Enterprise AI solutions
Your one-stop AI solution partner for
business transformation
See what is possible when AI is built into the systems that already run your business — then deployed in your cloud, with the source and IP transferred to you.
50+AI models shipped
100+Projects delivered
6–12 wksTypical production delivery
100%Code & IP ownership
For startups
From AI idea to first customers
Built for founders proving an AI service in the product — a pilot, an MVP, a new line of revenue — without standing up an in-house research bench.
We turn your AI idea into a shippable first release
We keep the burn low while you prove real AI traction
We unblock data, evals, and go-live so you can launch
We put AI inside the product your users already open
Service portfolio
Custom AI engineering for your business
You need specialized engineering to get past the prototype. This portfolio covers the work we actually ship — retrieval, models, vision, language, automation, and the product around them.
Knowledge retrieval
Generative AI & RAG
Assistants that answer from your documents, tickets, and product data — with citations and permissions, not an unbounded public chatbot.
Knowledge retrieval
Generative AI & RAG
- Internal document and data search
- Verified source citations
- Role-based access on every answer
- GenAI assistants for support, legal, and ops
- Autonomous agents with tool calling
Domain models
Custom LLM Development
Fine-tune, distill, or host a model when a generic API is too costly, too generic, or cannot leave your network.
Domain models
Custom LLM Development
- Private hosting in your cloud
- Fine-tuning on your examples
- Cost and latency budgets you can operate
- Native GPT, Claude, Llama, and Gemini
- Custom AI APIs for product teams
See and measure
Computer Vision
Detect, classify, and measure from images and video — quality inspection, document capture, and on-device camera pipelines.
See and measure
Computer Vision
- Cloud and on-device inference
- Document and defect detection
- OCR and quality inspection
- Classification and segmentation
- Feedback loops for model quality
Language interfaces
NLP & Chatbots
Intent, extraction, multilingual support, and assistants that call your APIs — with conversation design people actually finish.
Language interfaces
NLP & Chatbots
- Tool-calling into CRM and tickets
- Human handoff on hard cases
- Voice, speech-to-text, and IVR
- Multilingual intent and extraction
- Evaluation sets, not prompt folklore
Workflow integration
AI Automation
Agents and workflows that classify, extract, route, and act in CRM, ERP, and internal tools — with approval where risk is high.
Workflow integration
AI Automation
- Document and data extraction
- CRM and ERP syncing
- Human-in-the-loop on irreversible steps
- Smart bots for finance, HR, and ops
- Anomaly detection in logs and workflows
Custom software
AI Applications
Full products with AI inside the workflow: web and mobile clients, admin tools, and APIs — not a notebook sitting next to the business.
Custom software
AI Applications
- Tailored workflow interfaces
- Streaming responses in production UIs
- Integration with the systems you already run
- Recommendation and ranking models
- MLOps: versioning, drift, and retraining
Success stories
AI systems we have shipped
Live Zupitrix products — vision, LLM tutors, and intelligent marketplaces — not generic stock studies.
Computer vision · Product
Bud Proof AI
Camera intelligence for harvest analysis — a customer-facing product with React, Python, and PostgreSQL, not a lab notebook.
- Vision
- On-device + cloud
- Live
- Shipped product
- Python
- Inference stack
LLM · Language learning
Fluent Fox
Conversational practice with virtual AI tutors — short mobile sessions, not a desktop chatbot wrapped in a WebView.
- LLM
- Tutor conversations
- Mobile
- Practice loops
- Live
- Learning product
Education · Retrieval + matching
AI Learning Platform
Students find experts, book live sessions, and learn on their schedule — Next.js, Python, and AWS behind the marketplace.
- RAG
- Knowledge + matching
- AWS
- Production cloud
- Web
- Full-stack app
Marketplace · Intelligent matching
My Guru
Expert sessions and paid live events where recommendations and scheduling have to feel instant on a phone.
- Match
- Expert routing
- Live
- Paid sessions
- Mobile
- Native-class client
Development process
From the first call to code handover
A structured path from requirements to weekly working releases and complete ownership transfer.
Stage 01
Discuss requirements
A short call on the decision you need, existing systems, data, and whether custom AI is the right spend. Mutual NDA first.
- Requirements review
- Technical approach
Stage 02
Define the solution
Architecture, scope, timeline, and cost in a written proposal — including evals, cloud, and what you will own at handover.
- Architecture blueprint
- Scope and timeline
Stage 03
Develop the solution
Engineers build and test with regular working releases on shared staging. You see retrieval quality, vision accuracy, or agent tool use on real examples.
- Weekly working releases
- Testing and verification
Stage 04
Deploy and handover
We deploy into your cloud, transfer repositories, documentation, and IP, and leave monitoring so the system does not silently drift.
- Your cloud
- 100% source and IP
Strategic technology partnerships
Built on platforms you already trust
We deploy high-performance AI on AWS, Azure, and Google Cloud — with the data platforms and GPU stack the work actually needs.
AWS
Azure
Google Cloud
Databricks
NVIDIA
Salesforce
Why Zupitrix
The Zupitrix philosophy
Senior engineers. Honest feasibility. Your repositories. If custom AI is not the right technical decision two years from now, we will say so now.
PILLAR 01
Business-first strategy
We start with the decision and the metric — hours saved, defect rate, conversion — then pick the model. Technology follows the ROI, not the other way around.
PILLAR 02
Pragmatic, scalable implementation
Cloud-native serving on AWS, GCP, or Azure, with MLOps so the system stays an asset: versioned, monitored, and cheap enough to run after launch.
PILLAR 03
Responsible AI and governance
Privacy, audit logs, bias checks, and human review on high-risk actions. Enterprise adoption needs trust, not a prototype that cannot be explained.
Technology
Technologies we work with
Models, cloud, frameworks, and data stores selected for the project — not because they are trending this quarter.
Model-agnostic architecture. We choose the model based on your requirements.
- OpenAIGPT models
- AnthropicClaude models
- GoogleGemini models
- MetaLlama models
- Azure AIMicrosoft
Technologies we use: PyTorch, TensorFlow, Hugging Face, scikit-learn, Keras, ONNX, LangChain, OpenCV, OpenAI, Anthropic, AWS Bedrock, SageMaker, Vertex AI, Azure OpenAI, Databricks, Google Gemini, PostgreSQL, Pinecone, Weaviate, Redis, Elasticsearch, MongoDB, BigQuery, Snowflake, Python, FastAPI, Docker, Kubernetes, MLflow, Weights & Biases, NVIDIA, GitHub Actions.
PyTorch
TensorFlow
Hugging Face
scikit-learn
Keras
ONNX
LangChain
OpenCV
FAQ
Questions about custom AI development
Straight answers on IP, cost, timelines, cloud, and what happens if custom AI is not the right fit.
Your AI advantage starts with the right partner
Let Zupitrix be your
strategic competitive edge
We will discuss requirements, technical approach, timeline, and cost — and whether custom AI is the right investment.
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