Problem
Growth exposes single-node designs, unclear failure modes, and releases that depend on heroics. Cost, latency, and incidents become surprises. New AI workloads land on infrastructure that was never designed for them.
Cloud-native platforms
Platforms that stay available as products, traffic, and teams grow.
GillTech AI designs cloud-native backends, distributed services, and delivery pipelines that engineering leaders can operate — with enough clarity for business stakeholders to understand the trade-offs.
Solution
The company designs for failure, scale, and operability: services, data stores, caches, queues, search, CI/CD, and observability. Choices are explained in business terms (risk, cost, time-to-change) as well as technical ones.
Capabilities
- AWS-oriented cloud architecture
- Docker and container delivery
- Microservices where they earn their keep
- Event-driven architecture
- Distributed systems design
- Caching, databases, and search
- Queues and asynchronous work
- Observability
- CI/CD
- Scalability, reliability, and performance engineering
Use cases
A product backend that must absorb traffic spikes without a manual failover ritual
Decomposing a hotspot from a monolith into a service with its own data and SLOs
Introducing queues so slow work does not block user requests
Making deployments routine so AI and product changes can ship during business hours
Architecture
- Clients
- Cloud-native services
- Data / Cache / Search / Queues
- CI/CD
- Observability
Technical depth, executive clarity
For executives
Cloud and distributed design is about risk, cost, and time-to-change: systems that stay available as products and traffic grow, with failures that can be seen and recovered from.
For engineering leaders
AWS-oriented architecture, containers, data stores, caches, search, queues, CI/CD, and observability — kept as simple as the problem allows, with SLOs that match how the business actually operates.
Delivery approach
Start from SLOs and failure modes, not from a service diagram. Keep the system as simple as the problem allows. Automate delivery and rollback. Instrument before optimizing. Review cost alongside reliability.
Technology
AWS
Docker
CI/CD
PostgreSQL
Redis
Elasticsearch
Queues
Metrics and tracing
Outcome
A platform that can take more load and more change with fewer surprises — understood by both engineering and the operators of the business.
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Continue exploring
Service
Legacy Modernization
GillTech AI modernizes applications, APIs, databases, and architecture in controlled increments so operations keep running while the platform becomes easier to change. Modernize without unnecessary disruption.
Service
System Integration
GillTech AI connects applications, APIs, databases, CRMs, ERPs, payments, messaging, and internal services into reliable workflows.
Service
Engineering Productivity
GillTech AI applies AI-assisted development, tests, documentation, and workflow design so teams ship with more consistency — without treating AI as a replacement for engineers.
Industry
SaaS & Technology
AI features that never leave the lab
Industry
E-commerce & Retail
Orders, inventory, and support sitting in different tools
Industry
Financial Services
Legacy cores that slow product change
Work
Products and illustrative solutions
See GillTech AI products and solution patterns.
Contact
Book a Consultation
Discuss AI engineering, automation, or modernization.