Artificial intelligence development services

AI runs on your data, on your infrastructure.

HiTechCloud builds virtual assistants, internal document Q&A, text processing and computer vision — deployed on GPU Cloud in Vietnam so your data never leaves your own infrastructure.

Discuss your AI problem GPU Cloud cho AI

Capabilities: RAG · Virtual assistants · NLP · Computer vision
Infrastructure: NVIDIA GPU Cloud operated by HiTechCloud
Data: Stored and processed in-country
Model: Self-hosted open models or third-party APIs

Overview

The biggest barrier to enterprise AI is data, not models

The strongest models today are a few lines of code away over an API. What sinks an enterprise AI project is rarely model quality — it is internal data that is scattered, dirty and without clear access rules, and a legal team that will not let it leave the building.

That is exactly where HiTechCloud focuses: standardizing internal data onto a platform with proper permissions, and only then building the AI application on top. For sensitive data, open models run on our GPUs in Vietnam and the data never leaves your infrastructure.

We also say plainly when a problem does not need AI. Many "build us a chatbot" requests are better solved by reorganizing the documents and building decent internal search.

Common problems

Why so many AI projects stop at the demo

Company data cannot leave the building

Contracts, customer files and internal documents cannot be uploaded to a public service, so the pilot never becomes production.

The demo ran on clean data

The model performs well on sample documents, then drops sharply on real scans and real forms.

Nobody can measure how accurate it is

Without a test set and acceptance criteria, arguments about quality come down to impressions.

No GPU capacity for the long run

Training and inference need GPUs continuously; renting by the hour abroad is hard to accept on both cost and latency.

Service

Groups of AI solutions

Internal document Q&A (RAG)

An assistant that answers from your own documents, procedures and contracts, cites its sources and respects each user's permissions.

Virtual assistants and customer care

Answering repeat questions automatically on the website, phone line and chat, handing over to a person when it goes beyond scope.

Document and form processing

Extracting data from invoices, receipts, contracts and scanned forms straight into accounting or ERP.

Computer vision

Detecting product defects on the line, counting and sorting, and monitoring workplace safety through existing cameras.

Analytics and forecasting

Demand forecasting, transaction anomaly detection and customer segmentation from your own historical data.

Model serving and MLOps

Deploying models to GPU, versioning them, monitoring output quality and scaling with real query volume.

Process

How an AI project is delivered

01
Defining the problem and the criteria

Agree what the AI must achieve and how it will be measured — before discussing models or technology.
02
Data preparation

Collecting, cleaning, classifying and setting permissions on the internal data to be used.
03
Trials with verifiable results

Building a trial on real data and measuring accuracy and cost per query at the expected scale.
04
Deployment and monitoring

Going live on GPU Cloud, monitoring output quality and improving on real user feedback.

Technology

The technology stack we use

Model

Self-hosted open models
Commercial model APIs
Fine-tuning

Platform

Vector database
RAG pipeline
AgentBase
MLOps

Infrastructure

GPU Instance
GPU Cluster
Object Storage
Kubernetes

Integrations

REST API
Webhook
ERP / CRM
Internal chat channels

Customer

Who this service is for

Businesses with a large internal document base

Staff lose time searching contracts and procedures for information.
The documents may not be uploaded to a public service.
Answers have to cite their source so they can be verified.

Businesses processing many forms and documents

Invoices, slips and records are still keyed in by hand.
Scan quality varies between sources.
You need accuracy criteria agreed before a wider rollout.

Engineering teams that need GPU capacity

You have the model and the data but no GPUs running continuously.
Renting GPUs abroad raises cost and latency problems.
Training data has to stay on infrastructure in Vietnam.

Why choose HiTechCloud

What HiTechCloud brings

Data stays in-country

For sensitive data, open models run on GPUs in a Vietnamese data center, meeting the personal data protection requirements of Decree 13/2023/ND-CP.

GPU is a service we already run

We run GPU Cloud for customers daily, so the cost and compute capacity for an AI project are known upfront rather than resold.

An honest assessment

If the problem does not need AI yet, or the data is too thin for a model to be reliable, we say so and propose preparation work instead of shipping something unusable.

FAQ

Is our company data used to train public models?

No. With open models deployed on HiTechCloud GPUs, the data stays entirely inside your infrastructure. If a project uses a third-party API, we set out that provider's data-handling terms so you can decide before deployment.

How much data do we need to start?

For document Q&A, your existing documents are enough to start — no model retraining needed. Forecasting or computer vision needs historical data or labeled images; how much is assessed during the survey.

Roughly what does running an AI system cost?

GPU or API-call costs, plus storage and operations. During the trial we measure real cost per query and project it against expected user numbers, so you can decide whether to scale.

Can the AI assistant give wrong answers?

It can, which is why our systems always cite the source document for each answer, restrict answers to internal data, and hand over to a person when confidence is low.