Product Engineering

High-Velocity Engineering for SaaS & Digital Products

From MVP to scale - AI-augmented teams that ship production-grade software at startup speed with enterprise reliability. Full-stack, cloud-native, AI/ML - every stack, every stage.

Schedule a Technical Deep-Dive See Case Studies →

Trusted by

Seneca ESG Cygon InfraSignal Amatrium FrontFundr

Speed

MVP in 8-12 weeks. Full product teams deployed in 4-6 weeks.

AI-Native Delivery

AI handles code generation, review, and testing. Engineers focus on architecture and domain logic.

Enterprise Grade

ISO 27001, SOC 2 aligned. Production monitoring, CI/CD, automated testing from day 1.

Scale On Demand

2-person pods to 20+ engineers. Add or reduce capacity without re-onboarding.

Eastgate Software's Product Engineering practice delivers full-lifecycle software development - from discovery and architecture through deployment and scaling. With 200+ engineers across Vietnam, Germany, and Japan, we build cloud-native SaaS platforms, AI-powered applications, and enterprise systems for clients in FinTech, ITS, retail, and manufacturing. Every team uses AI-augmented development workflows for faster delivery without sacrificing quality. ISO 27001 certified, SOC 2 aligned, and backed by 12+ years of delivering mission-critical systems for clients including Siemens Mobility and Yunex Traffic.

Industries: FinTechSaaS & Digital ProductsIntelligent TransportationManufacturingRetail & E-Commerce
8-12 Weeks to MVP
200+ Engineers
93% Client Retention
4-6 Weeks Team Ramp
50+ Products Shipped
12+ Years Delivery
ISO 27001 · SOC 2 Aligned · Clutch 5.0 · ACDC (Agent-Centric Development Cycle)

How We Work With You

A proven path from first conversation to long-term partnership. Choose the entry point that fits your stage.

1

Explore

Validate the fit, define the approach

  • Discovery & Technical Assessment
  • Architecture Advisory
  • AI Feasibility Study
  • Pilot / PoC
2

Build

Ship production-grade software

  • Embedded Engineering Teams
  • Full-Stack Product Development
  • Platform Modernization
  • AI/ML Integration
3

Scale

Optimize, automate, grow

  • QA Systems & Test Automation
  • DevOps & Infrastructure
  • Performance Optimization
  • ACDC (Agent-Centric Development Cycle)

Core Engineering Capabilities

6 core engineering domains with deep expertise across the full product lifecycle.

Sub-capability Description Tech Stack Experience
Web Applications SaaS platforms, dashboards, admin panels, customer portals with responsive design. React, Next.js, Vue, Angular 50+ web products shipped
Mobile Development Cross-platform mobile apps with shared codebase and native performance. React Native, Flutter iOS + Android
API & Microservices RESTful and GraphQL APIs, event-driven microservices, message queues. Node.js, Python, .NET, Java High-throughput systems
Database & Storage Schema design, query optimization, caching layers, data migration. PostgreSQL, MongoDB, Redis Enterprise-scale data
Capability

Enterprise Platform Modernization

Cloud migration, ERP integration, workflow automation, and API gateway engineering. Modernize legacy systems with zero-downtime migration.

Cloud Migration & Infrastructure

Zero-downtime migration from on-premise to Azure/AWS with high-availability architecture and auto-scaling.

AWSAzureGCPTerraformKubernetes

Application Modernization

Refactor legacy monoliths into cloud-native microservices. Strangler Fig pattern for incremental, risk-free migration.

.NETJavaDockerKubernetesAPI Gateway

ERP & CRM Integration

SAP, Oracle, Salesforce, Dynamics 365 integration, customization, and data migration with zero business disruption.

SAPOracleSalesforceDynamics 365MuleSoft

Workflow Automation & RPA

End-to-end process automation for procurement, approvals, HR, and finance. Significantly reduce manual processing time.

Power AutomateUiPathPythonNode.js

Data Platform & Analytics

Data warehousing, ETL pipelines, BI dashboards, and real-time reporting for data-driven decision making.

