What's included
Reduction in
manual workflow load
What's included
Run Critical Technology. Keep Improving It.
Tech Tammina managed services keep business-critical applications, platforms, integrations, and cloud services stable today, while continuously reducing friction, technical debt, recurring incidents, and the cost of change through application management, platform support, production operations, engineering, automation, knowledge management, and continuous improvement.
Go-Live Is the Beginning of the Operating Life.
The value of an implementation is determined long after launch. Applications change, platforms upgrade, integrations fail, business rules evolve, users need support, and technical debt accumulates.
A managed service has to do more than close tickets — under both fully managed and co-managed models, it has to preserve operational knowledge, protect service continuity, make change safer, and continuously improve the estate.
Run
Own service health, requests, monitoring, and daily operations.
Resolve
Restore service, find root cause, prevent repeat incidents.
Improve
Use service data to cut recurring work, strengthen knowledge.
Evolve
Carry upgrades, enhancements, and AI capabilities forward.
Operate the Full Application and Platform Lifecycle.
Six core services keep applications, platforms, and product context running and improving.
Application Managed Services
Triage incidents, fix defects, deliver prioritized enhancements.
Platform Managed Services
Support platforms, environments, integrations, upgrades, release health.
Production Operations
Monitor availability, jobs, interfaces, performance, critical business flows.
Integration & API Support
Operate APIs, jobs, partner connections, and errors across enterprise systems.
Cloud & DevOps Operations
Support cloud environments, pipelines, infrastructure automation, observability.
Enhancements & Continuous Engineering
Convert recurring issues and debt into a governed backlog.
Use AI to Reduce Operational Friction — Not to Hide It.
AI changes managed-services economics when it’s connected to trusted service history, observability, knowledge, and approved operational actions — autonomy expands only where telemetry, runbooks, and controls support it.
Service Intelligence
Summarize incidents and surface known resolutions faster.
- Summarize incidents and service history
- Correlate related context automatically
- Surface known resolutions faster
Knowledge Intelligence
Turn resolved work into reusable operational knowledge.
- Improve knowledge search and retrieval
- Identify knowledge gaps
- Keep human review on production guidance
Proactive Operations
Use telemetry and patterns to catch issues early.
- Identify emerging issues from telemetry
- Recommend corrective action early
- Data preparation
Agent-Assisted Operations
AI agents handle low-risk tasks, humans approve the rest.
- Act through approved tools and workflows
- Apply permissions and full auditability
- Require human approval for material actions
Transition. Stabilize. Operate. Improve. Evolve.
A managed-service relationship should have a visible operating lifecycle, not an indefinite steady-state queue.
Transition & Stabilize
Discover and map the estate, capture knowledge, baseline demand, close high-frequency gaps.
Operate
Run incidents, requests, problems, releases, monitoring, and service reporting.
Optimize
Automate repeatable work, strengthen knowledge, reduce recurring incidents.
Evolve
Carry upgrades, enhancements, modernization, and AI capabilities into the roadmap
Combine Service Discipline With Engineering Reliability.
ITSM provides service-management discipline; SRE brings engineering methods to reliability and toil reduction.
Service Management
- Incident Management — restore service with clear ownership and escalation
- Problem Management — find systemic causes, reduce repeat incidents
Change & Delivery
- Change & Release — control production change with rollback paths
- Service Requests — standardize and automate repeatable requests
Reliability & Learning
- SLO / Service Health — track availability, latency, critical journeys
- Toil Reduction — eliminate or automate repetitive operational work
- Error Learning — strengthen monitoring, runbooks, and recovery
Support the Business Service — Not Just the Technology Component.
Workflow Platforms . Application Management
Enterprise Workflow & Case Platforms Operate workflow apps, cases, rules, documents, and platform changes with business context.
Workflow Platforms . Application Management
Flow: Monitor → Support → Resolve → Release → Improve
Customer, Employee & Partner Applications Support portals, web and mobile apps, user journeys, and prioritized enhancements.
