EV Charging Platform Engineering with Route Optimization for a Florida Operator

Industry

Automotive & Mobility

Geography

Florida, United States

Cooperation Period

6.5 months

Overview

An EV charging network operator serving Florida brought in Clixlogix to review why AWS spend was climbing faster than usage. That review produced an architecture mandate. The mandate produced working changes to the charging data foundation. Those changes exposed the next constraint in the mobile experience. That work created the conditions for range aware route planning. Structured platform data then made a B2B fleet API business viable. Over 6.5 months of engineering work and within 6 months of public launch, the platform crossed 170,000 downloads, cut failed charging attempts by 30%, lifted partner station utilization by 18%, and secured 3 fleet contracts.

A venture capital firm referred the client to Clixlogix. The VC firm had watched another portfolio company use Clixlogix during periods of engineering demand overload and had seen the delivery cadence hold across multiple quarters. The referral came with that track record already validated.

The first mandate was narrow. The client wanted Clixlogix to examine rising AWS spend and identify where infrastructure cost had moved out of line with usage. Clixlogix traced cloud spend through the production architecture, correlating line item costs against application traffic, charger telemetry, provider integrations, routing calls, and mobile behavior. That investigation exposed a larger engineering finding beneath the cost signal. Recurring infrastructure cost could be traced to application behavior, data movement, service boundaries, and workload design.

Client engineering leadership and the Clixlogix technical architect took a joint decision to address the underlying causes inside the architecture itself. The engagement expanded from a targeted cost review into a program of architectural work, where each body of work produced the evidence that scoped the next engineering decision.

A Scrum Master coordinated the work through short delivery cycles across both organizations. Agentic engineering workflows built on Anthropic Claude via Claude Code, alongside Cursor for IDE integrated work, supported architecture analysis, integration comparison, code scaffolding, automated test generation, and technical documentation. Senior engineers retained decision authority over architecture, security, routing rules, payment flows, charging integrations, and production releases.

Within 6 months of public launch, the platform crossed 170,000 downloads, reduced failed charging attempts by 30%, increased partner station utilization by 18%, and helped the client secure 3 fleet contracts for API access.

About the Client

The client operates a public EV charging network serving drivers across Florida, including major metropolitan areas and long distance travel corridors along the Atlantic and Gulf coasts.

The business connects charger hosts and site owners with EV drivers, fleet operators, and roaming network partners. As charging activity increased, the company needed greater control over infrastructure economics, charger data quality, the driver experience, and future commercial services.

Charging data added another source of complexity. The client’s own network and external charging providers exposed station information through different protocols, payload structures, refresh behavior, and reliability characteristics. The application had to reconcile those sources before drivers could trust a single availability view. A charger marked available could become occupied or unavailable during the journey. A provider could stop responding while the previous state remained cached. Repeated status queries increased infrastructure activity while still leaving the driver exposed to stale information.

The Challenge

The visible problem was rising cloud spend. The underlying causes crossed several parts of the application architecture. AWS resources had grown broadly across compute, data, streaming, logging, and traffic services. Infrastructure demand had grown unevenly as mobile usage and charger telemetry increased.

Routing created another engineering dependency. A useful EV route needed charger availability, battery state, vehicle consumption characteristics, charger power, pricing, historical reliability, and detour distance from the direct route. Each routing decision also carried compute and external API cost.

The engineering question therefore became broader than AWS spend. The team needed to reduce unnecessary infrastructure activity while improving the quality of the information flowing through the platform.

Fig 1 – The cloud review mapped traffic, charger telemetry, provider requests, routing calls, database activity, and logging volume back to the services driving recurring AWS spend.

The Solution

The engagement expanded in stages. Each body of work produced the evidence that scoped the next engineering decision.

The Cloud Review

Clixlogix built a workload map across the production environment. The team correlated infrastructure spend against seven operational signals.

Application traffic
Charger events
Database activity
Provider request volume
Routing calls to external mapping APIs
Log ingestion volume
Peak usage periods derived from mobile telemetry

This gave the client engineering leadership a service by service view of where recurring cloud cost originated. The findings clustered into six areas.

Database sizing and query behavior. Production database capacity had been chosen during a peak season and never revisited. The query workload was dominated by a small number of expensive reads that a better index and cache strategy would absorb.

