01
AI Applications
Design and build intelligent applications that combine AI models with software interfaces, APIs, data, workflows, and business logic.
Service 01
Artificial Intelligence
Retrieval
Generative AI
Agents
Evaluation
AI Development
Build AI
that works
in production.
Nanexi designs and engineers artificial intelligence systems that combine models, knowledge, software, workflows, data, evaluation, and infrastructure into complete production applications.
Start an AI projectAI engineering
The model is
only one part
of the system.
Production AI requires more than calling a model API. Useful intelligence depends on context, retrieval, software architecture, user workflows, data quality, integrations, security, and evaluation.
Nanexi engineers those layers together so artificial intelligence becomes part of a reliable product or business workflow instead of remaining an isolated experiment.
Capabilities
Intelligence
engineered across
the stack.
Start with one capability or combine several into a complete AI-powered product, platform, workflow, or internal system.
02
Retrieval Systems
Build retrieval-augmented systems that connect models with business knowledge, documents, structured data, and supporting evidence.
03
Generative AI
Engineer generation workflows for text, analysis, extraction, classification, synthesis, drafting, and knowledge-intensive tasks.
04
AI Agents
Develop controlled agentic systems that can reason through tasks, use tools, interact with APIs, retrieve information, and follow workflow boundaries.
05
AI Evaluation
Define test cases, evaluation criteria, failure states, grounding checks, quality controls, and observability for production AI behavior.
06
AI Integration
Integrate intelligent capabilities into existing products, internal systems, APIs, databases, automation workflows, and cloud environments.
Our approach
Don't build
around the model.
Build around
the work.
Models change quickly. Business problems, workflows, evidence requirements, user needs, and operational constraints define the real system. The architecture should make the intelligence replaceable and the product durable.
What we build
AI for
real business
workflows.
Intelligent systems should solve a defined problem, improve a workflow, support a decision, or complete useful work.
Document Intelligence
Extract, understand, classify, search, summarize, compare, and work with information across business documents.
Knowledge Systems
Turn internal knowledge into searchable, grounded intelligence for teams, customers, operations, and product workflows.
Business Automation
Use AI inside structured workflows to reduce repetitive knowledge work while keeping business rules and human review visible.
Decision Support
Combine structured data, retrieved evidence, and AI reasoning to support analysis without hiding uncertainty or source information.
AI Assistants
Build assistants designed around a specific product, team, knowledge base, workflow, or operational responsibility.
AI SaaS Products
Build complete AI-powered products with interfaces, authentication, data models, billing, infrastructure, observability, and production controls.
System architecture
Five layers
working as
one system.
01
Models
Foundation models, embeddings, inference strategies, prompting, structured output, and model selection.
02
Knowledge
Documents, retrieval, search, embeddings, structured data, evidence, context, and grounding.
03
Software
Interfaces, APIs, workflows, permissions, integrations, orchestration, application logic, and product experience.
04
Data
Storage, pipelines, databases, metadata, analytics, ingestion, transformations, and data quality.
05
Evaluation
Test sets, quality criteria, grounding, monitoring, validation, failure detection, and human review.
Reliable intelligence
Build for
uncertainty,
not around it.
AI systems can fail in ways traditional software does not. Production architecture should account for uncertainty, missing context, incorrect retrieval, unsupported outputs, model changes, and operational failure states.
Grounding and source visibility
Structured evaluation
Human review where appropriate
Failure-state handling
Logging and observability
Security and access control
Engineering process
From problem
to production
intelligence.
01
Understand
Define the problem, users, workflow, available data, business rules, constraints, and expected outcome.
02
Design
Choose the right AI architecture, system boundaries, model strategy, retrieval design, interfaces, integrations, and controls.
03
Build
Engineer the AI behavior together with the software, data, APIs, infrastructure, and user experience around it.
04
Validate
Evaluate quality, grounding, failure modes, security, latency, reliability, observability, and real workflow behavior.
05
Evolve
Improve the system using production evidence, user feedback, new knowledge, changing requirements, and model improvements.
Principles
How we think
about AI
systems.
01
Problem before model
02
Grounding before confidence
03
Software around intelligence
04
Human control where needed
05
Evaluation before production trust
06
Architecture built for change
Related services
Go deeper into
specific AI
capabilities.
01
AI Application Development
Build complete AI-powered applications, products, and SaaS platforms.
02
AI Agent Development
Engineer controlled agents that reason, use tools, retrieve information, and act inside workflows.
03
Generative AI Development
Build retrieval, generation, document intelligence, knowledge, and generative workflows.
AI Development
Build
intelligence
into software.
Tell us the product, workflow, knowledge problem, or AI capability you want to build. We can help shape the architecture and engineering path from there.