What's included
Reduction in
manual workflow load
Quality Engineering
Engineer Confidence Into Every Release.
Quality engineering is the discipline of building confidence into software before, during, and after release. Tech Tammina brings risk-based quality engineering, test automation, performance, API and integration assurance, accessibility, and AI evaluation into the delivery lifecycle so teams can move faster without losing confidence in what reaches users.
Release Speed Is Increasing. So Is the Surface Area of Failure.
Modern releases no longer change one application in isolation. A single business journey can cross web and mobile experiences, APIs, workflows, data pipelines, SaaS platforms, cloud services, and AI.
Teams have to validate not only whether a feature works, but whether the entire business outcome stays reliable as architecture, data, integrations, and intelligent behavior change.
PREVENT
Move quality upstream into requirements, architecture, and design
PROVE
Automate evidence across journeys, APIs, integrations, and releases.
OBSERVE
Use production signals to see what pre-release testing missed.
IMPROVE
Feed defects, incidents, and telemetry back into test design.
Quality Should Operate as a Continuous Control System.
The strongest quality model is a closed loop connecting business risk, engineering evidence, and production behavior.
RISK
Identify journeys, integrations, and data where defects create the greatest impact.
DESIGN
Make requirements, architecture, APIs, and AI behavior testable before build starts.
AUTOMATE
Place checks at unit, API, integration, workflow, UI, and system levels.
EVALUATE
Assess functional, AI, security, and accessibility evidence to gate release decisions.
OBSERVE
Monitor production behavior, incidents, performance, AI drift, and user journeys.
LEARN
Turn production evidence into better coverage and risk models — feeding back into Risk.
AI Changes What We Test — and How We Test It.
AI-Augmented Testing
Accelerate test design, scenario generation, and defect analysis.
- Test design & scenario generation
- Test-data preparation & maintenance
- Defect analysis & documentation
AI & Agent Evaluation
Evaluate AI apps and agents across task success, safety, and cost.
- Task success & groundedness
- Tool use & policy adherence
- Latency, cost & consistency
Continuous Evaluation
Treat AI quality as an ongoing discipline, not a one-time test.
- Model, prompt & data changes
- Retrieval & tool monitoring
- Repeatable scenario evaluation
Human Escalation
Test whether AI recognizes uncertainty and escalates to people.
- Uncertainty recognition
- Autonomy boundaries
- Auditability & escalation routing
Quality Starts Before Code.
REQUIREMENTS
Make acceptance criteria measurable and expose ambiguity early.
ARCHITECTURE
Review testability, failure modes, and integration contracts.
DATA & ENVIRONMENTS
Plan representative test data and service virtualization early.
CI/CD
Run fast checks continuously; reserve slow suites for real risk.
Quality Engineering Across the Enterprise Delivery Lifecycle.
Tool-agnostic quality engineering across the systems and platforms that matter most to the business.
Quality Strategy & Transformation
Define risk models, test architecture, and governance.
Functional & Workflow Quality
Validate business rules, roles, and end-to-end processes.
Test Automation
Engineer maintainable automation connected to CI/CD.
API & Integration Assurance
Validate contracts, schemas, auth, and cross-system behavior.
Performance & Resilience Engineering
Evaluate response time, throughput, and recovery under load.
Data Quality Engineering
Validate transformations, completeness, and lineage-sensitive changes.
Accessibility & Experience Quality
Validate accessibility, responsive, and cross-browser journeys.
AI Quality & Evaluation
Build evaluation datasets and monitoring for AI agents.
Quality Around the Business Journey — Not Just the Application.
Tech Tammina’s delivery experience spans environments where quality crosses applications, workflows, integrations, data, and production operations.
Always-On Student & Institutional Journeys Validate applicant, student, faculty, and administrative journeys end to end.
Education · Digital Experience
Validate applicant, student, faculty, and administrative journeys end to end.
Journey → Roles → Integrations → Performance → Release
Insurance Operations Quality
Document + Workflow
Validate document intake, extraction, business rules, and workflow states.
Document → Extract → Validate & Decide → Review → Complete
Connected Business Applications
API + Workflow
Validate journeys together with APIs, integrations, identity, and data movement.
Experience → API → System → Workflow & Data → Outcome
Release & Resilience Assurance
Cloud & Modernization · Non-Functional
Validate modernized applications under realistic traffic and dependency failure.
Deploy → Load → Fail → Recover & Observe → Improve
AI and Agent Quality
AI · Continuous Evaluation
Evaluate AI journeys for groundedness, tool use, escalation, and cost.
Scenario → Evaluate → Act → Escalate & Monitor → Learn
More Automation Is Not the Goal. Faster, Better Evidence Is.