Log retention discipline. Several high volume log groups retained diagnostic traffic well beyond its useful operating window, with no lifecycle policy distinguishing high value audit data from short useful debug traffic.

Event stream capacity model. Event ingestion capacity was sized around peak season load year round, oversized for the majority of the year and inefficient during shoulder months.

Outbound traffic path and AWS service connectivity. Some traffic to AWS managed services was unnecessarily traversing paid egress paths when a private endpoint path was available. External provider polling continued through the internet as required, though request behavior was later tuned to reduce redundant calls through batching and coalescing where the provider API supported it.

Cache underuse. A managed cache cluster existed but was carrying only session data, while the highest frequency database reads (availability lookups) were not cached at all.

External API cost. Route requests repeatedly recomputed internal inputs and invoked external mapping services even when portions of the underlying request context had not materially changed.

The cloud assignment produced a clear architecture finding. The platform needed three things before infrastructure behavior could become consistently efficient.

A common charging data model
Differentiated caching
A rebuilt event flow

Consulting Insight

A cloud bill reports engineering decisions made months earlier. Each of the six findings traced back to a choice about sizing, retention, caching, or request behavior. Reading the number as an architecture signal is what turned a cost assignment into a platform program.

Charging Data Architecture

The team moved from individual workload fixes into the way charger data entered and moved through the platform. The client’s own network used OCPP 1.6 J, a version of the Open Charge Point Protocol, around its charger management systems. External charging data arrived through roaming interfaces built on OCPI, the Open Charge Point Interface, alongside provider APIs and aggregation relationships.

Integration boundaries per partner network. Clixlogix created dedicated integration services for each external partner network. External networks reached the platform through a combination of bilateral roaming interfaces, provider APIs, and hub aggregation relationships, with the choice made per partner. Direct integration gave the client control over the pricing surface, reservation flow, and status freshness for stations in its primary Florida coverage areas. Hub federation gave broader reach at lower integration burden for partners where corridor coverage was the priority.

Each integration service ran as an isolated worker with its own circuit breaker, retry budget, and dead letter queue. This contained provider failures within their integration boundaries and reduced their ability to create pressure across downstream services.

Fig 2 – External charging providers were isolated behind integration workers with retry budgets, circuit breakers, and dead letter queues before data entered the normalized availability service.

Canonical state model. OCPI defines charging status at the EVSE level, meaning the individual charging point that serves one vehicle at a time, while connector records carry the physical and electrical attributes required to determine compatibility. Providers vary in how they populate status in practice. The team compared observed status values across every partner plus the client’s own OCPP status set. That comparison produced a set of provider deviation matrices which the architects used as input when defining a canonical EVSE status vocabulary for the platform. The canonical model itself, which downstream services depend on, was engineer authored. Each mapping decision was documented so future integrations could onboard against the same reference.

Differentiated caching. The team introduced typed cache keys with cache lifetimes tuned to the freshness characteristics of each data type. Live availability, dynamic pricing, station and EVSE metadata, and historical reliability scores each carried their own cache policy. Cache invalidation was driven by the normalized ingestion service, which treats push events from providers that support them and scheduled polling from providers that do not as inputs to the same normalized event path.

Failure handling that reflects reality. When a provider stopped responding within its expected refresh envelope, the service moved affected EVSEs into an explicit unknown state and stopped serving a stale response as current. This addressed a recurring source of “dead on arrival” driver reports in the existing tooling.

Historical status changes created another useful data set. The team could evaluate how consistently a specific charger remained available over time and use that history when estimating whether a station was a sensible stop for a driver who had not arrived yet.

Consulting Insight

Serving a stale availability response as current is the failure a driver actually feels. Moving affected chargers into an explicit unknown state costs a small amount of apparent coverage and protects the thing the product is judged on, which is whether the charger works when the driver arrives.

Native Mobile Engineering

The normalized data service changed what the driver application could consume. Availability, provider freshness, pricing, and reliability could now reach the client through consistent contracts. At the same time, mobile telemetry showed that the existing experience was still generating repeated requests, handling degraded connectivity poorly, and struggling with marker density on statewide zoom. That made the mobile client the next constraint the joint engineering team needed to address.

