AI development services that ship to production
Zodinet delivers end-to-end ai development services that solve core enterprise challenges. From autonomous AI agents and intelligent customer copilots to custom machine learning models and semantic data analytics, we bridge the gap between experimental generative AI models and stable, high-throughput production software.
Enterprise Data Privacy
Full IP Ownership
AI Solutions We Engineer
As a dedicated ai software development company, we construct specialized capabilities tailored to quantifiable business outcomes.
Autonomous AI Agents & Copilots
As an ai agent development company, we engineer multi-agent systems capable of executing multi-step goals across external APIs, databases, and software tools with human-in-the-loop controls.
Multi-step workflows Tool calling
Enterprise AI Chatbots
As an ai chatbot development company, we build domain-specific virtual assistants that communicate naturally while adhering strictly to your company knowledge base using verified RAG pipelines.
RAG Architecture Zero-hallucination
Custom Machine Learning Models
As an experienced machine learning development company, we develop, train, and deploy supervised and unsupervised models for churn prediction, demand forecasting, and anomaly detection.
Predictive Models Time-series forecasting
Data Analytics Services & Dashboards
As part of our data analytics services, we implement headless analytical layers using technologies like Cube.js, allowing high-performance querying over large transactional databases.
Cube.js Real-time metrics
AI Injected into Existing Products
We inspect your existing monolithic or microservice architecture and inject targeted custom ai solutions via secure REST or GraphQL endpoints with zero legacy refactoring required.
REST/GraphQL API Modular integration
Why Choose Zodinet for AI Development
Four concrete engineering advantages that de-risk your artificial intelligence investments.
1. Hands-on AI Expertise
We deliver real software systems in production, working daily with embedding models, fine-tuned open-source LLMs (Llama 3, Mistral), vector databases, and evaluation frameworks.
2. Proven Product Thinking
Zodinet engineers, operates, and scales its own SaaS product, Bitebolt restaurant management software. We optimize for latency, unit token costs, and user clarity.
3. Cost-Effective Vietnam Rates
By basing our engineering operations in Vietnam, we provide senior software engineers and ML practitioners at competitive rates, reducing your capital outlay by 40% to 60%.
4. Long-Term Support & Tuning
Deploying a model is only day one. Zodinet provides continuous monitoring, automated regression test suites, and prompt engineering refinements on an ongoing basis.
Real-World Production AI Projects
Case studies showcasing our engineering execution.
Bitebolt Intelligent POS & Inventory
Integrating data analytics and automated inventory auditing, Bitebolt processes transaction streams to help restaurant operators optimize ingredient ordering and reduce waste.
Enterprise Document Knowledge Agent [ADD REAL AI PROJECT]
Custom enterprise knowledge agent indexing multi-format corporate documents with role-based access control, source citation, and sub-second query latency.
Status: Production Evaluation
How an AI Project Runs: From Data Check to Production
A structured 4-phase delivery process that eliminates scope uncertainty.
Discovery & Data Check
Data quality audit, API accessibility review, accuracy benchmark targets, and latency threshold definitions.
Proof-of-Concept Sprint
2 to 4-week sprint building a functional prototype with your real data to validate token economics and output accuracy.
Production Hardening
Hardening into resilient microservices with task queues (BullMQ/Celery), vector clustering, and strict safety guardrails.
Monitoring & Optimization
Continuous Langfuse observability, prompt versioning, drift detection, and human-in-the-loop review queues.
Engagement Models for AI Initiatives
| Model | Best Suited For | Deliverables | Timeline |
|---|---|---|---|
| Proof-of-Concept Sprint | Validating feasibility of new AI concepts | Data audit, model selection, working prototype, token cost model | 2 to 4 weeks |
| Fixed Scope Project | Well-defined standalone modules & tools | Architecture spec, end-to-end delivery, automated QA, launch warranty | 6 to 12 weeks |
| Dedicated AI Team | Ongoing product roadmap & agent expansion | Full-time ML engineers, backend devs, and prompt engineers | Ongoing (6+ months) |
Production Technology Stack [CONFIRM STACK]
Languages & Frameworks
TypeScript
Go
LangChain
LangGraph
LlamaIndex
LLMs & Serving
Anthropic Claude
Google Gemini
Llama 3
vLLM
Ollama
Vector & Analytics
pgvector (Postgres)
Cube.js
Redis
ClickHouse
Cloud & Tracing
Google Cloud Vertex
Docker
Kubernetes
Langfuse
Frequently Asked Questions
How much does an AI project cost?
Project costs depend on data readiness, architecture complexity, and infrastructure requirements. A focused Proof of Concept (PoC) sprint typically ranges from $5,000 to $15,000 [CONFIRM PRICING], while full production multi-agent systems or custom analytical platforms vary based on integration scope. We provide transparent, itemized estimates following an initial technical audit.
How long does a proof of concept take?
A standard Zodinet PoC sprint takes between 2 and 4 weeks. By the end of this sprint, your team receives a working prototype running on your test data, an accuracy benchmark report, and an accurate projection of production infrastructure and API token costs.
Who owns the code and the model?
You retain 100% intellectual property ownership. All custom source code, fine-tuning scripts, pipeline definitions, and vectorized data belong exclusively to your organization upon milestone completion.
How do you protect our data?
We adhere to strict data security protocols. For proprietary enterprise data, we design architectures that utilize zero-data-retention APIs or deploy self-hosted open-source models inside your own private VPC (Virtual Private Cloud). Your private corporate data is never used to train public third-party foundational models.
Can you add AI to our existing system?
Yes. Over half of our AI engagements involve augmenting existing web, mobile, or ERP systems. We expose clean, decoupled REST or GraphQL endpoints that your current frontend or backend can query without requiring large-scale legacy refactoring.
Ready to Build Production-Grade AI?
Connect with an experienced software architect at Zodinet to discuss your data architecture, model selection, and business goals.
