Generative AI Solutions

Large language models are genuinely capable at a specific class of work: reading unstructured text, drafting, summarising, extracting structure from documents, and holding a useful conversation over material they have been given. They are also confidently wrong on a regular basis, and no amount of prompt engineering removes that.

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10+Years of Industry Expertise 200+Talented Developers 90%Customer Satisfaction Rate

ISO 9001:2015 Certified ISO 27001:2013 Certified

What Clients Say About Our Generative AI Solutions Services

Independent reviews from Clutch and DesignRush. Every review below is shown in full and links back to its source platform so you can confirm it yourself.

4.9/5.0 12 reviews View all reviews on Clutch

5.0/5.0 6 verified reviews View all reviews on DesignRush

5.0

May 2026

“Their balance between technical expertise and product thinking stood out.”

AgileTech Viet Nam developed an AI-powered analytics platform for an AI visibility and GEO solutions company. The platform featured real-time reporting dashboards and data processing pipelines. AgileTech Viet Nam’s work improved the client’s data processing efficiency and the accuracy of AI-generated insights. The platform was user-friendly and supported the client’s first large-scale deployments. AgileTech Viet Nam’s team was communicative, adaptive, and technically proficient.

Improved data-processing efficiency and AI insight accuracy

5.0

Nov 2024

“We saw huge cost savings and optimized stock levels, so we consider AgileTech a partner in our company transformation.”

AgileTech created a working Supply Chain Management (SCM) system that has helped our supermarket chain simplify operations across inventory management, demand forecasting and logistics. They integrated our existing systems, automated inventory and logistics and gave us robust demand forecasting with AI, we saw huge cost savings and optimized stock levels. And they continued to deliver on quality post launch with regular updates and training sessions, so we consider AgileTech as a partner in our company transformation.

AI-driven demand forecasting across inventory and logistics

5.0

Jun 2025

“Their proactive communication, flexibility, and technical expertise stood out throughout the project.”

AgileTech Viet Nam used React Native to develop an iOS and Android app for a real estate platform. The team integrated the app with the client’s backend and implemented property search features. AgileTech Viet Nam’s high-quality work resulted in a 30% increase in user registrations and over 1,000 app downloads within the first month. The team delivered on time, provided regular updates, and responded to feedback. Their flexibility and proactive communication impressed the client.

30% more sign-ups and 1,000+ downloads in the first month

5.0

May 2026

“They followed a structured Agile approach with clearly defined milestones.”

AgileTech Viet Nam developed a web-based logistics management system for an automotive parts supplier. The team integrated the platform with third-party providers and built a real-time tracking dashboard. AgileTech Viet Nam’s work reduced manual processing by 55%, improved real-time shipment visibility, and increased operational efficiency across multiple departments. The team followed a structured Agile approach, communicated consistently, delivered on time, and was flexible.

Manual processing reduced by 55% with real-time shipment visibility

5.0

May 2026

“They were not just executing tasks but were genuinely invested in the product’s success.”

AgileTech Viet Nam developed an MVP for a digital solutions provider’s marketplace platform. They created the UI/UX design, developed the front- and backend, and integrated payment systems. AgileTech Viet Nam successfully launched the MVP on time, allowing the client to onboard 500 users within two months. The platform was stable with minimal issues. Moreover, the team was collaborative, flexible, quick to adapt to changing priorities, and genuinely invested in the project’s success.

MVP launched on time, 500 users onboarded within two months

5.0

May 2026

“Their ability to handle both high-level architecture and hands-on implementation is rare.”

An IT services company hired AgileTech Viet Nam to re-architect their enterprise SaaS platform. The team conducted a technical audit, designed a new architecture, migrated legacy data, and added API gateways. Thanks to AgileTech Viet Nam’s work, the client saw improvements in system scalability, platform uptime, and deployment time. The team followed the Agile methodology, communicated effectively, and was responsive to the client’s feedback. AgileTech Viet Nam helped the client make better decisions.