Map refreshes, repeated station requests, local cache age, route recalculation, marker density, and weak connectivity all influenced both application performance and infrastructure workload.

Fig 3 – Native iOS and Android clients consumed a shared API contract while station tiles, server side clustering, offline cache sync, and live availability services kept the statewide charging map responsive under changing network conditions.

Native applications with a shared API contract. The existing mobile client was replaced with native iOS and Android applications, rebuilt against the new backend contracts. Several requirements shaped the decision together. High marker density on a statewide map required responsive rendering under load. In vehicle surfaces (CarPlay, Android Auto) needed direct access to operating system specific APIs. Background operations differed per platform and benefitted from native primitives. The team selected native iOS and native Android over a shared UI codebase. Both applications consumed the same backend contract with generated client code, keeping behavior aligned across platforms.

Marker clustering that scales to statewide zoom. Rendering hundreds of station markers naively would degrade frame rates on older devices. The team used server side clustering keyed on map zoom and viewport. At statewide zoom, chargers cluster into cells with summary counts and status roll ups. Zooming in reveals individual markers only when the driver is close enough for the marker to be actionable. Cluster summaries are computed backend side and cached, so the client does not recompute clustering on every pan.

Offline cache and reconciliation. Both clients maintain a local cache of station data covering a radius around the driver’s current location. The cache is populated in tiles requested from the backend and reconciled with the live service when connectivity is strong. When the driver moves outside the covered radius, new tiles are fetched and older tiles are evicted.

In vehicle experience. The iOS application supports Apple’s EV charging CarPlay experience using the permitted point of interest surfaces. The Android application supports charging through the Android for Cars POI application model. Voice interactions inside the phone application cover a focused set of charging related actions through the phone’s native speech recognition surface. Constrained intent vocabularies keep recognition accuracy high in noisy conditions. Background location tracking uses each platform’s low power update mechanisms to protect battery during long drives.

Range Aware Route Planning

The new mobile applications had the delivery surface for richer routing, and the normalized data service had the availability, reliability, pricing, and freshness inputs that routing needed to make decisions worth showing a driver. Together they made a real range aware routing service viable.

Earlier routing logic could identify roads and candidate charging locations. The new platform makes a much richer decision.

Vehicle consumption model. The routing service estimates battery draw across each route segment using a per vehicle consumption model. The model factors in driving speed, a weather informed adjustment for HVAC demand, elevation grade where route topology data supports it with a partial recovery estimate on descents, and a standard payload baseline. The output is a state of charge delta against the driver’s battery capacity.

Reachability filter. Before evaluating candidate charging stops, the router computes a reachability boundary from the driver’s current location bounded by state of charge minus a safety reserve. Chargers outside the boundary are dropped from consideration immediately, so scoring calculations run only on stations the vehicle could actually reach.

Scored candidate selection. For each remaining candidate, the router computes a composite score across five inputs.

Estimated charging time at the candidate stop
Detour distance from the direct route
Historical reliability of the specific charger
Current cost per kWh
Predicted congestion at estimated arrival time

Estimated charging time is derived from remaining state of charge, charger power output, and a simplified per vehicle acceptance profile suitable for route planning.

Drivers can adjust weights through preferences (fastest route, cheapest charging, minimum stops). The search returns an ordered set of route options with the reasoning surfaced in the UI, so a driver sees why a particular stop was recommended over a nominally closer alternative.

Fig 4 – The routing service combined battery state, charger availability, reliability, pricing, and speed checks before recommending a charging stop, alternate station, or status refresh.

Corridor caching for internal scoring artifacts. For popular corridors, the router caches its own scoring artifacts (candidate charger sets, reachability boundaries, per vehicle consumption calculations, and corridor geometry derived from OSRM route data) keyed by rounded coordinates plus vehicle model and preference profile. Turn by turn route rendering continues to go through Google Maps Platform under Google’s terms. Internal calculations that OSRM can perform (route matrix, geographic filtering, and internal route metadata generation) run against OSRM, the Open Source Routing Machine, to reduce quota consumption from external APIs where the calls do not need commercial traffic awareness or presentation quality.

Edge case coverage. The routing engine is tested against a defined set of production relevant failure conditions. Closed stations mid route. Provider outages announced mid trip. Battery reaching a critical threshold between chargers. Reserved charger going offline before arrival. Weather API failure defaulting to conservative assumptions. Each condition is scripted as an integration test that runs in CI against controlled provider and route fixtures, so regressions surface immediately.

