LLM migration services
We migrate production LLM workloads between providers and model versions without the outage. Breaking-change audits, eval-backed cutovers, and honest cost re-baselining, because the parameters that return a 400 are the easy part and the silent behavioral changes are what actually bite.
No sales script. You talk to the engineers who'd build it.
Our team works a shifted day so you get real-time standups and same-day turnarounds in your time zone, not next-morning replies.
Every line of code, model weight, and prompt is yours from day one. NDAs and clean IP assignment are standard, not an upsell.
You work directly with the engineers building your system. No account managers sitting between you and the people writing code.
We move from scoping to a working system in production in weeks. Most engagements ship something usable inside the first month.
What we build
Concrete systems we ship, tuned to your data and your stack.
Breaking-change audit
Every call site checked for removed parameters, changed defaults, and deprecated request shapes before anything moves.
Eval-backed cutover
We build the eval set first, then migrate against it, so quality is measured rather than hoped for.
Cost re-baselining
Tokenizers change between generations. We re-measure real prompts instead of applying a multiplier.
Prompt re-tuning
Newer models often need shorter prompts. We strip scaffolding that now causes over-verification.
How we work
Scope & evals
We pin down what success means and build the evaluation set before writing the feature, so quality is measured, not guessed.
Build in the open
Weekly demos against real data. You see progress every week and can change direction before it gets expensive.
Ship & instrument
We deploy with logging, cost tracking, and guardrails in place, then tune against production traffic.
Hand off or stay
Take the keys with full docs, or keep us on for iteration. Either way you're never locked in.
Questions, answered
Why is migration more than changing the model name?
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The errors announce themselves; the silent changes don't. A model that switches thinking on by default will quietly truncate outputs that used to fit. A new tokenizer shifts every token-denominated assumption you have. Those are the ones that reach production.
How do you avoid quality regressions?
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We build or extend your eval set before touching anything, then run the candidate in shadow mode against real traffic. The decision comes from your distribution, not a benchmark table.
Can you migrate us off a provider entirely?
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Yes. We usually start by putting your model calls behind an interface, which makes this migration and every future one a config change rather than a project. That abstraction is the actual deliverable.
How long does a migration take?
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A single well-abstracted service is often days. A codebase with vendor SDK calls scattered through it takes longer, and most of that time goes into the abstraction, which pays for itself the next time a model ships.
Let's scope your build.
Tell us what you're trying to ship. We'll tell you honestly whether AI is the right tool and what it would take.
Start the conversation