Consultancy grounded in how AI actually gets deployed
Every Cogniq engagement follows a consistent pattern: understand your current state, agree the scope, work collaboratively, and close with your team equipped to continue independently. We don't use this structure because it sounds good in a pitch — it's what has made engagements work over the past six years.
Our three practice areas were chosen because they address the gaps that consistently prevent enterprise ML from delivering value in production. They're complementary — many clients engage us across more than one area over time.
Discover
Understand your current AI state and gaps
Scope
Agree deliverables, timeline, and pricing
Build
Implement collaboratively with your team
Hand Over
Transfer knowledge and close the engagement
Machine Learning Operations Setup
Helps organisations establish the infrastructure, processes, and tooling needed to reliably develop, deploy, and maintain machine learning models in production. The engagement covers pipeline design, model versioning, monitoring and alerting setup, and retraining workflows.
Consultants work with engineering teams to implement repeatable deployment patterns and governance checkpoints. Particularly relevant for organisations moving from experimental AI projects to sustained production systems.
Process Overview
Deliverables included
- Configured MLOps platform (cloud or on-prem)
- Operational runbooks for day-to-day use
- Monitoring dashboard and alerting rules
- Team training sessions (minimum 2 sessions)
- Retraining workflow documentation
Deliverables included
- Annotation guidelines document (data-type specific)
- Tool selection recommendation with rationale
- QA protocol and review cycle design
- Inter-annotator agreement benchmarks
- Labeling operations manual
Data Annotation & Labeling Strategy
Designs efficient and quality-controlled data labeling workflows for machine learning projects. Covers annotation guideline development, labeling tool selection, quality assurance protocols, and workforce management strategies.
Particularly valuable for organisations undertaking large-scale supervised learning projects with complex data types — where the quality of labeled data directly determines the quality of the resulting model.
Process Overview
AI-Powered Process Mining
Uses artificial intelligence techniques to analyse event logs from business systems — uncovering actual process flows, bottlenecks, and deviation patterns. The engagement includes data extraction, process model discovery, conformance analysis, and enhancement recommendations.
Suited for operations-heavy organisations using ERP, CRM, or workflow management systems — where the gap between the designed process and the actual process is a source of inefficiency or risk.
Process Overview
Deliverables included
- Process maps of actual vs designed workflows
- Deviation and bottleneck report
- Conformance analysis summary
- Prioritised optimisation recommendations
- Business-language executive summary
Which service suits your situation?
| Feature / Consideration | MLOps Setup | Annotation Strategy | Process Mining |
|---|---|---|---|
| Starting price (MYR) | 4,600 | 3,100 | 6,200 |
| Typical duration | 4–8 weeks | 2–4 weeks | 6–10 weeks |
| Requires ML team | Recommended | Not required | Not required |
| Technical configuration | — | ||
| Business-language reporting | |||
| Knowledge transfer sessions | |||
| Best suited for | Orgs scaling from experimental ML to production | Teams starting supervised learning projects at scale | Operations-heavy orgs with ERP/CRM event data |
Shared standards across all services
Data Confidentiality
Client data is handled with strict confidentiality. NDAs available on request. PDPA-aware handling throughout.
Version Control Standards
All configurations, documentation, and code follow version control practices for auditability and reproducibility.
Internal Review Process
Major deliverables are reviewed internally before client submission to maintain quality and consistency.
Documentation Standards
Runbooks, manuals, and reports are written for the people who will use them, not for people who designed the system.
Progress Communication
Agreed update cadence at project start. No information asymmetry — progress is shared proactively.
Honest Scoping Commitment
If an engagement isn't a good fit, we'll say so early rather than proceed. Scoping conversations carry no obligation.
Transparent pricing
All prices in Malaysian Ringgit (MYR). Complex or larger-scale engagements quoted separately after scoping discussion.
starting price
- Annotation guidelines
- Tool recommendation
- QA protocol design
- IAA benchmarks
- Operations manual
starting price
- Configured MLOps platform
- Operational runbooks
- Monitoring & alerting
- 2+ team training sessions
- Retraining workflows
starting price
- Process maps
- Deviation analysis report
- Conformance summary
- Optimisation recommendations
- Executive summary
Not sure which service fits?
An introductory call can help clarify which engagement — if any — would be useful for your current situation. No cost, no obligation.
Get in Touch