The routing service also generates structured events about planned stops, selected alternatives, route recalculations, and actual driver behavior. That instrumentation became useful beyond the consumer application.

Consulting Insight

The reachability filter runs before scoring for a reason. Computing a composite score across five inputs for every charger in Florida would be expensive, and most of those results would describe stations the vehicle could never reach. Bounding the candidate set first keeps the costly calculation on stations a driver could actually choose.

B2B Services and Fleet APIs

By this point the platform had something the original cloud engagement did not have. Charging events, availability history, routing decisions, mobile actions, provider behavior, and station utilization were represented through consistent data structures with defined schemas.

Partner web console. A web console gives partner users access to operational and utilization views, with role based access differentiating station owners, fleet operators, and internal client staff.

Fleet API surface. Fleet operators receive REST API access to three capability areas.

Station lookup with filter parameters (connector type, charging speed, location, availability)
Session status where the underlying partner network exposes it through its roaming interface
Reservations where the underlying partner’s roaming interface supports them

Where session control or reservations are not available from a specific partner, the API surface degrades gracefully to station lookup and status polling for that partner’s stations.

Webhooks push three event streams to a fleet operator’s registered endpoints within a defined area of interest.

Session state changes
Reservation status transitions
Station availability updates

Authentication uses OAuth 2.0 with rotating client secrets and scoped access tokens.

Commercial tiers. The client’s leadership defined three commercial tiers around rate limits and uptime commitments to match the shape of fleet contracts already in discussion. Tiered pricing kept commercial conversations focused on scope selection.

Station owner analytics. Station owners receive embedded dashboards covering utilization by station and time of day, revenue by session, top user segments, and comparison against anonymized network benchmarks. The dashboards run from the same event pipeline that supports the driver application.

The client secured 3 fleet API contracts within the first 6 months of launch, all sourced through introductions and inbound interest. Each contract used a different tier.

Engineering at Pace

The engagement did not operate through a fixed sequence of isolated project stages. The scope expanded as working software, production telemetry, architecture findings, and client priorities exposed the next meaningful engineering constraint.

Joint architecture leadership. Client engineering leadership worked directly with the Clixlogix technical architect throughout the engagement. Architecture reviews covered infrastructure economics, service boundaries, provider behavior, mobile requirements, routing decisions, security, and production operations. Decisions were captured in an Architecture Decision Record repository so the reasoning behind each choice remained visible to future engineers.

Joint engineering backlog. Delivery ran on a shared backlog across both organizations. Scrum ceremonies aligned to that backlog with a daily overlap window across both teams. The point of the operating model was less the ceremony structure and more the discipline of letting engineering evidence set priority.

Evidence set the priority. Findings from provider telemetry, database behavior, mobile request behavior, availability history, and the live cloud cost dashboard continuously moved items into and out of the immediate backlog. When a workstream produced a finding that changed the shape of the next work, the finding was captured in an Architecture Decision Record and the backlog was resequenced against the new evidence.

Agentic Engineering Workflows

Agentic engineering workflows were used across the engagement to accelerate work that was repetitive, structurally similar across many inputs, or well specified but time intensive to execute by hand. The operating principle was consistent. Agents drafted, engineers reviewed, and no agent assisted change reached production without review and approval from a senior engineer with relevant domain context.

Runners used. Anthropic Claude via Claude Code was the primary agentic runner, invoked through the terminal for scoped tasks with defined inputs and outputs. Cursor with Claude as the underlying model handled IDE integrated work where a developer wanted inline suggestions and multi file edits with immediate visual review. GitHub Copilot handled autocomplete for standard boilerplate.

Architecture analysis. During the cloud review, engineers supplied Claude Code with scoped Cost Explorer exports, sanitized database performance snapshots, and repository configuration extracts. Agents produced structured summaries of resource utilization against cost per service, flagged log groups with excessive retention, identified expensive query behavior, and surfaced candidate access policy tightening. Security decisions and the actual policy changes remained with senior engineers, who reviewed each candidate against production knowledge, corrected misclassifications the agent had inferred without full context, and defined the actual adjustments.