Improved scalability, uptime, and deployment time on an enterprise SaaS

5.0

May 2026

“They were flexible in accommodating evolving business needs while still keeping the project on track.”

AgileTech Viet Nam developed a digital platform for a real estate firm. They designed the UI/UX, implemented advanced search and filtering functionalities, integrated dashboards, and automated reporting tools. The platform improved operational efficiency, business performance, lead conversion rates, and customer responsiveness. AgileTech Viet Nam implemented a well-structured project management approach, meeting timelines and responding to feedback with flexibility. Their tailored solutions stood out.

Improved efficiency, business performance, and lead conversion

5.0

Mar 2026

“The team demonstrated strong professionalism, reliability, and a proactive approach throughout the project.”

AgileTech Viet Nam developed a scalable engineering workflow system for an ICT company. The goal was to streamline internal processes, improve cross-team visibility, and enhance coordination. The system significantly improved operational efficiency by reducing manual processes and increasing transparency across teams for the client. AgileTech Viet Nam set clear milestones and provided regular updates to ensure everyone remained in sync. Their professionalism and reliability stood out.

Scalable engineering workflow system with fewer manual processes

5.0

Nov 2024

“The platform has greatly improved our operational efficiency, and we wholeheartedly recommend AgileTech.”

Working with AgileTech has been an amazing experience, especially when it comes to creating a personalized e-learning program for our IT services business. We were pleasantly surprised by how adaptable and scalable the platform developed by the AgileTech Team was. Their proficiency with user-friendly course administration and community participation technologies enabled us to provide a thorough instructional resource catered to our customers’ particular requirements. The platform has greatly improved our operational efficiency, and we are amazed by its capacity to manage heavy traffic with ease and offer comprehensive reporting and analytics. The platform remained successful and user-friendly because of the team’s dedication to continuous support and their quick response to criticism. For any organization looking to create innovative and reliable digital solutions, we wholeheartedly recommend AgileTech as a partner.

Personalized e-learning platform that handles heavy traffic

5.0

Nov 2024

“They used a potent tech stack including PHP, React, and AWS to guarantee perfect scalability and zero downtime.”

AgileTech provided a superb Learning Management System (LMS) that creatively and precisely addressed the intricate needs of our organization. Within a scalable, completely customizable platform, the LMS made it possible to create comprehensive courses, administer student tests, engage the community, and obtain real-time data insights. AgileTech’s modular and agile approach made sure that every component, from the user-friendly course builder to the sophisticated analytics and communication tools, was customized to meet our demands despite the difficulties of developing such a feature-rich system. In order to guarantee perfect scalability and zero downtime, even with high user concurrency, AgileTech used a potent tech stack that included PHP, React, and AWS. Their post-launch assistance, which included frequent training sessions and upgrades, gave our staff the means to fully use the LMS’s potential. This project was a testament to AgileTech’s technical skill, rapid deployment, and dedication, making them an outstanding choice for educational technology development.

Feature-rich LMS with course builder, analytics, and comms

5.0

May 2026

“Their professionalism, responsiveness, and ability to execute efficiently made them feel like an operational partner.”

AgileTech Viet Nam optimized a workflow for a consulting firm. The team improved lead management processes and streamlined internal communication systems across multiple departments. AgileTech Viet Nam’s work improved the client’s operations, customer communication process, and overall workflow efficiency. The team was strategic and reliable. Moreover, AgileTech Viet Nam worked closely with the client to understand their business objectives and recommend solutions.

4.5

Apr 2021

“They are incredible to work with and are truly passionate about technology and our concept.”

AgileTech Viet Nam provides mobile app development services for a social media startup. They are building the app using Node.js and Flutter. With the ongoing partnership, AgileTech Viet Nam’s passion has impressed the client so far. The team is truly into technology, and their concept makes the client feel confident about the project’s continuous success.

4.0

Jun 2019

“I was impressed with the flow of the project.”

