Top 7 AI Agencies in Singapore to Consider in 2025

Singapore is a regional hub for practical AI. Local firms serve banks, retailers, telcos, healthcare groups, and fast-growing startups. The focus is not hype. The focus is on outcomes: faster support, safer onboarding, better fraud checks, and smarter product search. This article guides readers through the landscape in simple, clear terms.

This article lists the Top 7 AI Agencies in Singapore section and then covers other topics leaders ask about: how to scope, how to budget, how to manage risk under local rules, what tools to expect, and what a first 90-day plan looks like. Every section uses direct language so teams can move from reading to doing.

If a brand, SME, or enterprise is looking for an AI agency in Singapore, this article offers a starting point. It does not try to cover every company. Instead, it points to agencies and solution partners that are active in 2025 and serve real use cases, with references where helpful.

How to Brief and Scope your AI agency in Singapore

A clean brief saves weeks. Keep it short and concrete:

• Business goal: Describe a single goal in numbers. Example: “Raise first-contact resolution (FCR) from 48% to 65% in six months” or “Cut onboarding time from 3 days to 1 hour.” Link every AI task to this number.
• User journeys: Map three to five journeys, not fifty. Show where AI will act: search, support, verification, risk check, recommendation, forecast, or workflow automation.
• Data map: List the datasets and access: CRM, chat logs, POS, images, documents, and events. Note owners, volumes, formats, PII status, retention rules, and consent status (PDPA matters; more on this below). 
• Constraints: State model and stack limits: “Must run on GCP,” or “Must avoid US data transfer,” or “Must use Bedrock,” etc. If the company plans to join a government or cloud program (for example, Google Cloud’s 2025 AI Cloud Takeoff incentives in Singapore), include that, because it may change the cost and architecture. 
• Success metrics: Choose two or three outcome metrics (AHT, CSAT, FCR, fraud rate, approval rate, conversion, shelf accuracy). Add one model risk metric (bias checks, hallucination rate, or approval workflow SLA).

Budget and Engagement Models for an AI agency in Singapore

• Discovery & design (2–6 weeks): Expect a fixed fee for research, data audit, feasibility, and a plan with prototypes and a backlog. Output includes architecture, data gap list, risks, and a 90-day pilot scope.
• Pilot build (8–16 weeks): This stage delivers a working slice in a limited channel or product line: a chatbot answering the top 50 intents, an IDV flow for one country, a vision model for one category, or a shelf scan in 10 stores. Costs depend on integrations, GPU time, and human-in-the-loop review.
• Scale-up (ongoing): Budget shifts to MLOps, model monitoring, governance reports, and training. For enterprise rollouts, the agency may form a center of excellence so internal teams take over. Singapore programs in 2025, like Google Cloud’s AI Cloud Takeoff and AI Trailblazers 2.0, can offset part of the spend for eligible firms. 

Commercial patterns to expect

• Fixed fee for discovery and POCs.
• Time & materials for custom builds.
• Platform or API usage for chat, IDV, AML, or search (seat- or volume-based).
• Managed service for model tuning, retraining, and monitoring.

Hidden costs to surface early

• Annotation and feedback loops.
• Prompt and RAG evaluation time.
• Red-team testing and AI assurance. (Singapore promotes AI assurance sandboxes and AI Verify tools to test real-world gen-AI systems.)

Also Read: Top 7 Blockchain Agencies in Singapore (2025 Update)

Top 7 AI Agencies in Singapore to Consider in 2025

Here are some of the leading AI agencies in Singapore to consider in 2025:

• Snap Innovation – Applied AI for workflows and fast pilot-to-production
• NCS (Singtel Group) – Enterprise-scale AI with strong security and governance
• AiChat – Chat and voice automation with agentic workflows
• ADVANCE.AI – Digital identity, KYC/KYB, and risk controls
• Trax – Retail computer vision for shelf truth and execution
• ViSenze (Rezolve AI) – Visual search, “snap to shop,” and recommendations
• Tookitaki – AML and financial crime detection with explainable models

Looking to use AI in a safe, practical way in 2025? Here’s a detailed look at the Top 7 AI Agencies in Singapore shaping real outcomes this year. Whether you need secure enterprise platforms, chat and voice automation, identity checks, retail computer vision, or visual search, these partners offer the tools, governance, and delivery models to help your team ship value with confidence.

1. Snap Innovation 

Snap Innovation focuses on practical AI that improves day-to-day work. Projects often blend process discovery, workflow automation, and small custom apps that plug into current systems. Teams start with clear problem framing, then deliver quick pilots that grow into stable tools. The approach balances speed with change management so staff can adopt new ways of working. For leaders who want useful outcomes without heavy jargon, this style is straightforward and results-driven.

Best for: SMBs, mid-market units, and cross-functional teams that want fast, usable AI tools inside current workflows.

2. NCS (Singtel Group)

NCS is a homegrown tech services firm with deep public-sector and enterprise programs. In 2025, the company highlighted new AI tools and accelerators, plus fresh investment in AI IP, talent, and sector pilots. Work spans conversational AI, knowledge-augmented systems, and gen-AI contact center deployments. NCS is strong at integration, security, and governance across complex estates. For large programs that need MAS-aligned process and long-term support, it is a solid first call.

Best for: Ministries, regulated industries, and large enterprises needing multi-year roadmaps, internal platform builds, and production support.