SnowflakedbtKafkaPower BITableau

API Gateway & Integration Hub

Centralized API management connecting internal systems, partners, and third-party services securely.

KongApigeeAWS API GatewayGraphQLREST
Capability

AI & Intelligent Automation

End-to-end AI engineering - from pilot to production ML at enterprise scale.

AI Agent & Agentic Workflows

Autonomous AI agents for document processing, decision support, and multi-step task execution with human-in-the-loop oversight.

LangChainCrewAIDifyOpenAIAnthropicAutoGen

ML Pipeline & Model Deployment

End-to-end ML infrastructure: data preparation, model training, serving, monitoring, and automated retraining.

PyTorchTensorFlowMLflowKubeflowSageMaker

GenAI Integration

Embed LLMs into existing products: RAG pipelines, prompt engineering, fine-tuning, output guardrails, and cost optimization.

OpenAIAnthropicHuggingFaceLangChainPinecone

Computer Vision & Image Analysis

Object detection, OCR, quality inspection, and visual analytics for manufacturing, retail, and infrastructure monitoring.

YOLOOpenCVTesseractAWS RekognitionCustom CNNs

Intelligent Document Processing

AI-powered extraction, classification, and routing for invoices, contracts, compliance documents, and tender files.

spaCyTextractDocument AICustom NLP Models

Process Mining & Automation

Discover bottlenecks, model workflows, and automate with RPA + AI. From manual processes to intelligent automation.

CelonisUiPathPythonPower AutomateKafka
Capability

Cloud-Native & DevSecOps

Ship faster with automated pipelines, infrastructure as code, and security baked into every layer.

CI/CD Pipelines

Automated build, test, deploy with quality gates and rollback support.

GitHub ActionsGitLab CIArgoCD

Container Orchestration

Containerized deployments with auto-scaling, self-healing, and zero-downtime rollouts.

DockerKubernetesHelm

Infrastructure as Code

Reproducible environments, multi-region deployments, disaster recovery.

TerraformPulumiCDK

DevSecOps

Vulnerability scanning, dependency auditing, SBOM generation, and compliance automation.

SonarQubeSnykWhiteSourceTrivyOWASP ZAP

Core Methodology

Our ACDC Development Methodology

Most teams prompt AI and hope for the best. We use a structured methodology built on three pillars: Spec-Driven Design, Test-Driven Design, and Human-in-the-Loop - where specifications become executable artifacts that AI agents build from.

The result: 30-50% faster delivery, fewer escaped defects, and code that matches intent on the first pass.

Spec-Driven Design
Test-Driven Design
Human-in-the-Loop

Our white paper covers the full methodology, the 7-step SDD workflow, practical toolstack, and the patterns that close the gap between intent and shipped software.

Get the Full Methodology

Free PDF white paper delivered to your inbox

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Exclusive for Product Engineering Clients

We Build Your Product and Your GTM Engine

Most engineering partners hand you a product and wish you luck. We equip B2B SaaS clients with the same agentic GTM framework we use internally - autonomous AI agents that handle signal detection, outreach, content generation, and attribution. One partner, two engines.

6-10 Tools replaced
$400 to $750 Saved per month
5 weeks First outreach
15-20 hrs Saved weekly
Read the Agentic GTM Framework

Agent Stack

Signal Visitor tracking + AI research
Enrich Research agents + AI scoring
Generate LLM agents (brand voice)
Execute Email + LinkedIn automation
Record AI-native CRM
Measure Attribution agent

Battle-tested on our own pipeline. Not theoretical.

"Working with Eastgate on our GenAI product exceeded our expectations. They delivered production-grade AI capabilities ahead of schedule with exceptional engineering quality."
Andrew Halonen

Andrew Halonen

Founder, GenAI Startup

Engagement Models

Embedded engineering teams. Outcome-based pricing. Partners, not vendors.

Long-term product development

Embedded Engineering Team

Engineers embedded in your team. Managed by EGS Team Lead. Sprint cadence, shared tools, daily standups.

