AI applications & copilots
Application logic, prompts and instructions, model access, tool calls, business workflow integration and production controls.
AI MIGRATION SERVICES · VIETNAM
Migrate AI workloads without losing the systems around them.
TitanBases provides AI Migration services in Vietnam for enterprise AI workloads moving across models, AI platforms and agent runtimes. We assess dependencies, design the target architecture, migrate the workload and validate the production controls around it.
- Workload-first assessment
- Target architecture + dependency mapping
- Evaluation + controlled cutover
MIGRATION CONTROL PLANE
CURRENT RUNTIME
Runtime A
TARGET RUNTIME
Runtime B
Model / API
RAG
Memory
Tools & MCP
Identity
Observability
Migration succeeds when workload behavior and production controls meet acceptance criteria — not merely when the new model responds.
Planning an AI migration?
Start with a workload assessment. Tell us what is moving, what it depends on and what outcome you need to preserve.
WHAT WE MIGRATE
AI workloads are more than a model endpoint.
The migration scope is defined around the workload and its production dependencies, not around a single API call.
Enterprise RAG
Retrieval pipelines, embeddings, vector search, metadata, grounding, access controls, evaluation datasets and observability.
AI agents
Agent instructions, tools, MCP or equivalent interfaces, durable state, memory, schedules, permissions, human approvals and runtime behavior.
Model & platform transitions
Workloads moving between direct model APIs, managed AI platforms, cloud environments or agent runtimes while preserving required behavior.
MIGRATION CONTROL PLANE
AI migration is not an API swap.
A production workload can fail even when the replacement model responds correctly. Migration planning has to account for the systems surrounding inference.
Instructions
System prompts, procedures, skills and runtime policy that shape workload behavior.
Tools & integrations
Tool contracts, MCP servers, enterprise APIs and write permissions.
Data & retrieval
Knowledge sources, indexes, embeddings, retrieval behavior and grounding controls.
State & memory
Conversation state, task state, durable memory and handoff context.
Identity & authorization
Enterprise identity, roles, approvals, secrets and least-privilege access.
Evaluation & operations
Behavioral evals, observability, rollback criteria, incident response and release controls.
ENGAGEMENT MODEL
From current-state workload to controlled cutover.
The exact sequence depends on the workload, but the engagement follows a measurable architecture and validation path.
- 01
Assess the workload
Inventory the current runtime, business outcome, dependencies, constraints and migration trigger.
- 02
Map dependencies
Identify model, data, tools, memory, identity, schedules, observability and external side effects.
- 03
Design the target
Define target architecture, interfaces, security boundaries, data flow and operating model.
- 04
Migrate & integrate
Implement the target workload and reconnect required enterprise systems without hiding incompatibilities.
- 05
Evaluate & cut over
Run behavioral and operational acceptance, define rollback conditions and move traffic under controlled criteria.
DELIVERABLES
Outputs your engineering team can operate.
The engagement is designed to leave behind explicit architecture, migration evidence and operational decisions rather than a black-box handoff.
01
Workload & dependency assessment
Current-state inventory, migration constraints, critical dependencies and risk classification.
02
Target architecture
Target runtime, data and tool boundaries, identity model, observability and control points.
03
Migration plan
Sequenced workstreams, compatibility gaps, test strategy, cutover and rollback criteria.
04
Evaluation & acceptance evidence
Workload-specific evaluation results and production readiness findings for the agreed migration scope.
TECHNICAL FOUNDATION
Built on the TitanBases AI Migration Framework.
For the technical model behind workload boundaries, dependency portability and migration acceptance, read the engineering framework that defines AI migration beyond model switching.
FAQ
AI Migration services — common questions.
Direct answers for teams evaluating an enterprise AI migration project.
What is an AI Migration service?+
An AI Migration service helps an organization assess, redesign, move and validate an AI workload when models, platforms or agent runtimes change. The scope can include applications, RAG, agents, data, tools, memory, identity, evaluation and operating controls.
Does TitanBases provide AI Migration services in Vietnam?+
Yes. TitanBases provides AI Migration services for enterprise workloads in Vietnam, covering assessment, dependency mapping, target architecture, migration implementation and technical validation.
Is AI migration the same as switching an API endpoint?+
No. A model or API endpoint is only one dependency. Production workloads can also depend on retrieval, tools, memory, identity, permissions, schedules, observability and evaluation behavior.
What types of AI workloads can be migrated?+
Typical scopes include AI applications and copilots, enterprise RAG, AI agents, model and API integrations, and workloads moving between direct APIs, managed AI platforms, cloud environments or agent runtimes.
Can TitanBases migrate an AI agent with tools and memory?+
Yes, when those dependencies can be inventoried and an appropriate target design is available. The migration scope can include instructions, tools, MCP or equivalent interfaces, durable state, memory, permissions, schedules and evaluation.
How do you validate that an AI migration succeeded?+
Acceptance should be based on agreed workload behavior and operational controls rather than model availability alone. That can include evaluation results, integration checks, permission boundaries, observability, rollback criteria and production readiness findings.
START WITH THE WORKLOAD
Planning an AI migration in Vietnam?
Share the workload, current platform and migration objective. TitanBases will help define the technical assessment and next implementation step.