Large language models built for business — not benchmarks.

A generic model answers from general internet knowledge, not your product catalogue, support history, or house vocabulary, so it either gives generic answers or hallucinates specifics it was never trained on. Fine-tuning on your own data, such as tickets, contracts and product catalogues, gets you answers grounded in what your business actually knows, at the cost of more setup than calling a public API directly.

Support and CRM are common starting points, but the same underlying capability extends further: generating marketing content and ad creative from a brief, forecasting trends from historical text data, and turning internal documents into something your team can actually query instead of searching manually. Which use case matters first depends on where the repetitive, text-heavy work is costing your team the most time.

A well-crafted prompt against a general-purpose API can go a long way for simple use cases, and we are honest when that is genuinely enough; not every business needs a fine-tuned model. Fine-tuning earns its cost when the same generic prompt keeps missing your specific vocabulary, edge cases or compliance requirements, which is a sign the model needs your data, not just better instructions.

No, your data is scoped, encrypted, and never used to train a shared or public model. Whatever we fine-tune stays specific to your deployment.

Every model decision is logged with the inputs and criteria behind it, so outputs can be audited rather than trusted blindly, and high-stakes or customer-facing outputs typically route through human-in-the-loop approval thresholds you define before anything reaches a customer directly. That review layer is part of the deployment, not an afterthought bolted on if something goes wrong.

This is exactly why traceable reasoning and human-in-the-loop review exist for anything customer-facing or high-stakes; the goal is to catch a wrong answer before it reaches a customer, not to accept it as an acceptable failure rate. Where a model is deployed with full autonomy, the approval thresholds and escalation rules are agreed with you during scoping, not left ambiguous.

There is no fixed threshold, but the model needs enough of your own tickets, transcripts, contracts or catalogue data to actually learn your vocabulary and patterns rather than just echo the base model. If that data is thin or scattered across systems that do not talk to each other, the more honest first step is often consolidating and cleaning it before fine-tuning, not fine-tuning on what is on hand.

Yes, these models are built to plug into whatever CRM, helpdesk or internal knowledge systems you already run, reading and acting on that data rather than requiring you to migrate onto a new platform first.

That structure, a one-time build versus ongoing fine-tuning and support as your data grows, is scoped with you upfront rather than assumed, since it depends on how fast your product, tickets or catalogue actually change. That is part of the first conversation, not something decided after the build starts.