AI Platform Migration

Platform migration happens for various reasons.

Capability gaps: Your current platform cannot do something you need. Perhaps you need better language understanding, more sophisticated workflows, or capabilities it simply does not offer.

Commercial concerns: Billing models change or usage grows beyond what your organisation can sustain. A different platform may fit your constraints better.

Vendor risk: Acquisitions, product changes, or company direction raise concerns about long-term viability. Moving to a more stable alternative reduces risk.

Integration needs: Your existing platform does not work well with systems you need to connect. A different choice offers better compatibility.

Performance issues: Response times, accuracy, or reliability do not meet requirements. Another platform can deliver what your current one cannot.

Moving from one AI platform to another requires more than copying code. Different platforms use different approaches, different data structures, and different paradigms.

Assessment evaluates your current system and identifies what needs to migrate. We document functionality, integrations, data, and dependencies.

Platform selection ensures the target platform actually meets your needs. We verify that migration solves the problem you are trying to address.

Architecture design determines how the new system will work. Sometimes migration is an opportunity to improve, not just replicate.

Content migration moves conversation flows, intents, entities, training data, and knowledge bases. Format translation is often required.

Integration rebuild reconnects to your business systems. APIs and data flows may need adjustment for the new platform.

Testing verifies that the migrated system works correctly. We compare behaviour against the original and address discrepancies.

Cutover moves production traffic to the new platform with minimal disruption. We plan carefully to avoid user impact.

We frequently help organisations move between:

Legacy chatbot platforms to modern conversational AI. Moving from brittle intent trees to more capable and maintainable approaches.

Single-cloud to multi-cloud (or different cloud providers). Aligning with security, governance, and operational requirements.

Custom implementations to managed platforms. Reducing operational overhead where standard capabilities are sufficient.

Managed platforms to custom implementations. Regaining control when constraints, integrations, or governance needs outgrow the platform.

Framework-based systems to LLM architectures. Evolving the core approach while preserving user experience and business logic.

Each migration type has characteristic challenges and approaches. Our experience helps avoid common pitfalls.

Migration does not mean starting over. We preserve:

Conversation design and UX work. What users see and how the interaction feels.

Training data and learned behaviours. The examples and patterns that make your system effective.

Integration logic and business rules. The real operational value behind the interface.

Analytics and baselines. What “good” looks like today, so improvements can be measured.

Operational knowledge. Runbooks, known failure modes, and ownership models.

The goal is continuity. Users should experience improvement, not disruption. Your team should retain familiarity, not face an entirely new system.

Platform migration carries risk. Systems might behave differently. Users might notice changes. Operations might face unfamiliar problems.

We manage these risks through:

Parallel operation: Running old and new systems simultaneously during transition, allowing comparison and fallback.

Gradual cutover: Moving traffic progressively rather than all at once, limiting blast radius if problems occur.

Comprehensive testing: Verifying behaviour before, during, and after migration with systematic test coverage.

Rollback planning: Maintaining ability to return to the previous platform if serious issues emerge.

Migration timelines depend on system complexity. Simple chatbots might migrate in two to four weeks. Complex systems with extensive integrations might take three to six months.

Investment reflects effort required: assessment, redesign, rebuild, testing, and cutover. We provide estimates after evaluating your current system.

Use these prompts to clarify what you should preserve, what to change, and how to reduce risk.

“What should we preserve from the current system (UX, business rules, data), and what should we redesign?”

“What are the highest-risk behaviours and integrations, and how do we test them before cutover?”

“What migration approach fits best: parallel run, staged rollout, or full replacement?”