01
Data Pipelines
Build reliable pipelines that move information between applications, databases, APIs, storage systems, analytics tools, and downstream services.
Service 08
Pipelines
Databases
Transformations
Search
AI-Ready Data
Data Engineering
Turn data
into usable
infrastructure.
Nanexi engineers data systems that collect, transform, store, validate, search, and serve information for software applications, analytics, automation, and artificial intelligence.
Build a data systemData infrastructure
Data becomes
valuable when
systems can use it.
Information often exists across applications, databases, spreadsheets, documents, APIs, storage systems, and external platforms with different formats and quality levels.
Data engineering creates the structure between those sources and the products that depend on them — making information easier to move, validate, retrieve, transform, analyze, and reuse.
Capabilities
From source
to system-ready
information.
Build pipelines, databases, retrieval systems, and data foundations around the applications and intelligence that actually consume the information.
02
Data Ingestion
Collect structured and unstructured data from APIs, files, applications, operational systems, databases, events, and external sources.
03
Data Transformation
Clean, normalize, validate, enrich, restructure, aggregate, and prepare raw data for applications, analytics, automation, and AI workflows.
04
Database Engineering
Design schemas, relationships, access patterns, indexing, storage models, constraints, queries, and persistence strategies around real application workloads.
05
Search & Retrieval
Build search infrastructure for structured data, documents, metadata, full-text search, semantic retrieval, and knowledge-intensive applications.
06
Data Platform Foundations
Engineer the storage, processing, pipelines, validation, access, observability, and infrastructure required by modern software and AI products.
Data principle
Don't move
data without
knowing why.
The architecture should begin with how information is used: which application needs it, how fresh it must be, what shape it should have, who can access it, and what happens when the source is incomplete or wrong.
What we engineer
Data systems
for products,
operations and AI.
Different consumers require different data models, freshness, access patterns, quality controls, and storage strategies.
Application Data
Operational databases and data models supporting users, products, workflows, permissions, transactions, business state, and software behavior.
Document Data
Pipelines and storage for documents, metadata, extracted information, chunks, embeddings, search indexes, and AI retrieval workflows.
Analytics Data
Structured data prepared for reporting, dashboards, analysis, operational visibility, business intelligence, and product measurement.
Integration Data
Information moving between internal software, external APIs, SaaS products, cloud services, automation platforms, and business systems.
AI Knowledge Data
Knowledge sources prepared for retrieval, grounding, search, context assembly, evaluation, and AI-powered application workflows.
Event & Workflow Data
Operational events, task state, audit information, system actions, processing status, and workflow history used across distributed applications.
Data architecture
Source.
Ingest.
Transform.
Store.
Serve.
A production data system is a chain. Weakness in any layer affects the application, analytics, automation, or AI system consuming the final result.
01
Sources
Applications, APIs, documents, databases, files, events, external systems, and operational data producers.
02
Ingestion
Batch processing, API ingestion, event flows, file processing, connectors, validation, retries, and data movement.
03
Transform
Cleaning, normalization, enrichment, aggregation, schema mapping, validation, and business transformations.
04
Store
Operational databases, analytical storage, object storage, search indexes, caches, and purpose-specific persistence.
05
Serve
APIs, search, analytics, applications, AI retrieval, internal systems, automation, and downstream consumers.
Ingest
Bring data
into the system.
Data can arrive through APIs, files, databases, application events, user uploads, scheduled processing, external platforms, and operational systems.
Transform
Make the
data usable.
Transformation converts source-specific information into structures that applications, search systems, analytics, workflows, and AI products can reliably consume.
Data quality
Bad data
becomes bad
system behavior.
Applications and AI systems can only work with the information they receive. Production pipelines should make missing, malformed, duplicated, stale, or inconsistent data visible before those problems silently propagate downstream.
Schema validation
Required-field checks
Duplicate handling
Type validation
Missing-data visibility
Transformation checks
Pipeline failure tracking
Source-level traceability
Retrieval infrastructure
Storage is
not the same
as retrieval.
Products need ways to find the right information quickly. Search architecture can combine structured filtering, metadata, full-text indexing, semantic retrieval, ranking, and application-specific context.
Filter
Use structured fields, metadata, permissions, categories, dates, states, and business constraints to narrow the search space.
Search
Retrieve information using keywords, full-text search, indexing, ranking, structured queries, or domain-specific search logic.
Retrieve
Use semantic retrieval and context selection when applications need conceptually relevant information rather than exact keyword matches.
AI-ready data
AI needs
knowledge
architecture.
Reliable AI applications need more than raw documents or database access. Knowledge must be prepared so retrieval can identify the right context, enforce boundaries, and preserve evidence required by the application.
Document ingestion
Metadata design
Chunking strategy
Search indexing
Embedding pipelines
Permission-aware retrieval
Source references
Knowledge freshness
Database engineering
Model data
around how the
product works.
Database design should reflect the product's entities, relationships, workflow states, permissions, query patterns, consistency requirements, and future extensibility.
01
Model
Define entities, relationships, constraints, states, ownership, and the structure of application information.
02
Query
Design indexes and access patterns around how users, services, workflows, and APIs actually retrieve information.
03
Protect
Enforce permissions, validation, constraints, secure access, and clear ownership around sensitive information.
04
Evolve
Plan schema changes and migrations so the data model can grow with the application without becoming fragile.
Engineering process
From raw source
to reliable
data system.
01
Map
Understand where data originates, how it moves today, who uses it, what quality problems exist, and which systems depend on it.
02
Model
Design schemas, entities, relationships, metadata, access patterns, transformations, retention, and boundaries around the information.
03
Build
Engineer ingestion, pipelines, databases, transformations, storage, APIs, search, monitoring, and supporting infrastructure.
04
Validate
Test completeness, schema expectations, transformations, duplicates, missing information, failures, permissions, and downstream behavior.
05
Operate
Monitor pipeline behavior, investigate quality issues, improve performance, evolve schemas, and adapt the data system as products change.
Data principles
How we think
about data
systems.
01
Design around how data is used
02
Make transformations explicit
03
Validate data at system boundaries
04
Preserve lineage where it matters
05
Separate operational and analytical needs
06
Build for change, not perfect permanence
Related services
Build the
systems that
use the data.
01
Cloud Engineering
Build the infrastructure, deployment, storage, runtime, observability, and production foundations supporting data workloads.
02
AI Development
Turn structured knowledge and retrieval infrastructure into grounded artificial intelligence systems and applications.
03
Custom Software Development
Build the applications, APIs, workflows, and business systems that consume and create operational data.
Data Engineering
Build the
data layer
behind the
intelligence.
Tell us where your information lives, how it moves today, and what products, workflows, analytics, or AI systems need from it. We can design the data architecture around those requirements.