01
Retrieval-Augmented Generation
Build RAG systems that retrieve relevant business knowledge before generating responses, analysis, drafts, or recommendations.
Service 04
RAG
Document Intelligence
Knowledge Systems
Structured Generation
Validation
Generative AI Development
Generate
with context,
not guesses.
Nanexi builds generative AI systems that connect models with relevant knowledge, structured workflows, software, validation, and human review so generation becomes useful inside real products and business processes.
Build with Generative AIGrounded generation
Generation is
more useful when
it knows the source.
General-purpose models can generate impressive output, but business applications often depend on information that exists inside company documents, databases, policies, product material, and approved knowledge.
Nanexi designs generative systems that retrieve relevant context first, generate inside defined boundaries, and expose supporting evidence when the workflow depends on specific information.
Capabilities
From raw
information to
useful output.
Combine retrieval, generation, software, data, and validation into one production system.
02
Document Intelligence
Use generative AI to extract, classify, summarize, compare, analyze, transform, and work with information across complex documents.
03
Knowledge Applications
Turn internal documents and structured information into grounded applications for search, question answering, analysis, and business workflows.
04
Structured Generation
Generate responses, reports, summaries, proposals, classifications, JSON, extracted fields, and other structured outputs inside controlled workflows.
05
Generative Workflows
Combine retrieval, generation, business rules, APIs, validation, and human review into multi-step production workflows.
06
Generative AI Integration
Integrate generative capabilities into existing applications, SaaS products, APIs, internal tools, databases, and cloud systems.
Generative AI principle
No source.
No confident
answer.
When output depends on business-specific information, the system should retrieve supporting context, expose evidence, and surface missing knowledge rather than silently inventing certainty.
What we build
Generative AI
inside real
workflows.
Generation becomes more valuable when it is connected to the information, application state, business rules, and human process around the task.
Grounded Question Answering
Answer questions using approved company knowledge, retrieved evidence, source context, and visible uncertainty instead of relying only on general model knowledge.
Content & Response Drafting
Generate structured drafts using business context, instructions, templates, source material, and workflow-specific requirements.
Document Analysis
Analyze long documents, identify important information, compare sections, extract requirements, summarize context, and produce structured findings.
Knowledge Synthesis
Combine information from multiple approved sources into useful summaries, comparisons, explanations, and structured business output.
Information Extraction
Convert unstructured text and documents into defined fields, entities, categories, requirements, actions, and structured data.
Generative Product Features
Add generation, retrieval, summarization, transformation, drafting, and reasoning features inside existing digital products.
RAG architecture
Retrieve.
Ground.
Generate.
Validate.
Retrieval-augmented generation works best when each layer is treated as part of the production architecture rather than a single model call.
01
Knowledge
Documents, databases, structured information, metadata, search indexes, embeddings, and approved business context.
02
Retrieval
Search, filtering, semantic retrieval, ranking, chunking, context selection, and evidence assembly.
03
Generation
Models, prompting, structured output, context management, synthesis, transformation, reasoning, and generation behavior.
04
Application
Interfaces, APIs, workflows, permissions, business logic, integrations, persistence, and user experience.
05
Validation
Grounding checks, source visibility, evaluation, review states, observability, failure handling, and production controls.
Generate
Create useful
output.
Models can draft, transform, classify, extract, summarize, synthesize, reason, and produce structured information from the context available to them.
Ground
Give the model
the right context.
Retrieval connects generation with business-specific information, evidence, documents, structured data, and source material required by the workflow.
Reliable generation
Generation
needs a
verification layer.
Production generative systems should make it possible to inspect source context, detect unsupported responses, enforce output structure, review important results, and observe behavior over time.
Source visibility
Retrieval quality checks
Structured output validation
Missing-evidence states
Human review
Model and prompt evaluation
Observability
Failure handling
Engineering process
From knowledge
to production
generation.
01
Understand
Define the information problem, users, source material, workflow, expected outputs, constraints, and quality requirements.
02
Ground
Design the knowledge architecture, retrieval strategy, chunking, metadata, search behavior, context assembly, and evidence boundaries.
03
Generate
Engineer prompts, structured outputs, model behavior, transformations, reasoning steps, and application logic around the retrieved context.
04
Validate
Test grounding, source relevance, hallucination risk, output structure, completeness, latency, failure modes, and human review requirements.
05
Operate
Deploy with observability, monitor production behavior, inspect retrieval quality, improve evaluation, and evolve the system over time.
Generative AI principles
How we think
about grounded
generation.
01
Retrieve before generating when knowledge matters
02
Show evidence instead of hiding uncertainty
03
Use structured outputs where software depends on the result
04
Separate source knowledge from model knowledge
05
Evaluate the complete pipeline, not only the prompt
06
Keep human review for consequential output
Related services
Build the
complete system
around generation.
01
AI Development
Design the broader AI architecture, retrieval, reasoning, model strategy, evaluation, and production intelligence layer.
02
AI Application Development
Turn generative capabilities into complete SaaS products, internal systems, and production applications.
03
AI Automation
Use grounded generation inside repeatable workflows connecting systems, APIs, business rules, and human review.
Generative AI Development
Turn knowledge
into useful
generation.
Tell us the information, workflow, documents, users, and output your system needs. We can design the retrieval, generation, application, and validation architecture around it.