3. AiChat

AiChat builds chat and voice experiences across web, apps, and messaging channels. The platform includes Voice AI, Agent Copilot, and agentic workflows that route tasks or fetch knowledge. Use cases include support, lead capture, and simple commerce flows. Brands use it to raise first-contact resolution and deflect repetitive calls. Multilingual support helps teams serve English plus regional languages at scale.

Best for: Retail, F&B, airlines, healthcare, and any brand with high chat volumes or call-deflection goals.

4. ADVANCE.AI 

ADVANCE.AI is a specialist in digital identity verification and risk. Solutions include face match, liveness, document checks, KYB, and risk scoring. The goal is to reduce fraud while keeping onboarding smooth for real users. Tools support compliance and case management across markets. This is a focused partner rather than a general “AI for everything” shop.

Best for: Banks, fintechs, payments, crypto, and marketplaces that must cut fraud and friction at the same time.

5. Trax

Trax focuses on computer vision for retail execution. It monitors shelves, detects out-of-stocks, and measures planogram compliance. Field teams use insights to correct issues faster, lifting sales and availability. The stack is mature and deployed across global CPGs and retailers. For brands with thousands of SKUs and many stores, consistent “shelf truth” is the main value.

Best for: CPGs, supermarkets, pharmacies, and convenience chains that need real-time shelf truth.

6. ViSenze (now part of Rezolve AI)

ViSenze is a longtime leader in visual search for e-commerce. In 2025, it joined Rezolve AI, signaling growth in image-based discovery and product matching. Typical features include “snap to shop,” similar-item search, and ML-driven recommendations. These tools increase findability and reduce drop-off when words are hard to use. Large catalogs in fashion and home see clear gains in conversion and AOV.

Best for: Fashion, home, and large catalog retailers that want image-based discovery and better conversion.

7. Tookitaki

Tookitaki’s FinCense platform combines AI with community intelligence to improve detection and reduce false positives. Features support transaction monitoring, sanctions screening, and case workflows. The focus is on explainable models and model governance to match regulator needs. Banks use these tools to manage alert volume without missing risk. This helps control compliance cost while staying audit-ready.

Best for: Banks, insurers, and fintechs needing AML, transaction monitoring, sanctions screening, and explainable models.

There is no single winner for all cases. The best choice depends on your data, systems, governance, and how fast you must show value. Use the pros and cons tables, the buyer checklist, and the RFP questions to reach a confident and simple decision.

AI Governance, PDPA, and Risk Management in Singapore

Singapore treats AI as both an opportunity and a trust challenge. Teams should prepare three things before kickoff.

1. PDPA Alignment

The Personal Data Protection Act (PDPA) governs the collection, use, disclosure, and care of personal data. An AI project must define purpose, consent or legitimate interest, retention, data minimization, and safeguards. Work with the DPO to log data flows and access. 

2. AI Governance Testing

Singapore’s AI Verify Foundation and IMDA promote tools and sandboxes to test AI systems against recognized principles (fairness, explainability, robustness, etc.). In 2025, Singapore launched new AI assurance steps and shared insights from world-first tests of real-world gen-AI apps. Ask the agency to include AI Verify-style tests and reporting in the plan. 

3. Program Incentives and Public Initiatives

In June 2025, Google Cloud and Singapore announced AI Cloud Takeoff with incentives up to S$500,000 for local enterprises to build AI solutions. This can shape budgets and timelines, and it pairs well with an agency partner. 

Practical checklist:

• Data inventory with PDPA roles and access. 
• Model cards and evaluation reports.
• Human-in-the-loop (HITL) gates for high-risk actions.
• AI assurance plan using AI Verify or similar. 
• Incident playbook and rollback plan.

Also Read: 7 Best Blockchain Development Companies in Singapore (2025 Update)

Typical Stacks and Tools an AI Agency in Singapore will Use

• Cloud & compute: Agencies commonly build on GCP, AWS, or Azure. In Singapore, GPU access and managed services keep improving through local data centers and telco-linked offerings. (Regional GPU-as-a-service and cloud programs continue to expand to meet demand for training and inference.) 
• Data & integration: Expect ETL/ELT pipelines, event streams, and vector databases for retrieval-augmented generation (RAG). For contact center use cases, speech-to-text and call summarization often sit on top of foundation models, with orchestration layers similar to NCS’s Ins8.ai approach. 
• Evaluation & safety: Include prompt evaluation, hallucination tests, red-teaming, bias checks, and approval workflows. Align testing with AI Verify guidance where possible.

Specialized APIs:

• Identity & risk: ADVANCE.AI for IDV, KYC/KYB. 
• AML: Tookitaki FinCense across monitoring and screening. 
• Conversational: AiChat for chat and voice with handoff and agent tools. 
• Vision & retail: Trax for shelf analytics; visual search: ViSenze (Rezolve AI). 
• Audience intelligence: SQREEM for cookie-free targeting. 

Conclusion

This article kept the focus on how to plan, build, and govern real AI work in Singapore. It showed a simple path: start from one clear business goal, map a few user journeys, and link data and tools to that goal. With the right scope and a small pilot, teams can move from talk to working results.

Good outcomes also need trust. Projects should follow local data rules, align with PDPA, and use practical AI assurance checks such as AI Verify-style testing. A steady review loop, human oversight for high-risk steps, and clear model reports help leaders manage risk while they scale.

Singapore’s support ecosystem makes adoption easier. Public programs, cloud incentives, and shared testing methods can reduce cost and time. Use these resources with a capable partner, keep the plan small at first, and grow by proof. This approach helps any organization get value from AI in weeks—and keep that value over time.