AI Solutions
Generative AI Services
Design, build and run secure generative AI solutions, from knowledge assistants and copilots to autonomous agents, grounded in your own data and measured against real business outcomes.
Future-Proof Your Business with Generative AI
Large language models can now read contracts, answer customer questions, draft reports and write code. The organizations getting real value from them are not the ones with the most pilots; they are the ones that connect generative AI to well-defined processes, trusted data and clear success metrics.
Hanoi Technology helps you do exactly that. We identify the use cases worth building, design architectures that keep your data private, evaluate quality before anything reaches users, and operate solutions in production with monitoring for accuracy, latency and cost.
The Challenge
Why Most Generative AI Pilots Never Reach Production
Generative AI is easy to demo and hard to industrialize. These are the obstacles we help clients overcome.
Hallucinations and Unreliable Answers
Models produce fluent but wrong answers when they lack context. Without grounding in your own content and systematic evaluation, users quickly lose trust.
Data Privacy and Security Concerns
Legal and security teams need to know where data is processed, who can see it and whether it is used for training. Unclear answers stop projects at the approval stage.
Unpredictable Costs
Token usage grows quickly with long prompts, large documents and agent loops. Without routing, caching and budgets, costs can outpace the value delivered.
No Path from Prototype to Production
A notebook prototype lacks authentication, logging, monitoring, error handling and integration with the systems where work actually happens.
Business Value
Redefine Business Value with Generative AI
Fuel Growth with GenAI-Powered Products
Add assistants, smart search and content generation to your products to differentiate them, increase engagement and open new revenue streams.
Unlock Insight from Unstructured Data
Search, summarize and extract structured facts from documents, emails, tickets and call transcripts that were previously too costly to analyze.
Automate Knowledge Work End to End
Combine language models with workflows and tools to draft, check and route work, so employees review results instead of doing repetitive steps.
Deliver Personalized Experiences at Scale
Tailor answers, recommendations and communication to every customer in their own language, around the clock.
Enter the Era of Generative AI
Generative AI is moving from experimentation to everyday operations. The winners will be organizations that treat it as an engineering discipline, with architecture, evaluation, governance and cost management, rather than as a series of isolated experiments.
Our Services
Hanoi Technology’s End-to-End Generative AI Services
One partner from the first workshop to a monitored production system.
GenAI Strategy Consulting
We run discovery workshops with business and IT stakeholders, map candidate use cases, score them on value, feasibility and risk, and define a roadmap with measurable success criteria.
- Use-case backlog and value scoring
- Data and architecture readiness review
- Governance and responsible AI guidelines
Retrieval-Augmented Generation (RAG)
We build assistants and search experiences that answer from your documents, knowledge bases and databases, with citations, access control and freshness built in.
- Document ingestion, chunking and indexing
- Hybrid vector and keyword search
- Answers with sources and permission filtering
AI Agents and Copilots
We design agents that plan multi-step tasks and call your APIs, such as looking up orders, updating CRM records or preparing reports, with human approval for sensitive actions.
- Tool and API integration
- Approval steps and guardrails
- Full activity logging and replay
Custom Models and Fine-Tuning
When prompting and retrieval are not enough, we fine-tune open or commercial models on your data for tone, format or domain accuracy, and compare them objectively against baselines.
- Training data preparation
- Fine-tuning and distillation
- Benchmarking against base models
LLMOps and Evaluation
We put generative AI under engineering control with evaluation datasets, regression tests, prompt versioning and production monitoring.
- Golden test sets from real tasks
- Automated quality and safety checks
- Dashboards for accuracy, latency and cost
Augmented GenAI Teams
Add experienced generative AI engineers, data engineers and solution architects to your team for as long as you need them.
- Flexible team composition
- Knowledge transfer to your staff
- Working-hour overlap with your time zone
Our Approach
From Idea to Production in Five Steps
A proven path that proves value early and avoids expensive dead ends.
- 01
Discover and Prioritize
Workshops with business owners to collect use cases, quantify their value and confirm data availability. We select one or two candidates with the best balance of impact and feasibility.
Deliverables: Prioritized use-case list · Success metrics · High-level architecture
- 02
Build an Evaluation Set
Before writing prompts, we collect representative questions and tasks with expected answers. This becomes the yardstick for every design decision that follows.
Deliverables: Golden dataset · Quality criteria · Baseline measurements
- 03
Prototype and Compare
We build a working prototype, test two or three model and retrieval options against the evaluation set and share the results transparently.
Deliverables: Working prototype · Model comparison report · Cost per transaction estimate
- 04
Harden for Production
We add authentication, permissions, logging, guardrails, error handling and integration with your systems, then run security and user acceptance testing.
Deliverables: Production-ready application · Security review · Runbook and documentation
- 05
Launch, Monitor and Improve
After go-live we monitor accuracy, user feedback, latency and spend, review failure cases regularly and roll out improvements safely.
Deliverables: Monitoring dashboards · Monthly improvement reports · Roadmap for further use cases
Use Cases
Generative AI Use Cases We Deliver
Examples of solutions our clients use every day.
Customer Service Assistant
Answers routine questions from your help center and order data, and hands complex cases to agents with a summary and suggested reply.