Min: 4 people, 6+ months
Defined scope, fixed deliverables

Project-Based

Fixed scope, milestone-based. EGS owns PM + QA. You review at each milestone.

Min: $50K per SoW
Testing the fit

Pilot / PoC

Small-scope proof of concept. Fixed price. Working software, not a slide deck.

Min: $15-25K, 4-8 weeks
See our full engineering process and delivery model →

People Also Ask

What does a product engineering engagement look like?
Most engagements start with a 2-week discovery sprint where we map your technical landscape, define architecture, and identify risks. From there we move to a foundation sprint (2-4 weeks) to set up infrastructure, CI/CD, and core functionality - then into iterative 2-week delivery sprints with weekly demos and continuous testing.
How fast can you ramp up a team?
We deploy full product teams in 4-6 weeks. A typical team includes 2-4 engineers, a QA specialist, and a technical lead. For urgent projects we can start with a smaller pod in 2 weeks and scale up as requirements stabilize.
What tech stack do you use for product development?
Our core stack includes React, Next.js, Node.js, Python, .NET, and Java for application development. We deploy on AWS, Azure, and GCP using Kubernetes, Terraform, and Docker. For AI/ML we use PyTorch, LangChain, and OpenAI. We match the stack to your requirements - not the other way around.
How do you integrate AI into the development process?
Every team uses AI-assisted development tools for code generation, automated code review, test generation, and architecture analysis. This means faster delivery without sacrificing quality. We also build AI features into the products themselves - RAG pipelines, ML models, GenAI integration - when the use case calls for it.
What is your approach to quality assurance?
QA is embedded from day one, not bolted on at the end. We run automated test suites on every commit (unit, integration, E2E), AI-assisted test generation for edge cases, performance testing with k6, and security scanning with OWASP ZAP. Every sprint includes a stabilization phase before deployment.
How do you handle scaling from MVP to production?
We architect for scale from the start - containerized deployments, auto-scaling, multi-region capability - even during MVP. When traffic grows, we add performance monitoring (Prometheus, Grafana), implement caching layers, optimize database queries, and scale infrastructure horizontally. No re-architecture needed.
What industries do you serve?
We serve enterprise clients across FinTech, SaaS, Intelligent Transportation Systems (ITS), manufacturing, and retail. Our engineers have deep domain expertise in regulated industries where compliance, security, and reliability are non-negotiable.
How does the Pilot/PoC model work?
A Pilot/PoC engagement runs 4-8 weeks at $15-25K. We take a single, well-defined use case and deliver a working prototype - not a slide deck. This lets you evaluate our engineering quality, communication style, and domain fit before committing to a longer engagement.
Does your AI-augmented development methodology slow down delivery?
For small bug fixes and trivial changes - yes, the overhead isn't worth it. But for features and greenfield projects, spec-driven workflows actually accelerate delivery because AI agents execute from precise specifications instead of ambiguous prompts. We right-size the process: full spec-first for substantial work, lightweight AI-assisted coding for quick fixes.
How do you ensure AI-generated code is production-ready?
AI code goes through the same validation as human-written code - automated security scanning, architecture compliance checks, performance testing, and human review. Many developers report extra debugging during initial AI adoption, so we invest in 5-pillar validation: security, testing, architecture, performance, and compliance.
How do you prevent specs from falling out of sync with code?
We treat specifications as living artifacts, not static documents. Hooks and agents automatically flag when code diverges from specs. During code review, AI validates changes against the original spec. Specs are updated as part of the development workflow, not as an afterthought.
How do junior developers grow when AI handles 'easy work'?
We use AI as a teaching tool, not a replacement for learning. Junior developers pair with AI to understand codebases faster, but they're still responsible for understanding the 'why' behind changes. Code review sessions, architecture discussions, and spec writing develop fundamentals that AI can't replace.
Get Started

Ready to Ship Faster?

Start with a 30-min call. Validate with a $15-25K pilot.

0 -12 wk

MVP Delivery

From kickoff to production

0 -6 wk

Team Ramp

Full team deployed

00 %

Client Retention

Partners, not vendors