Client collaboration

CLIENT EXPERIENCES

What Organisations Say About Working With Us

Honest accounts from teams and leaders who have engaged Tekad Systems for AI integration and assessment work across Malaysia.

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40+

ORGANISATIONS SERVED

4.8

AVERAGE CLIENT RATING

5

YEARS IN PRACTICE

93%

CLIENTS RETURN OR REFER

WHAT CLIENTS SAY

Client Testimonials

"The maturity assessment gave our board a grounded picture of where we stood — not the optimistic spin we'd been getting from vendors. That honesty was exactly what we needed before committing to a significant investment. The advancement plan has since shaped our AI roadmap for two years."

AR

Ahmad Razif bin Yusof

Chief Strategy Officer · Shah Alam

February 2026

"We had a large customer dataset but weren't using it well. The segmentation work opened up genuinely new ways of thinking about our customer base — segments we hadn't considered, with clear implications for retention. Our marketing team now refers to these segments daily."

TM

Tan Mei Ling

Head of Marketing · Petaling Jaya

January 2026

"I was sceptical — we'd had bad experiences with technology consultants who oversold and underdelivered. The Tekad team was refreshingly upfront about what AI could and couldn't do for our workflows. The literature analysis tools we now use have cut our review time considerably."

PR

Dr. Priya Ramasamy

Innovation Lead · Subang Jaya

March 2026

"Technical findings were always presented in plain language that our non-technical board could engage with meaningfully. That's not a common skill in this field. The five-dimension framework also made sense of what had felt like a scattered set of AI initiatives within our group."

MF

Mohd Faizal Hashim

Group CEO · Kuala Lumpur

February 2026

"Our research centre works across multiple disciplines, which made AI integration more complicated than for a single-domain team. Tekad Systems took time to understand each team's workflow separately before designing anything. The result was tools that actually matched how different researchers work."

SL

Sarah Lim Hui Ting

Research Centre Director · Cyberjaya

January 2026

"The segmentation model surfaced a high-value behavioural cluster our previous demographic approach had completely missed. Once we understood how these customers engaged differently, we adjusted our service packaging and saw a noticeable improvement in retention within three months."

RK

Rajan Krishnamurthy

Head of Analytics · Bangsar South

March 2026

IN DEPTH

Case Studies

CHALLENGE

R&D Division, Manufacturing Group — Shah Alam

A 35-person R&D team was spending roughly 40% of research time on manual literature reviews and data collation. They had experimented with basic AI tools independently but found them poorly suited to their materials science domain.

SOLUTION

An eight-week engagement covering workflow mapping, tool selection, a training programme for research leads, and domain-specific literature analysis pipelines. Close collaboration with senior researchers ensured tools matched how they actually worked.

OUTCOME

Literature review time reduced by approximately 55%
Pattern recognition flagged three areas of previously unnoticed data variance
All 35 researchers comfortable with new tools within 10 weeks

8 weeks · AI Research Operations · RM 790

CHALLENGE

Mid-Sized Conglomerate — Kuala Lumpur

Leadership had approved a multi-year AI investment budget but lacked clarity on where to focus. Multiple departments had proposed competing AI projects, risking expensive misalignment between investment and actual organisational capability.

SOLUTION

Five-week AI Maturity Assessment with sixteen stakeholder interviews across six departments, a governance documentation review, data audit, and benchmarking against three comparable regional organisations. Concluded with a facilitated half-day planning workshop with the board.

OUTCOME

Board aligned on a two-year AI investment sequence in a single session
Three low-readiness projects deprioritised, redirecting resources appropriately
Governance gaps identified and actioned before major spend was committed

5 weeks · AI Maturity Assessment · RM 2,100

CHALLENGE

Consumer Finance Company — Petaling Jaya

A financial services company with over 180,000 customers was operating with broad demographic segments that were no longer generating useful marketing insight. Campaign performance had stagnated and the marketing team sensed their customer base was far more varied than their segmentation suggested.

SOLUTION

A ten-week customer segmentation engagement drawing on three years of transaction, product usage, and service interaction data. Eight behavioural clusters were identified — substantially different from the company's existing five demographic segments — each receiving a full profile and written activation strategy.

OUTCOME

One high-value cluster identified that had not appeared in any previous analysis
Targeted campaign to this cluster significantly outperformed previous campaigns
Marketing team fully operating with new segment model within 12 weeks

10 weeks · Customer Segmentation · RM 3,570

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