Capabilities

Infomerx delivers cohesive web interfaces and supporting software for datasets, maps, and operational tooling. Each theme below opens with example inquiry shapes—problem-led phrases that show up in early calls and email—then common project examples and typical deliverables.

Production web applications

Maintainable architectures and TypeScript-heavy stacks—SvelteKit surfaces, APIs, validation, and long-lived admin or client-facing products.

Example inquiry shapes

• Our internal admin is held together with duct tape—every small change feels like it could break production.
• The customer app still lives on legacy server pages; we need a migration path, not a rewrite that freezes the business.
• We're lining up a new product and want architecture, auth boundaries, and CI in place before the backlog outruns us.
• We keep saying “we should move to SvelteKit” but nobody has scoped what the first shippable slice actually looks like.

Common project examples

• Greenfield or modernization of a customer-facing product with auth, roles, and audit needs.
• Internal admin consoles that replace spreadsheets without hiding business rules.
• SSR or static prerender decisions driven by SEO, hosting, and operational constraints—not dogma.

Typical deliverables

• Application architecture notes, API contracts, and incremental release milestones.
• Automated checks (types, unit/integration tests, lint) wired into CI so refactors stay safe.
• Handoff-friendly documentation: how to run locally, deploy, and extend without a tribal ritual.

Common starting shape

Two to three weeks: discovery plus a narrow vertical slice (routing, one auth boundary, one integration risk) and a clear go/no-go for the full build.

Data visualization systems

High-density dashboards, accessible charting idioms where they matter, and exploratory views that stay responsive as data grows.

Example inquiry shapes

• Leadership wants a one-glance dashboard, but analysts need drill-downs—we are stuck between “too shallow” and chart soup.
• When we load real production volumes, the charts stutter and the layout stops behaving.
• We need dense tables and charts to stay keyboard- and screen-reader-friendly—not an accessibility bolt-on at the end.

Common project examples

• Programme or policy dashboards where users compare regions, cohorts, or indicators at a glance.
• Exploratory tools for analysts who need filters, drill-downs, and export without “chart soup.”
• Pairing tables and charts with coherent progressive disclosure for expert audiences.

Typical deliverables

• Reusable visualization components and layout primitives tuned for keyboard and screen-reader use.
• Performance budgets for large datasets (chunking, virtualization, sensible defaults).
• Design-to-code alignment so visual density does not collapse under real data.

Common starting shape

One to two weeks: spike on your densest chart or table path—interaction model, performance ceiling, accessibility notes—before committing to a programme-wide build.

Geospatial interfaces

Map-led exploration UX: filters and layers structured for clarity, including public-sector style tooling where map–list coherence matters.

Example inquiry shapes

• The filters update the table but the map does its own thing—people stop trusting what they see.
• Operators live in this map all day; it has to stay fast with heavy layers and honest list–map hand-off.
• Geography is the real mental model for this tool, not a decorative basemap behind the same old forms.

Common project examples

• Map-first explorers with declarative layers, clustering, and hand-off between auto-zoom and manual control.
• Spatial dashboards where geography is the primary mental model—not a decorative basemap.
• Flows that keep list, map, and detail panes aligned under heavy filtering.

Typical deliverables

• Map UI widgets and state machines that keep query parameters and the map in sync.
• Operator-facing notes for data prep, layer contracts, and deployment assumptions.
• Accessibility and performance passes for long sessions on dense layers.

Common starting shape

Two weeks: map–list coherence slice—synced filters, one representative layer stack, and a short operator checklist for data prep and deployment assumptions.

Interactive 3D (where appropriate)

Commercial presentation or technical visualization—not decorative noise layered on weak information design.

Example inquiry shapes

• Sales wants something on the web where 3D actually explains the product—not spinny marketing chrome.
• Buyers only understand our layout or equipment when they can manipulate it in the browser.
• We need a tight loop between inputs and feedback (geometry, spatial context, or analysis)—not open-ended shader play.

Common project examples

• Marketing or sales experiences where 3D supports comprehension (e.g. property, equipment, spatial context).
• Technical playgrounds where geometry and analysis feedback loops must stay interactive.

Typical deliverables

• Scene graphs and asset pipelines sized for web delivery (formats, LOD, loading strategy).
• Clear separation between rendering, simulation/analysis callbacks, and UI state.
• Documentation of trade-offs when mobile GPUs or bandwidth are constrained.

Common starting shape

Short prototype sprint: one hero scene or technical feedback loop at target hardware, with asset path and production trade-offs written down—not open-ended R&D.

AI and automation (practical scope)

Integrations that bridge Python services, ML workflows, and shipped frontends without over-promising magic.

Example inquiry shapes

• We have a model or scoring job in Python; we need contracts, logging, and failure semantics before it touches anything user-facing.
• Nothing the model suggests should ship without an operator saying yes—we need that workflow designed, not just an API.
• Critical batch work still lives in brittle scripts on a laptop; we want monitored runs and clear alerts when something breaks.

Common project examples

• Sidecar services for scoring, enrichment, or batch jobs with explicit contracts to the browser.
• Human-in-the-loop workflows where models assist but operators stay accountable.
• Replacing brittle scripts with monitored jobs and clear failure modes.

Typical deliverables

• Service boundaries, request/response schemas, and observability hooks appropriate to risk.
• Feature flags or staged rollouts when model outputs touch user-facing decisions.
• Plain-language runbooks for operators—not only Jupyter notebooks on a laptop.

Common starting shape

One to two weeks: one model-backed operator flow with explicit failure semantics, logging, and human-in-the-loop guardrails—bounded to a single shippable milestone.

Prefer accuracy over spectacle; scope decisions are deliberate and collaboratively defined. Representative projects are summarized under selected work.