Porter

Application Refactoring · Document Processing · IT Operations Automation

Porter (Smartshift Logistics Private Limited), a leading logistics platform based in India, orchestrates on-demand mini-trucks, tempos, and two-wheelers across 50+ major cities. When order volumes doubled, Porter’s platform engineering team began facing severe scaling constraints on legacy infrastructure.

Read the original story Explore all use cases

Achieved 30% zero-touch driver verification automation on Gemini platform

Automated vehicle categorization on Gemini platform achieved 97.5% classification accuracy, eliminating manual back-office sorting for complex three-wheeler and truck make/models.

Porter leverages GKE and Gemini 2.5 Flash on Google Cloud to modernize microservices and automate driver onboarding.

Reduced Code-to-Deployment lead time down to ~2 days on Google Cloud

Reported impact

Business outcome

Scaled sandbox testing from 28 to 1,200 tests monthly with Google Kubernetes Engine (GKE); Cut driver onboarding times from days to minutes with Gemini; Achieved 30% zero-touch driver verification automation on Gemini platform; Slashed document processing costs by 75% using Gemini Flash.

Solution

For data management, Porter migrated 130+ PostgreSQL databases to Cloud SQL for PostgreSQL , leveraging PostGIS and Cloud Pub/Sub to process real-time geospatial telemetry pings from active drivers. To handle 0.5 Petabytes (500 TB) of monthly application logs economically, Porter deployed a self-hosted Vector collector on GKE streaming directly to Cloud Storage , visualized seamlessly using Grafana. Meanwhile, to boost developer velocity, Porter built “Flash”, an in-house sandbox framework on GKE and Cloud Pub/Sub allowing engineers to run 1,200 parallel feature tests monthly. To transform driver onboarding, Porter deployed Gemini 2.5 Flash on Gemini platform to extract structured JSON data from identity documents. To comply with India's DPDP (Digital Personal Data Protection) and Aadhaar Acts, Porter relies on Cloud Key Management (KMS) for symmetric key encryption.

Data flow

Source data is captured through Cloud Pub/Sub, stored or unified in BigQuery, Cloud Storage, Cloud SQL for PostgreSQL, processed with Vertex AI, orchestrated via Gemini platform, Google Kubernetes Engine (GKE), GKE Gateway API, Cloud Composer, and surfaced through Gemini 2.5 Flash, Gemini Flash, Gemini, Cloud Run

Solution architecture

13 components · 5 layers

Ingest
- Cloud Pub/Sub Moves events between services so the platform can process updates asynchronously at scale.
Compute
- Vertex AI Hosts and orchestrates machine learning and generative AI capabilities inside the solution.
Storage
- BigQuery Stores and queries the analytics and AI-ready datasets that power the workload.
- Cloud Storage Stores large files, lake data, or static assets that feed downstream analytics and applications.
- Cloud SQL for PostgreSQL Provides the managed relational data layer for operational application data.
Serving
- Gemini 2.5 Flash Handles low-latency generative AI tasks and natural-language interactions for the application.
- Gemini Flash Handles high-volume generative AI tasks with lower latency and lower serving cost.
- Gemini Powers natural-language reasoning, summarization, generation, or assistance inside the workflow.
- Cloud Run Runs application services and proprietary code in a managed container environment.
Orchestration
- Gemini platform Coordinates the solution's agentic and generative AI workflows on Google Cloud.
- Google Kubernetes Engine (GKE) Orchestrates the platform's containerized services and high-concurrency workloads.
- GKE Gateway API Provides centralized traffic and service routing for the platform's Kubernetes applications.
- Cloud Composer Schedules and orchestrates data pipelines across Google Cloud services.

Architecture clues

Achieved 30% zero-touch driver verification automation on Gemini platform
Cut driver onboarding times from days to minutes with Gemini
Porter leverages GKE and Gemini 2.5 Flash on Google Cloud to modernize microservices and automate driver onboarding.
Reduced Code-to-Deployment lead time down to ~2 days on Google Cloud
Scaled sandbox testing from 28 to 1,200 tests monthly with Google Kubernetes Engine (GKE)
Slashed document processing costs by 75% using Gemini Flash

Evidence from the source

Achieved 30% zero-touch driver verification automation on Gemini platform

Cut driver onboarding times from days to minutes with Gemini

Reduced Code-to-Deployment lead time down to ~2 days on Google Cloud

Scaled sandbox testing from 28 to 1,200 tests monthly with Google Kubernetes Engine (GKE)

Slashed document processing costs by 75% using Gemini Flash

Transparency Evidence & methodology 100/100 reference value · 79% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100

Problem clarity20/20
Measures whether the customer's operational problem is specific enough to compare.
Implementation depth25/25
Rewards named components, data flow, integrations, and architecture detail.
Outcome specificity20/20
Measures whether the reported benefit is concrete rather than a generic claim.
Quantified impact25/25
5 metrics describe measurable impact.
Deployment scale10/10
Rewards evidence of a live or scaled deployment and meaningful integration scope.

Evidence confidence79%

Source authority20/25
The main evidence is an official vendor-published customer story.
Claim traceability21/25
10 of 12 key fields link to a named source.
Direct evidence20/20
5 direct source quotes retained.
Date provenance0/10
The publication date has no documented provenance.
Source transparency8/10
1 source reference document publisher, URL, authority, retrieval, and supported fields.
Implementation specificity10/10
Rewards source-backed components, data flow, quantified impact, and a specific outcome.

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

Google Cloud
Porter customer story

First-party
https://cloud.google.com/customers/porter

Source type
Vendor-published customer story
Supports
- Customer
- Business problem
- Solution
- Products
- Solution components
- Data flow
- Integrations
- technical area
- use case category
- Business outcome
- outcome category
- Metrics
- Architecture
- evidence quotes

Date evidence

Sitemap last modified Oct 9, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
Open date source
Unverified extracted date Oct 9, 2026 · Google Cloud · Unknown Legacy extracted date without source-page confirmation.
Open date source

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

Similar use cases

See all like this

Google Cloud Archived · Sep 26, 2026 Delivery Hero Logistics Germany Delivery Hero needed a standardized AI engineering platform that could support autonomous software delivery, let teams use the strongest coding model for each task, and still preserve centralized governance, review, and… Application RefactoringIT Operations Automation Claude · Gemini · Gemini CLI

AWS Mar 5, 2024 Mercedes-Benz Consulting Commercial and Professional Services Global With millions of internal documents to search through, getting relevant answers quickly was a standing challenge for employees. Additionally, if an employee left the company or a colleague with specific knowledge was… Application RefactoringDocument Processing AWS Glue · AWS Lambda · Amazon OpenSearch Service

Google Cloud Modified · Oct 9, 2026 Anthropic Software / SaaS San Francisco, CA, USA During large-scale training runs, traffic is highly variable—dropping to near zero during model checkpointing before bursting to millions of events per minute. Anthropic needed a serverless messaging queue that scaled… Handles peak throughput of over 1M events per second Document ProcessingIT Operations Automation BigQuery · Bigtable · Cloud Pub/Sub