Wrennon isn’t wired to a single AI provider. We engineer your agent on the model that best suits your catalog, budget, and latency needs — and we can move you to a different one later without rebuilding anything.
No single model wins on every dimension. We match your workload to the model that's actually strongest for it — not the one that's easiest for us to default to.
Multi-step logic, layered return policies, and technical product questions need a model that reasons carefully instead of pattern-matching to a plausible-sounding answer.
A live chat widget during a flash sale needs sub-second responses at scale. Some models are engineered specifically for speed at high concurrency.
Routine, repetitive questions — order status, shipping windows — don't need frontier-level reasoning. A fraction of the per-token cost, at high volume, adds up fast.
A 100,000-SKU catalog or a lengthy policy document benefits from a model that can hold far more context in a single pass without losing track of it.
Serving customers across regions means a model that handles non-English phrasing, idioms, and tone shifts as well as it handles English.
Some teams can't send customer data to a third-party API at all. Open-weight models can be deployed inside your own VPC to meet that requirement.
Pick a provider for its strengths, or let us recommend one based on your catalog size, traffic, and budget.
This happens once during setup, and again anytime you want to switch.
Catalog size, conversation volume, latency expectations, and any compliance or data-residency constraints you already have.
Your agent is built, prompted, and evaluated against that specific model — not a generic wrapper that happens to call an API.
If a better-fit model becomes available, or your needs change, we re-engineer the agent on the new model — your data, prompts, and integrations carry over.
Yes. If you already know which provider you want — for cost, compliance, or preference — we build against exactly that model from day one.
Yes, including Llama, Mistral, and Qwen deployed on your own infrastructure or VPC. This is common for teams with strict data-residency requirements — see our security practices for details.
Your plan pricing doesn't change based on which model powers your agent. A model swap is scoped and quoted like any re-engineering work, but your chat allotment and features stay the same.
We weigh reasoning quality on your actual product data, response latency at your expected volume, and cost per resolved conversation — then recommend the model with the best balance for your specific case, not a one-size-fits-all default.
Locking a customer service agent to a single AI provider means inheriting its outages, price hikes, and blind spots.
AI EngineModel-agnostic doesn't mean no opinion. Here's the repeatable process behind the decision.
Share your constraints — budget, compliance, latency — and we'll recommend the model that fits.