AgileTech Viet Nam developed a mobile app from scratch. After helping with scoping and objective formation, AgileTech created design mockups and developed the full iOS and Android solutions. Valuable user data is being gathered in ongoing beta tests, which will soon lead to growth and expansion opportunities. AgileTech Viet Nam provided consistent communication and established a smooth workflow. Issues were raised proactively, allowing fixes to come earlier in the process.

5.0

Nov 2024

“AgileTech excelled in supporting our project from initial mock-ups through to the final delivery on the App Store.”

AgileTech excelled in supporting our project from initial mock-ups through to the final delivery on the App Store, incorporating data analytics that we could easily share with our customers. The scope of work included mock-up creation, UI/UX design, interactive prototyping, app development, rigorous testing, and setting up a secure production environment on AWS, culminating in the apps release on both iOS and Android platforms.

5.0

Nov 2024

“Since we launched our new site we’ve seen a significant increase in traffic and sales, which proves their work is effective.”

We’ve had an amazing experience with AgileTech for our eCommerce platform. The team worked with us to build an user friendly online store that met all our business needs. Their eCommerce development expertise was evident from the start as they shared valuable insights on how to maximize sales. The AgileTech team are not only talented but also care about their clients success. Since we launched our new site we’ve seen a significant increase in traffic and sales which proves their work is effective.

5.0

Nov 2024

“Their attention to detail in web development and UI/UX design has given us a site that looks great and works flawlessly.”

Working with AgileTech has been a game changer for us. We wanted a modern and responsive website and AgileTech delivered more than we expected. The team was super collaborative and involved us in every step of the process. Their attention to detail in web development and UI/UX design has given us a site that looks great and works flawlessly. The AgileTech team members are not only talented professionals but also great communicators, so we could share our vision and ideas easily. They were so committed to our success and the end result has increased our user engagement and satisfaction so much.

Explore all 16 client testimonials

Benefits of Generative AI Solutions

Explore the benefits AgileTech Vietnam delivers for every generative ai solutions engagement.

Answers From Your Own Documents

A general model knows nothing about your policies, contracts, or product documentation. Retrieval augmented generation grounds responses in your own governed content and cites the source, so an answer can be verified rather than trusted. This does not eliminate wrong answers, but it makes them checkable, which is the difference between a usable system and an unusable one.

Structure Extracted From Unstructured Text

Invoices, contracts, claims, forms, and email arrive as text and have to become fields in a system. Language models handle the variation in wording that defeats template-based extraction, which makes them well suited to this work, provided extracted values are confidence-scored and low-confidence cases routed to a person rather than written straight through.

Drafting That Removes the Blank Page

Producing a first draft of a response, summary, or description is where these models are most reliable and least risky, because a person reviews the output before it is used. The productivity gain is real and the failure mode is contained, which is why we usually recommend starting here rather than with an autonomous customer-facing system.

Our Generative AI Development Process

Six phases from use case assessment to a monitored production system, with the containment of wrong answers designed in rather than discussed afterwards.

Use Case Assessment and Risk Framing

We establish what the system will do, what a wrong answer would cost, and who would notice. This determines everything that follows: whether output can be used directly or must be reviewed, how much grounding is required, and whether the use case is appropriate at all. Some are not, and we say so at this stage.

Content Preparation and Grounding Design

The documents the system will draw on are inventoried, cleaned, chunked, and indexed, with access controls preserved so the system cannot surface content a user is not entitled to see. Source quality governs answer quality more than model choice does, and contradictory or outdated documents produce contradictory answers.

Evaluation Set Construction

Before building, we assemble a set of representative questions with known correct answers, including the awkward cases and the questions the system should refuse. Without this there is no way to tell whether a change improved the system or simply changed it, and iteration becomes guesswork.

System Development

Retrieval, prompting, tool use, and any workflow integration are built and tuned against the evaluation set. Model selection is made on measured performance for your task and your cost and latency constraints, not on general benchmark standing.