Automation creates leverage when it’s stable, layered, observable, and aligned to risk.
UNIT & COMPONENT
Fast feedback close to the code and business logic.
API & CONTRACT
Validate services, schemas, permissions, and compatibility before UI.
INTEGRATION, WORKFLOW & DATA
Prove cross-system behavior, messages, and data movement.
UI & MOBILE
Automate the critical user journeys where interface must be validated.
NON-FUNCTIONAL
Automate performance, accessibility, security, and resilience checks.
AI EVAL
Run repeatable scenario evaluations as models, prompts, and tools change.
QUALITY ECONOMICS
Quality Should Improve the Economics of Change.
Investors and technology leaders need evidence that the organization can change software quickly without increasing risk. Baselines and targets are established per engagement.
CHANGE FAILURE RATE
How often does a production change create an incident or rollback?
REGRESSION LEAD TIME
How quickly can teams obtain trustworthy evidence after a change?
JOURNEY RELIABILITY
Are business-critical user and operational journeys consistently completing?
REWORK
How much engineering capacity is consumed by defects found too late?
RECOVERY
How quickly can the organization detect, understand, and recover from failure?
AI QUALITY
Are AI task success, escalation, safety, latency, and cost within
FROM PILOT TO PRODUCTION
Production AI Needs More Than a Model.
A compelling demo is only the beginning. Enterprise AI has to work reliably with real data, real systems, real users, and real exceptions.
CHANGE FAILURE RATE
REGRESSION LEAD TIME
JOURNEY RELIABILITY
REWORK
RECOVERY
IMPROVE
What Has to Work Every Time?
A student journey. A policy change. A payment. An API. A workflow. A customer portal. An AI decision. A production release. Bring us the business-critical journey — we’ll help engineer the quality strategy, automation, performance, integration assurance, and AI evaluation needed to release it with confidence.
What clients say
Alex Rivera
The team didn’t just build an automation; they re-engineered our entire operations workflow. We’re moving twice as fast now.
Sarah Jenkins
“Finally, an automation partner who actually understands enterprise security requirements. No hand-waving, just solid execution.”
Michael Rodriguez
“The team understood our complex integration requirements and built something that just works. Worth every penny.”
Marcia Solis
They made sense of our complex requirements and produced a solution that just works. Couldn’t be happier with the value.
Adam Smith
They quickly grasped our complicated integration needs and delivered a solution that works flawlessly. Absolutely worth the investment.
FAQ
Common Questions About AI & Intelligent Automation
What enterprise teams ask before redesigning a process with AI.
What is quality engineering?
Quality engineering is the discipline of building confidence into software across the entire delivery lifecycle, not just testing it before release. It covers test automation, API and integration assurance, performance, accessibility, and AI evaluation, applied continuously from requirements through production.
How is quality engineering different from traditional QA testing?
Traditional QA tests a feature before release. Quality engineering treats quality as a continuous control system: it moves risk assessment upstream into requirements and design, automates evidence across every layer, and feeds production signals back into test design and risk models.
What is AI and agent evaluation?
AI and agent evaluation tests probabilistic AI systems for task success, groundedness, tool use, policy adherence, safety, latency, and cost using repeatable scenario datasets. It also tests whether the system recognizes uncertainty and escalates exceptions to people rather than acting alone.
Do you test APIs and system integrations?
Yes. API and integration assurance validates contracts, schemas, data movement, authentication, authorization, error handling, retries, and cross-system business behavior. As architecture becomes more distributed, we also validate state consistency, identity and entitlement behavior, and the traceability of a transaction across APIs, events, and services.
What does performance and resilience testing cover?
Performance engineering answers a business question: will the critical journey stay usable as demand, data volume, or infrastructure behavior changes? We model realistic workloads, define service-level objectives, analyze bottlenecks, test dependency failure and recovery, and verify scalability under real pressure.
How do you prevent defects from reaching production?
We move quality upstream into requirements, architecture, and data design so defects are prevented rather than caught late, reducing defect escape and the rework it creates. Production telemetry, incidents, and user behavior are then fed back into risk models and test coverage.
Who owns quality on a project?
Ownership is shared. Product teams own the quality of what they release; quality engineers design test architecture, automation, and risk models; platform engineering provides reusable environments and tooling; and specialists validate the security, data, and AI risks that need dedicated expertise.
What testing tools does Tech Tammina use?
Our approach is tool-agnostic: we fit the client’s applications, pipelines, cloud environment, and engineering standards rather than a fixed toolset, using automation, API, and performance technologies suited to the architecture. We also connect synthetic journeys, canary releases, and observability into pipelines after go-live.