Enterprise Knowledge Search
Lets employees ask questions in natural language across policies, manuals, wikis and shared drives, with answers that cite their sources.
Document Processing
Extracts fields from invoices, contracts, claims and applications, checks them against business rules and flags exceptions for review.
Sales and Proposal Copilot
Drafts proposals, emails and account summaries from CRM data and past documents, ready for a salesperson to refine.
Maintenance and Field Assistant
Gives technicians step-by-step guidance from manuals and service history on their phone or tablet, even in the field.
Report and Insight Generation
Turns dashboards and data extracts into written commentary for management reports, highlighting trends and anomalies.
Technology
Models and Tools We Work With
We are model-agnostic and choose per use case based on quality, privacy, latency and cost.
- Azure OpenAI
- OpenAI GPT models
- Anthropic Claude
- Google Gemini
- Amazon Bedrock
- Llama and Mistral open models
- LangChain
- LlamaIndex
- Semantic Kernel
- pgvector
- Azure AI Search
- Pinecone
- Python
- TypeScript
Platforms
Deploy Generative AI Securely on Your Preferred Platform
- Microsoft AzureModernize data, analytics and AI within your Azure tenant.Explore
- AWSBuild, migrate and run data and AI workloads on Amazon Web Services.Explore
- Google CloudBigQuery, Looker and Vertex AI for data-driven organizations.Explore
- DatabricksUnified lakehouse for data engineering, analytics and AI.Explore
Why Hanoi Technology
Why Organizations Choose Hanoi Technology
Senior, Cross-Functional Teams
Strategy, data, AI, cloud and engineering specialists work as one team, so nothing is lost between hand-offs.
Proven Delivery
More than 50 projects delivered for clients in North America, Europe and Asia-Pacific.
Radical Transparency
A written update every week and a demo of working software every sprint, so you always know where things stand.
Cost-Effective Global Delivery
Experienced engineers from our Hanoi delivery hub, with working-hour overlap for every major time zone.
Success Story · SaaS
AI support assistant and smart escalation for a global SaaS company
A project-management SaaS provider receiving thousands of support requests a day needed faster answers without growing the support team. We built an AI assistant grounded in its knowledge base, with escalation to human agents.
Read the Full StoryMore Success Stories
HR tech
80%faster hiring processAI candidate screening and matching for an HR tech startup
An HR tech startup processing thousands of applications a month needed recruiters to spend less time screening CVs. We built resume parsing, job matching and an automated shortlisting workflow.
E-commerce and retail
+32%conversion ratePersonalized shopping experience for a US fashion retailer
A fast-growing US fashion and lifestyle retailer had plenty of traffic but a 2.1% conversion rate. We combined a recommendation engine with a simpler path from discovery to checkout.
Resources
Featured Resources
Industry trends and practical guidance to help you get more value from data, AI and technology.
FSMA 204 After the Delay: Using Shipment Monitoring to Get Traceability-Ready by 2028
The FDA Food Traceability Rule deadline moved to July 2028, but the data work is large. Here is how to use shipment monitoring and event data to get ready without wasting the extra time.
CSRD After the Omnibus: What the Revised ESRS Mean for Reporting From 2027
The Omnibus and the simplified ESRS reshaped EU sustainability reporting in 2026. Here is who is in scope, what changed in the standards and how to adapt your data and process.
OpenTelemetry in 2026: Building a Vendor-Neutral Observability Strategy
OpenTelemetry has become the default way to collect telemetry. Here is what changed in 2026 and a practical roadmap for a vendor-neutral observability stack.
Find the Generative AI Use Cases Worth Building
Book a free consultation with our generative AI team. In one session we will review your ideas, data and constraints and suggest where to start for the fastest measurable result.
Generative AI Services FAQ
What generative AI services does Hanoi Technology offer?
We cover the full lifecycle: strategy and use-case discovery, retrieval-augmented assistants, AI agents and copilots, fine-tuning of custom models, LLMOps with evaluation and monitoring, and augmented teams of generative AI engineers. You can engage us for a single phase or end to end.
How do we identify a use case that will benefit from generative AI?
Good candidates involve large amounts of text or knowledge, repetitive drafting or searching, and a measurable outcome such as handling time or response quality. Our discovery workshop scores each idea on value, feasibility, data availability and risk so you can choose with confidence.
Can generative AI be deployed securely with our confidential data?
Yes. We deploy within your own cloud tenant using enterprise services such as Azure OpenAI or Amazon Bedrock, where your prompts and data are not used to train public models. We add role-based access, encryption, logging and data loss prevention controls approved by your security team.
How do you prevent hallucinations?
We ground answers in your own content using retrieval, require citations, restrict the model to approved sources and instruct it to say when it does not know. Every release is tested against an evaluation set of real questions, and low-confidence answers are routed to a person.
How much does a generative AI solution cost to run?
Running cost depends mainly on the model, the length of prompts and documents, and usage volume. During prototyping we measure the cost per transaction and optimize it through model selection, caching and routing, so you know the operating cost before launch.
How long does it take to go from idea to production?
A focused use case typically takes two to four weeks for discovery and a prototype, and another six to ten weeks to harden, integrate and launch. Timelines depend on integration complexity and approval processes, which we plan for from the start.