Human Review and Fallback Design

We design what happens when the system is uncertain, when retrieval finds nothing relevant, and when a user disputes an answer. Confidence thresholds, escalation paths, and clear disclosure that a response is machine generated are part of the build, because a system without them will eventually assert something false to someone who acts on it.

Deployment, Monitoring, and Iteration

Cost per interaction, latency, refusal rates, and user-reported errors are monitored after launch. Model providers update their models and your content changes, so periodic re-evaluation against the same set is how you detect regression rather than being told about it by a user.

AgileTech Vietnam: Your Generative AI Partner

The reasons global companies trust AgileTech Vietnam to deliver this service, from deep expertise to uncompromising quality and security.

Honest About What These Systems Do Wrong

Language models produce fluent, confident, incorrect output as a property of how they work, not as a defect to be engineered away. We design for containment and we will tell you when a use case cannot tolerate it, rather than presenting grounding as if it were a guarantee.

Evaluation Before Iteration

We build a set of representative cases with known answers before tuning anything. Teams that skip this iterate on impressions, cannot tell improvement from change, and have no way to detect regression when a model provider updates.

Grounded in Governed Content

Answers are drawn from your own documents with sources cited and access controls preserved. Content quality determines answer quality, so document preparation is treated as substantive work rather than a preliminary step.

Human Review Designed In

Confidence thresholds, escalation paths, and disclosure that output is machine generated are part of the system design. Where output can materially affect a person, a review path is a requirement rather than an enhancement.

Cost and Latency Made Visible

Per-interaction cost and response time are measured during development and monitored afterwards. These systems can be surprisingly expensive at volume, and discovering that after launch is a poor way to learn it.

Model Choice on Merit

We select and can switch between providers and open models based on measured performance for your task, your cost ceiling, and your data residency requirements. We avoid architectures that make changing provider prohibitive.

10+ years delivering software and AI engineering for start-ups, SMEs, and enterprises.
200+ talented developers across engineering, data, cloud, and QA disciplines.
300+ projects completed with 90% customer satisfaction rate.
Refined Quality Management practices verified by ISO 9001:2015
Powerful Security Management practices supported by ISO 27001:2013
Named Top App Developer in Vietnam by Clutch.co

Technologies We Work With

The models, retrieval infrastructure, and evaluation tooling we build with. We avoid architectures locked to a single provider, because model capability and pricing shift quickly.

Models

OpenAI
Anthropic Claude
Google Gemini
Llama
Mistral

Retrieval

pgvector
Pinecone
Qdrant
Elasticsearch

Orchestration

LangChain
LangGraph
LlamaIndex

Evaluation & Ops

Ragas
LangSmith
Python
Docker

AgileTech Vietnam team in the Hanoi office

Who can Benefit from AgileTech's Generative AI Solutions?

Companies With Knowledge Locked in Documents

Policies, procedures, contracts, and technical documentation accumulate faster than anyone can index them, and the practical result is that staff ask a colleague instead of searching. The knowledge exists and is unreachable.

Companies Processing High Volumes of Unstructured Text

Invoices, claims, applications, and inbound email have to become structured records, and the variation in how humans write defeats template-based extraction. This is usually handled by people doing repetitive reading.

Companies Supporting Customers or Staff at Scale

Support functions handle a long tail of questions that are individually simple and collectively expensive. Assistance is a genuine fit, but the risk profile of a customer-facing assistant differs sharply from an internal one.

Best Practices for Choosing your Generative AI Partner

Six practical steps to evaluate and select the right partner for your project.

Ask what the system will get wrong

A partner who describes only capabilities has either not deployed one of these systems in production or is not telling you about it. Expect a direct account of the failure modes and how each is contained.

Require an evaluation set as a deliverable

Ask how the system will be measured and insist on a set of representative cases with known answers, built before tuning. Without it, iteration is guesswork and regression is invisible.

