AI Studio for production applications
We build and operate production AI applications that connect people, enterprise systems and intelligent agents across modern cloud platforms.
From Slack-native apps to cloud AI platforms, we help teams move beyond prototypes and run AI reliably in production.
Coordinates models, permissions, tools, evidence, and actions inside operational workflows.
Philosophy
Teams do not need another dashboard that nobody opens. They need intelligent software that fits into daily workflows, connects existing systems and helps people take action faster.
TER TECH builds AI applications that live inside conversations, support requests, engineering workflows, approvals and operational processes.
Capabilities
From application development to cloud operations, we help organizations build AI systems that are ready for production and designed to evolve.
Build intelligent applications that solve real business problems.
Production infrastructure designed for reliable AI workloads.
Operate, monitor and continuously improve AI applications after they reach production.
AI value is created after deployment, when real users depend on the application every day.
Connect AI applications with the systems your business already relies on.
Production lifecycle
Most AI initiatives look impressive in a controlled demo. The real challenge starts when the application has users, permissions, integrations, monitoring, costs, failures and business expectations. TER TECH focuses on the full production lifecycle.
Products
We build reusable AI products alongside custom client solutions.
Context helps engineering teams investigate production issues by collecting read-only evidence from cloud infrastructure, logs, deployments and operational systems, then posting grounded summaries where teams collaborate.
A Slack-native assistant for IT, HR and operations requests that answers common questions, creates tickets and escalates when needed.
A platform assistant that helps developers check service status, request environments, find runbooks and understand deployment issues.
An AI assistant that helps teams find trusted answers across documents, systems and internal knowledge bases.
Solutions
AI applications should adapt to the way each team works.
Incident investigation, deployment visibility, runbook search, service ownership and release coordination.
Developer self-service, internal tools, environment requests, status checks and engineering productivity workflows.
Access requests, account provisioning, device support, policy questions and ticket automation.
Approvals, vendor requests, recurring reports, internal workflows and process automation.
Customer escalations, SLA visibility, knowledge answers, ticket routing and conversation summaries.
Employee questions, onboarding workflows, policy answers, approval flows and internal support.
Integrations
We are focusing the first version around AI platforms, cloud infrastructure, and Slack-native workflows, with the rest of the enterprise ecosystem added as the operating surface expands.
OpenAI, Anthropic, Amazon Bedrock and Google Gemini connected through governed application workflows.
AWS, Kubernetes, Terraform, CloudWatch, Datadog and production deployment paths for AI workloads.
Apps, agents, approvals, incident investigation and support workflows where teams already collaborate.
Featured build
AI-powered incident investigation for engineering teams.
During production incidents, engineers lose time gathering evidence from logs, deployments, cloud infrastructure and tickets.
Context collects trusted evidence and generates grounded incident summaries directly where teams collaborate.
Slack -> AWS -> GitHub -> CloudWatch -> SQS -> OpenAI -> Amazon Bedrock
Faster investigation, clearer context and less manual evidence gathering.
Production First. Every application is designed with real users, real systems and real operating conditions in mind.
Cloud Native. Built for modern cloud platforms using containers, serverless, automation and infrastructure as code.
Observable. Applications should be measurable, debuggable and monitored from day one.
Secure. Least privilege, secure credentials, controlled access and enterprise-ready architecture.
Reliable. Designed to handle failures, integrations, rate limits and operational complexity.
Evolvable. Built to improve continuously as users, models and business workflows change.
About
TER TECH combines AI Engineering, Cloud Engineering and Platform Engineering to help organizations move from AI experiments to production-ready applications.
We build applications that connect to real systems, run on cloud infrastructure and become part of the daily workflow.
We don't stop after deployment. We build. We operate. We evolve.
FAQ
Yes. We build custom AI applications, agents, internal tools and workflow automations based on your business needs.
Yes. Slack-native applications are one of our strongest specializations, especially for engineering, IT, support and operations workflows.
Yes. Operation is part of our positioning. We can monitor, maintain, improve and support AI applications after they reach production.
Yes. We integrate with cloud platforms, developer tools, business systems, databases, knowledge bases and internal APIs.
Our strongest expertise is AWS, but we can also support applications running on other modern cloud platforms depending on the project needs.
No. We build AI agents, AI-powered applications, internal tools, integrations and automation workflows.
Yes. We can help transform existing prototypes into production-ready applications with proper architecture, integrations, monitoring and deployment processes.
Discovery
Let's build AI applications that become part of your team's daily workflow and keep them running reliably in production.
Slack
AWS
OpenAI
Anthropic
GitHub
Jira
Salesforce
CloudWatch
Datadog
Kubernetes
Terraform
PostgreSQL
Redis