Aires de Río / Habitat Prime
- Live
- Client project
Marketing site + lead pipeline. ~3 leads/day at ~US$1 CPL.
Production web apps and data interfaces for teams who care about what's under the hood.
Software engineering for data-rich, interactive systems.
Problems clients hire Infomerx to solve
- Build or modernize a production web app (SvelteKit/TypeScript by default; React on request).
- Turn dense datasets into usable dashboards and maps.
- Rescue or refactor fragile internal tools.
- Prototype technical products involving 3D, ML/Python services, or complex browser UX.
Infomerx builds production-grade web applications, data visualization platforms, technical prototypes, and software systems where clarity, performance, and engineering depth matter.
Currently accepting new engagements; replies usually within two business days.
systems map clarity · performance · delivery
1
Product interface
SvelteKit · TypeScript · UX architecture
2
Data layer
SQL and No-SQL databases · APIs · ingestion · validation
3
Visualization
Charts · maps · 3D · interaction
4
Automation
Python services · ML integration · workflows
Evidence in shipped work
Case studies name delivery context (LLC, subcontract, programme, or in-house), link public references where they exist, and separate client-facing launches from pre-release products—so you can evaluate claims against something concrete.
Featured case study
Aires de Río / Habitat Prime
SvelteKit marketing site with map search, floor plans, gated downloads, and lead admin.
-
~3
leads per day average
during active campaign window
-
~US$1
per lead during campaign
ad spend efficiency at launch
-
400+
daily visitors during peak
campaign traffic to the site
-
Live
production launch
deployed with ops and admin tooling
Clear about delivery
We focus on systems that are useful, maintainable, and built for the long run. No hype, no vaporware — just shipped work and honest context.
Capabilities
Senior engineering without the ceremony.
Production web applications
SvelteKit, TypeScript, backend services, admin systems, dashboards, and client-facing platforms built for maintainability.
Data visualization systems
Accessible, high-density interfaces for charts, maps, filters, metrics, and exploratory analysis.
Geospatial interfaces
Map-driven products, spatial dashboards, public-sector tooling, and location-aware UX.
AI & technical prototypes
Practical integration of Python services, ML workflows, automation, experimental engineering tools, and browser-native domain-heavy UIs (e.g. computational 3D or analysis loops).
How engagements run
-
Discovery
Align on goals, constraints, and evidence of success before committing to a stack or schedule.
-
Architecture
Choose patterns, boundaries, and milestones that stay shippable—no surprise “big bang” reveal.
-
Delivery
Incremental releases with review, automation, and documentation that survives handoff.
-
Hardening
Operational basics—monitoring hooks, runbooks, and support scope—are part of done.
Technical depth without theatrics
Most products sit between a browser surface, a data layer, and services that do the slow or specialized work. The diagram is schematic: your deployment will differ; the discipline does not.
Interface SvelteKit · a11y · maps & charts
Application APIs · validation · auth boundaries
Data PostgreSQL · migrations · RLS where needed
Automation Python · jobs · ML when it earns its place
Case studies show how these layers showed up on real projects. Typical tooling on the stack page.
About Infomerx
The name
Infomerx was coined as a classical-style name for software: information made into useful goods — engineered systems, tools, and digital products.
The company
Infomerx Engineering Solutions LLC is a software engineering company led by senior technical practice. It is suited for organizations that need thoughtful architecture, hands-on implementation, and unusually broad technical range in one compact team.
Common starting shapes
Examples of how a first milestone is often bounded. Detail and deliverables stay on Capabilities.
Vertical slice spike
Two to three weeks: discovery plus a narrow vertical slice and a clear go/no-go for the full build.
Map–list coherence slice
Two weeks: synced filters, one representative layer stack, and a short operator checklist for data prep and deployment.
Model-backed operator flow
One to two weeks: one model-backed flow with failure semantics, logging, and human-in-the-loop guardrails.
Have a complex SvelteKit, data, map, or internal-tool problem?
Send the current state, the desired state, and the first milestone you need. Replies include clarifying questions, a rough sense of fit, and a proposed first milestone.