Buying principleA migration is complete when the team can explain what changed, prove the acceptable differences and reverse the cutover without improvising.
LLM migration consulting starts with a behaviour inventory
The first deliverable should describe what the current product actually does. Record user tasks, prompts, retrieval, tool calls, output schemas, refusal rules, memory, moderation, latency targets and the human handoffs around them.
Imagine an invoice assistant that extracts fields, asks for missing pages and routes uncertain totals to review. Migrating only the model call misses the confidence threshold, the JSON parser, the retry policy and the screen where a reviewer sees the source document.
The inventory turns “move to a new model” into a set of observable contracts. An AI product requirements document can name the intended behaviour, but the migration scope must also capture the live implementation and its workarounds.
Buy a migration contract before buying implementation hours
A migration contract links each product capability to its current evidence, proposed change, acceptable difference, release gate and owner. Without that chain, a consultant can finish the adapter while the product quietly becomes less reliable.
For a contract summarizer, the source-citation format may change even when the prose looks better. The migration contract can require every legal claim to preserve a verifiable source span, with unsupported conclusions treated as a blocking failure.
| Workstream | Evidence to inspect | Decision it supports |
|---|---|---|
| Behaviour inventory | Tasks, prompts, tools, data, interfaces and failure paths | What must remain stable |
| Compatibility map | API, schema, streaming, tool and state differences | What must be adapted |
| Parity suite | Real cases, rubrics, baselines and accepted deltas | Whether the new system is good enough |
| Operational profile | Latency, cost, rate limits, privacy and observability | Whether it can run under real conditions |
| Cutover plan | Traffic stages, gates, owners and rollback triggers | When production may move |
| Handover | Runbook, dashboards, known limits and change process | How the team operates after launch |
Compatibility is not product parity
An SDK can accept familiar parameters while the product behaves differently. Models vary in instruction following, tool selection, structured output, token usage, refusals and how they handle long or ambiguous context.
Anthropic's OpenAI SDK compatibility guide is explicit that the compatibility layer is mainly for testing and comparison, and that unsupported fields may be ignored. That is useful for a trial, not evidence that a live workflow can switch unchanged.
OpenAI's Responses API migration guide shows the same principle at the platform level: objects, conversation state and tool loops can change even when the user-facing job remains the same. Ask the consultant to map each interface difference to a product consequence.
Parity tests should compare decisions, not identical wording
The old and new systems do not need to produce the same sentence. They need to meet the same product contract, with any improvement or regression made visible to the people who own the risk.
A support assistant may answer a refund question with different phrasing. Compare whether it cites the correct policy, asks for required information, avoids an unauthorized refund and routes the case correctly. Exact text matching would punish harmless variation and miss a dangerous action.
OpenAI's evaluation guidance recommends task-specific datasets, comparison and continuous evaluation. If the migration needs a reusable test system rather than a one-off comparison, the LLM evaluation consulting guide explains the required rubric and release evidence.
| Layer | Example check | Wrong shortcut |
|---|---|---|
| Interface | Schema, streaming events, errors and tool arguments | The request returns HTTP 200 |
| Behaviour | Task success, refusal and escalation | The answer sounds fluent |
| Operations | Latency, token use, rate limits and retries | The demo felt fast |
| Risk | Privacy, logging, data location and access | The provider is well known |
Cost and latency must be measured on the whole workflow
A lower token price can still produce a more expensive product if the new model needs longer prompts, more retries or extra validation calls. Measure the complete task, including retrieval, tools, graders and failed attempts.
For a document assistant, one model may answer quickly but call the extraction tool twice. Another may use more output tokens but finish in one pass. Record cost per completed task, end-to-end latency and the rate of human review rather than comparing a price table alone.
Use representative traffic and disclose the assumptions. A scenario based on 1,000 short English requests says little about a product handling long multilingual documents.
Stage the cutover and make rollback boring
Do not replace the old path for every user in one release. Run shadow comparisons where policy allows, then move a small, observable traffic slice before expanding by task, customer or risk level.
A recruiting assistant could migrate read-only draft generation first while keeping candidate ranking on the existing path. If citation failures or review time exceed the agreed gate, route traffic back with a configuration change rather than an emergency code rewrite.
Rollback needs a deadline, an owner and preserved compatibility with the previous system. The consultant should rehearse it before broad cutover. NIST's AI Risk Management Framework calls for testing before deployment, production monitoring and documented recovery controls.
The handover should outlive the provider launch
The internal team should receive the adapter, parity suite, decision log, dashboards, runbook and known-limit register. It should also know which provider or model changes require the suite to run again.
Suppose the new provider changes a default model version three months later. The owner should be able to pin the version, rerun critical cases and review failed traffic without calling the original consultant to remember how the system works.
An AI governance engagement can assign longer-term approval and incident roles. The migration itself should still leave enough operational evidence for the product and engineering teams to own day-to-day changes.
Use this checklist before hiring an LLM migration consultant
A credible proposal makes the first workflow, comparison method and cutover rule concrete. Check these items before approving a broad rewrite.
- The first live workflow and costly failure are named.
- Prompts, tools, data, schemas, state and human handoffs are inventoried.
- API differences are mapped to visible product consequences.
- Parity cases use representative production tasks and agreed rubrics.
- Cost and latency cover the complete workflow, not one model call.
- Critical regressions can block cutover even if an average improves.
- Traffic stages, monitoring owners and rollback triggers are explicit.
- The internal team receives runnable tests, a runbook and known limits.
Bring Seuil one workflow, one failure and one deadline
Send Seuil one live AI workflow, the failure that would cost trust or money, the current provider path and the date forcing the change. That is enough to define a migration contract and decide whether implementation help is justified. For broader selection criteria, use the AI product development partner guide.