available on request.
Clean, commented code
All delivered code follows consistent formatting and includes inline documentation. Structured for maintainability by teams unfamiliar with the codebase.
Infrastructure flexibility
Systems designed to run on AWS, Azure, GCP, or on-premise. We document infrastructure requirements clearly and avoid proprietary lock-in.
Evaluation-first approach
We define quality metrics before we start building. Evaluation is not an afterthought — it is built into the development cycle from week one.
Comprehensive documentation
Architecture diagrams, API references, operational runbooks, and a handoff guide are standard deliverables on every engagement.
Full IP transfer
You own the code, models, and data artefacts we produce. We retain no usage rights and do not reuse client data or models for other projects.
Let's find the right starting point for you.
Most conversations begin with a short scoping call. No lengthy procurement process required.
Book a Scoping Call