Provider integration analysis. For each partner integration, agents compared live payload samples against the OCPI specification. They produced per provider deviation matrices showing which fields were populated, how status enums were used in practice, and where endpoints deviated from the spec. The architects used those matrices as input when defining the canonical status vocabulary. The vocabulary itself, which downstream services depend on, was engineer authored.

Code scaffolding. Provider adapters followed a consistent structure across authentication, endpoint client, response validation, status mapping, error taxonomy, retry, circuit breaker, and event publishing. Agents generated scaffolds for each new adapter given the base class and provider deviation matrix. Engineers implemented the provider specific logic in marked extension points and wrote the tests.

Automated test generation. Routing engine edge case scenarios were described in a shared scenario document. Agents converted each scenario into a candidate integration test structured against the existing test framework and controlled fixtures. Engineers reviewed each generated test for correctness, expanded fixture data where necessary, and added scenarios the agent had missed.

Production diagnostics. When production incidents occurred, engineers used Claude Code to correlate logs, traces, database performance snapshots, and stream metrics across the incident window. Diagnostic material was passed to the agent through sanitized incident bundles, with user identifiers, vehicle context, location detail, and provider credentials redacted at the collection stage. The agent produced a chronological narrative of what happened across services with specific timestamps called out.

Technical documentation. Architecture Decision Records, API changelog entries, provider integration notes, deployment runbooks, and test evidence were drafted from underlying commit diffs and test outputs, reviewed by engineers, and merged alongside the code they described.

Fig 5 – Agentic work moved through Claude Code and Cursor for scoped drafting, then through engineer review, test evidence, PR gates, and production approval before release.

Governance and access controls. Architecture decisions and designated high risk changes went through client CTO review. Senior engineers on both teams owned routine PR approval within that governance structure. Agent use remained subject to those existing review controls.

Protected paths were unavailable for agent writes. Payment flows, authentication and authorization code, charging session state machines, and database schema changes could only be authored by engineers directly. Where an agent suggestion touched a protected area, an engineer could inspect the suggestion and manually implement the relevant change under normal review, without the agent writing to the protected path.

Secrets were excluded from agent context through repository controls, scoped data preparation, and Claude Code deny rules covering the payment service directory and the auth service directory. Any PR authored with agent assistance carried a marker in the description so reviewers knew to apply extra scrutiny, particularly around edge case handling and error paths.

The result was reduced repetitive engineering effort on adapter scaffolding, test scenario expansion, documentation drafting, and incident correlation, while senior engineers retained ownership of the decisions that would carry the platform for years.

Consulting Insight

Agentic workflows earned their place on work that was repetitive and well specified. The governance held because the boundary lived in the repository, through protected paths, deny rules, and review markers. Senior engineers kept authorship of payment flows, authentication, charging session state, and schema changes.

Results

Within 6 months of public launch, the expanded platform produced measurable product and commercial outcomes.

170,000 Plus Downloads Across Native iOS and Android

Downloads crossed 170,000 across native iOS and Android, giving the operator a directly owned driver surface.

30% Reduction in Failed Charging Attempts

Failed arrival events dropped as stale availability and unreliable charger recommendations were removed from the driver flow.

18% Increase in Partner Station Utilization

Partner station utilization increased as routing shifted drivers toward available capacity across the network.

3 B2B Fleet Contracts Secured Within 6 Months

The platform opened a B2B API revenue line through three fleet contracts in the first 6 months.

Under 2 Second API Response at Peak Traffic

The availability service kept peak-period API responses under 2 seconds.

Infrastructure Cost Decoupled from Traffic Growth

Infrastructure cost stopped scaling directly with traffic once caching, workload sizing, log retention, and request behavior were corrected.

58% Less Repetitive Engineering Effort in the First Cycle

Agentic workflows reduced repetitive engineering effort while senior engineers retained decision control.

7 Connected Engineering Workstreams Across the Platform

The engagement expanded from cloud optimization into seven connected engineering workstreams across the platform.

Combined downloads across iOS and Android passed 170,000, consolidating a meaningful share of Florida EV drivers on a single directly owned surface. The client recorded a 30% reduction in driver arrivals where the selected charger could not deliver the expected charging session, and partner charging locations recorded an 18% increase in utilization, driven by making the network visible in one place and by routing drivers past congested chargers toward available capacity.