Digital Products . Application Support
Flow: Request → Diagnose → Resolve → Enhance → Release
APIs, Interfaces & Scheduled Processing Monitor interfaces, jobs, events, and dependencies so processes don't fail silently.
Integration . Production Operations
Flow: Detect → Trace → Recover → Correct → Prevent
Cloud Application Operations Operate runtime environments, pipelines, and dependencies with engineering close to production.
Cloud . DevOps Operations
Flow: Observe → Respond → Recover → Tune → Automate
Post-Implementation Support A structured, dependable run-services path from go-live into ongoing support.
Implementation Partners . Run Services
Flow: Handover → Stabilize → Operate → Improve → Evolve
Every Ticket Should Leave the Service Better Than It Found It.
Recurring managed-services value comes from converting operational demand into permanent improvement.
Incident → Problem
Cluster recurring symptoms and address systemic causes.
Problem → Engineering
Turn root causes into code, configuration, or architecture fixes.
Request → Automation
Standardize and automate repeatable low-risk work.
Resolution → Knowledge
Capture validated fixes and operational context for reuse.
Telemetry → Prevention
Improve alerts, service indicators, and proactive detection.
Backlog → Modernization
Use support evidence to guide refactoring and upgrade work.
The Goal Is Not Cheaper Tickets. It Is a Better Run-to-Change Ratio.
For technology leaders and implementation partners, the value of managed services is releasing capacity back into innovation.
Service Stability
Recurrence
Mean Time to Restore
Toil
Knowledge Retention
Innovation Capacity
Who Owns the Application After Go-Live?
A new platform. A business-critical application. A portfolio inherited from another provider. A product that needs ongoing enhancements. An implementation partner looking for a dependable run-services path. Bring us the operating challenge — we’ll help define the transition, service model, support architecture, governance, knowledge, and continuous-improvement path to keep it running, and keep making it better.
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 Managed Services
What are Tech Tammina's managed services?
Managed services keep business-critical applications, platforms, integrations, and cloud environments stable while continuously reducing incidents, technical debt, and the cost of change. The model combines application management, platform support, production operations, engineering, automation, and knowledge management under one operating framework: Run, Resolve, Improve, Evolve.
What's included in application managed services?
Application managed services cover incident triage and resolution, user support, defect fixes, configuration management, release maintenance, and prioritized enhancements. The goal is to keep business applications healthy across the full operating lifecycle while preserving application knowledge for future support and improvement work.
How does AI fit into managed services?
AI is applied to reduce operational friction, not hide it. Service intelligence and knowledge intelligence come first — summarizing incidents, surfacing resolutions, and improving search. Agent-assisted actions expand only where telemetry, runbooks, and controls support them, with human approval required for material production actions.
What happens during service transition?
Transition covers discovering the technology estate, mapping service boundaries and dependencies, and capturing runbooks and operational knowledge. The team shadows existing operations, then reverse-shadows to validate readiness, before baselining demand and incident patterns. Steady-state begins only once agreed operational-readiness criteria are met.
What operating models are available?
Options include fully managed services, co-managed delivery alongside internal teams, platform run services, post-implementation application managed services, partner run services for implementation teams, and a dedicated support pod aligned to a defined application or platform portfolio, chosen by the client’s ownership needs.
Do you support implementation partners after go-live?
Yes. Post-implementation support gives implementation partners a dependable run-services path after deployment, moving through handover, stabilization, operations, and continuous improvement. Project teams can transition out while clients retain continuity, operational knowledge, and a governed path for future enhancements and modernization work.
How is managed services performance measured?
Beyond service-level agreements, performance is tracked through reliability, recurring incidents, change failure, backlog health, knowledge coverage, and toil reduction, with governance visibility into cost, risk, and improvement. Experience and responsiveness are measured alongside SLAs so outcomes, not just percentages, are managed.
Which platforms and systems do you support?
AI supports classification, extraction, data preparation, comparison, validation, and summarization, while delivery teams handle exceptions and judgment-based work. Tech Tammina measures demand, delivery, quality, capacity, and knowledge to see where automation and AI genuinely help. AI is introduced where the process, data, controls, and economics support it — not as a label on every engagement.