AI & Data Solutions
AI that fits into daily work,
connected to your data and business systems.
The value of AI in an enterprise comes from its connection to documents, records and processes under the correct permissions. InterCloud builds assistants, document pipelines, analytics and automation that fit into daily work, in Thai and English.
AI Solutions
Assistants and Copilots Based on Your Company Information
These solutions are built on the same foundation as InterCloud AI, the assistant used across our own products, and can be deployed on your own data with your own access rules.
Knowledge assistants
Assistants answer staff and customer questions from policies, manuals, contracts and tickets, with citations to the source document. Answers respect the permissions of the person asking, and an assistant does not reveal content that the user could not open directly.
Document understanding
Reduce manual data entry from documents. Fields are extracted from invoices, purchase orders, identity documents and forms, incoming mail is classified and items that require human review are flagged. The pipelines are built and tested on English and Thai documents, including mixed-language layouts and scanned pages.
Copilots inside applications
Drafting, summarizing and suggesting next steps inside the business system where the work takes place, for example a summary of a case file, a proposed reply to a customer or a form prefilled from an e-mail. Users review and confirm each suggestion, and the system records what was suggested and what was accepted.
Language support
Staff and customers work with the assistant in Thai or English. Prompts, retrieval and evaluation are tested in both languages, including Thai word spacing, numerals and formal register. Voice input and avatar interfaces are available where a conversational channel is appropriate, as in the InterCloud AI Avatar.
Data Analytics
Management Dashboards and Forecasting on Governed Data
We build the data pipeline and the agreed definitions first and the dashboards afterwards, because reporting projects fail on data quality and timing. Data from GlowScale, Orbio, your existing ERP and external sources is loaded into a governed warehouse with history retained.
On this foundation we build role-based dashboards, scheduled reports and self-service exploration for analysts. Where the data supports it, we add forecasting for demand, cash flow or capacity, with the confidence range displayed alongside each forecast.
Analytics and AI share the same data layer, so an assistant answers a question about a branch's results for the last quarter from the same figures as the management report.
- Pipelines from applications, databases, files and APIs into a central warehouse
- One definition per measure, documented and reused across every report
- Executive, management and operational dashboards with scheduled delivery
- Forecasting and anomaly detection where the data supports it
Automation
Less Repetitive Work in Back-Office Processes
Most back-office work consists of a chain of small, rule-based steps between systems. We automate the chain and leave decisions that require judgment to people.
Workflow automation
Cases move forward without manual follow-up. Approvals, reminders, escalations and status updates are triggered by events in your systems, with every step logged and a clear owner assigned when a case falls outside the rules.
Document processing
Incoming documents reach the ERP without manual entry. Invoices, orders and forms are read, matched against purchase orders or master data and posted, with exceptions routed to a reviewer so that the queue is not blocked.
System-to-system
Scheduled and event-driven integrations replace manual exports and re-keying between ERP, CRM, e-commerce and government portals, with retries and alerts included.
Communications
Every message sent is recorded on the customer or case record. Personalized e-mail, LINE and in-app messages are generated from templates and data and sent on the defined trigger.
AI in the loop
Language models classify, extract and draft within the flow where rules are insufficient, with confidence thresholds that determine when a person must confirm before a result is committed.
Measured results
Each automation reports volume, cycle time and exception rate, so that your team can see what it handles, where it stalls and whether the next step is worth automating.
MLOps
Models and Assistants Maintained and Monitored After Launch
A model or an assistant prompt is software that depends on data which changes over time. We treat it accordingly: it is versioned, tested against an evaluation set, deployed through the same pipeline as the rest of the system and monitored in production.
Evaluation sets are built with your subject-matter experts and include Thai-language cases and known edge cases. Every change to a model, prompt or retrieval step is scored against them before release, and the results are retained so that regressions remain visible.
In production we monitor latency, cost, answer quality signals and data drift, and we feed user corrections back into the evaluation set. Models can be hosted on AWS in your account where data residency requires it.
- Versioned models, prompts and retrieval configurations with release notes
- Evaluation sets reviewed by your experts, run on every change
- Production monitoring for quality, latency, cost and drift in SnowTrack
- Deployment on AWS, in your account where data residency requires it
Responsible AI & Data Governance
Commitments Your Compliance and Audit Teams Can Verify
The use of AI in an enterprise depends on trust. We build the following commitments into the design and document them so that your compliance and audit teams can verify them.
- Access control carried through to AI: assistants see only what the asking user is permitted to see
- Personal data handled under data-protection principles: minimization, purpose limitation and retention rules in the pipeline
- A human confirms before an AI-generated result is committed to a financial, legal or customer-facing record
- Every AI interaction logged with the sources used, so that answers can be traced and audited
- Evaluation for accuracy and bias on Thai and English cases before release, repeated on every change
- Clear disclosure to users when they are interacting with an assistant rather than a person
- Model and data hosting choices documented, with options to keep data within your AWS account
- A documented owner for each model and assistant, responsible for monitoring and retirement
Start with one process and a measurable result
Successful AI projects begin with a single process and a measurable result. Describe where manual work accumulates in your organization, and we will suggest a starting point.