Addressing the six cloud review findings within the first delivery cycles broke the earlier relationship where infrastructure cost scaled directly with application traffic and charger telemetry. Subsequent usage growth through the mobile, routing, and B2B releases did not produce proportional cost growth. Cost moved into an engineering signal that could be reviewed alongside traffic, reliability, and product usage.

What began as a focused cloud optimization assignment grew into ongoing engineering across infrastructure, charging data, native mobile, routing, analytics, fleet APIs, and production operations, with joint architecture leadership sustained across the full 6.5 month engagement.

Technologies and Tools

Area	Stack
Services Delivered	Digital Engineering, Consulting Service, IT Cost Optimization, Cloud Cost Optimization, Architecture Audit, Code and Architecture Review, Solution Architecture, Cloud Architecture, Mobile App Development (iOS and Android), Emerging Technologies (IoT), DevOps, Backend Engineering, Data Engineering, QA, Release Engineering
Engineering Model	Joint architecture leadership, joint engineering backlog, agentic engineering workflows on Claude Code and Cursor, continuous telemetry review, controlled fixture test coverage in CI, production feedback loops, Architecture Decision Records
Mobile	Native iOS (Swift, SwiftUI), Native Android (Kotlin, Jetpack Compose), shared API contract via OpenAPI, on device SQLite for offline caching, CarPlay and Android Auto integration as charging category POI apps, native platform speech recognition inside the phone application
Web	React, TypeScript
Backend	Node.js with NestJS, REST with OpenAPI contracts, managed event streaming for provider ingestion and routing telemetry, dead letter queues for provider integration failures
Database and Caching	Managed PostgreSQL with partitioning on high growth tables, managed Redis with differentiated cache policies per data type
Charging Protocol Integration	OCPP 1.6 J for the client's own network management. OCPI for partner network roaming. Partner networks connected under the platform include Blink, ChargePoint, EVgo, and Electrify America. Specific integration mechanism per partner (direct bilateral roaming, provider API, or hub aggregation) has been abstracted for whitelabel and public disclosure discipline.
Routing and Mapping	Google Maps Platform for driver facing turn by turn route rendering under Google's terms, native platform mapping APIs for map surfaces, OSRM for internal route matrix and geographic filtering calculations
Cloud and DevOps	AWS as the underlying cloud, infrastructure as code, managed container services, event streams, object storage, private networking with VPC endpoints for AWS service traffic, structured metrics and traces for observability, GitHub Actions for CI/CD
Authentication and Payments	OAuth 2.0 with OpenID Connect for driver auth, OAuth 2.0 client credentials for B2B API access, TLS for data in transit, a PCI DSS certified payment processor handling all cardholder data
Notifications and Analytics	Firebase Cloud Messaging and Apple Push Notification Service for driver notifications, managed event pipeline feeding mobile behavior analytics and a warehouse based reporting environment used for internal and partner dashboards

Services Delivered

Digital Engineering, Consulting Service, IT Cost Optimization, Cloud Cost Optimization, Architecture Audit, Code and Architecture Review, Solution Architecture, Cloud Architecture, Mobile App Development, Emerging Technologies (IoT), DevOps, Backend Engineering, Data Engineering, QA, Release Engineering

Team Composition

Technical Architect, Scrum Master, Backend Engineers, Mobile Engineers, Web Engineer, DevOps Engineer, Data Engineer, QA Engineer

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EV Charging Platform Engineering with Route Optimization for a Florida Operator

Overview

About the Client

The Challenge

The Solution

The Cloud Review

Charging Data Architecture

Native Mobile Engineering

Range Aware Route Planning

B2B Services and Fleet APIs

Engineering at Pace

Agentic Engineering Workflows

Results

170,000 Plus Downloads Across Native iOS and Android

30% Reduction in Failed Charging Attempts

18% Increase in Partner Station Utilization

3 B2B Fleet Contracts Secured Within 6 Months

Under 2 Second API Response at Peak Traffic

Infrastructure Cost Decoupled from Traffic Growth

58% Less Repetitive Engineering Effort in the First Cycle

7 Connected Engineering Workstreams Across the Platform

Technologies and Tools

Have a question for our team or need help with your project?

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