Establish where the human review sits

For any output that affects a person or a record, ask what triggers escalation, who reviews, and how users know they are reading machine-generated text. Answers to these should already exist in the design.

Check data handling and residency

Establish which provider processes your data, whether it may be retained or used for training, and whether that satisfies your obligations. For regulated or sensitive content this may rule out some providers entirely.

Ask about cost at your expected volume

Per-interaction cost multiplied by real usage produces figures that surprise people. Ask for a projection at your expected volume and for what happens if usage is much higher than planned.

Avoid architectures locked to one provider

Model capability and pricing shift quickly. Ask how difficult switching providers would be, and treat an answer of very as a significant cost to be weighed now rather than later.

Flexible Engagement Models

Choose the engagement model that fits your project size, budget, and timeline. All models include dedicated communication channels and transparent progress tracking.

Frequently Asked Questions

How is generative AI different from your machine learning service?

Generative AI works with language and content: assistants, retrieval over documents, drafting, summarising, and extraction from unstructured text. Machine learning as covered on our other page works with structured data to predict numeric or categorical outcomes such as demand, risk, or churn. They use different techniques, need different inputs, and fail in different ways. If your problem is a number, that page applies. If it is language, this one does.

Will the system make things up?

Sometimes, yes. Producing fluent and confident text that is incorrect is a property of how these models work, not a defect that can be fully engineered away. Grounding responses in your own documents and citing sources reduces it substantially and makes remaining errors checkable, but does not eliminate them. Any supplier telling you their system does not hallucinate is either mistaken or misleading you.

What is retrieval augmented generation and do we need it?

It means the system retrieves relevant passages from your own content and answers using them, citing what it used, instead of relying on what the model absorbed during training. If the system needs to answer questions about your specific policies, products, or documents, you need it. If it is only drafting generic text, you may not.

How is this different from RPA?

Robotic process automation executes deterministic, rule-based steps and does exactly the same thing every time, which is what you want for a defined process. Generative AI handles steps requiring language judgement, where the input varies and no rule can cover it, and it does not guarantee identical output for identical input. Many real workflows want both: RPA for the deterministic path, a language model for the step that needs interpretation. Our RPA development page covers the first.

What does it cost to run?

Cost is driven by interaction volume, the amount of content sent with each request, and the model chosen, and it can rise faster than expected once usage grows. We measure cost per interaction during development and project it at your expected volume rather than quoting a figure here, because the range across use cases is very wide.

Is our data used to train the provider models?

That depends on the provider and the contract tier. Business and enterprise agreements from the major providers generally exclude customer data from training, but the terms differ and change. We establish which provider processes your data, under which terms, and whether it satisfies your obligations, before anything is built. Where the answer is unacceptable, self-hosted open models are an alternative we can assess.

Can it work in languages other than English?

Yes, though performance varies considerably by language and by model, and it is generally weaker for languages with less representation in training data. Where multilingual performance matters we test it explicitly against your own content rather than relying on the provider claim.

How do you stop the system revealing information a user should not see?

Access controls are applied at retrieval, so the system can only draw on content the requesting user is entitled to see. Applying permissions after generation is unsafe, because the content has already influenced the answer. This is a design decision made at the start and is one of the first things worth asking any supplier about.

What happens when the model provider updates their model?

Behaviour can change without warning, including on cases that previously worked. This is why we build an evaluation set: it can be re-run against a new model version to detect regression before your users encounter it. Systems without one discover the change through user complaints.

How do we get started?

Describe a task in your organisation that involves reading or writing text at volume, and tell us what a wrong answer would cost. We will assess whether it is a suitable use case, propose the smallest version worth building, and tell you plainly if we think the risk cannot be contained well enough to proceed.

Trusted and Recognized

Independent directories, certification bodies, and award programs that have assessed our work. Each badge links to its source or names the standard behind it, so every claim on this page can be checked at first hand.

Top Software Development Company - GoodFirms

TechBehemoths Awards - Mobile App Development

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