PM INNOSOFT · AI Platform for Business
Frequently asked questions when starting AI projects
We summarize key questions most organizations ask before investing in AI and analytics.
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What should you know before starting AI projects?
You don’t need massive data from day one. We start by collecting clean data, designing the right structure, and building small proof-of-concept projects before scaling up.
We help you start with a small cross-functional team while PM INNOSOFT acts as your AI partner – supporting architecture, technology choices, and coaching your internal team.
We typically recommend starting with 1–2 focused PoC use cases to prove value and measurable impact, then design a scalable platform architecture for long-term rollout.
PoC-level projects often start at a modest budget, while full-scale, integrated systems with dashboards typically fall in higher ranges depending on scope and complexity.
If your existing cameras support RTSP/ONVIF and have sufficient image quality and positioning, we can often reuse them. We usually perform an assessment to confirm suitability.
Yes. We design the AI-OCR pipeline to support both Thai and English, including various invoice and tax document formats from multiple vendors, tuned to your document patterns.
Yes. We typically design a web API or service layer to integrate with your existing ERP, accounting, or line-of-business systems, so you don’t have to replace everything at once.
Typically you’ll need representatives from business/users, IT/systems, and at least one decision-maker for strategic topics. We help clarify roles and collaboration tailored to your organization size.
We usually start with repetitive, high-volume processes where data exists and impact is measurable—for example, document data entry time, daily document volume, or production downtime on the shop floor.
We help you collect and use only necessary data, minimize sensitive data usage, and design proper consent, access, and protection practices aligned with PDPA principles.
A typical proof-of-concept takes about 1–3 months, while full production deployments with integrations and dashboards usually take 3–6 months depending on complexity.
Pricing depends on scope, AI model complexity, required integrations, and ongoing support. You can start small and expand in clearly defined phases.
Typical KPIs include hours saved per month, reduction in data-entry errors, reduced production downtime, or faster document/report turnaround. We define these KPIs together from day one.
The flow usually is: 1) Requirements & discovery 2) Architecture & data design 3) PoC/prototyping 4) Refinement & feature expansion 5) Integration & production rollout 6) Monitoring, tuning, and continuous improvement.
Yes. We can set up a support & maintenance plan to handle incidents, tune AI models, and add incremental features, tailored to your team and budget.
It depends on your policy. We support cloud, on-premise, or hybrid deployments. Our stack (.NET, Blazor, containers, ONNX, etc.) is flexible for different environments.
Our goal is to reduce repetitive and manual tasks so your team can focus on higher-value work like analysis and customer interaction. It’s about augmenting people, not instantly replacing them.
Many SMEs see value from AI faster than large enterprises because they decide and adapt more quickly. We help you pick the right use case and grow in small, practical steps.
Yes. We offer workshops and consulting sessions to define your AI vision, roadmap, and project priorities before you commit to full technical implementation.
Yes. Knowledge transfer is important to us. We can train your team on using the system, reading dashboards, and understanding the AI/data architecture so you can maintain and extend it later.
