AI Implementation

Roughly eighty percent is ordinary software engineering: connecting to your systems, handling errors, building the review interface, monitoring, cost control, logging. The AI part is usually the smallest piece. That is exactly why prototypes are quick and production takes six to eight weeks.

From $12,000 for a single feature, with most projects between $12,000 and $30,000. Under the launch offer until 31 October, from $8,000. The two-week readiness audit at $4,500 gives you a firm fixed price, and that fee comes off the build if you proceed.

Because the prototype was never asked to handle the hard cases. Wrong answers, odd inputs, outages, volume, and the question of who is accountable when it gets something wrong. Building for those is a different discipline from building the demo, and most teams run out of confidence before they run out of budget.

Not unless you specifically choose an arrangement where it is, and we would flag that clearly. The standard configurations we build use commercial API terms where inputs are not used for training. Where you need a firmer guarantee than a contractual one, we can build with models running on infrastructure you control. We lay out the options and the cost difference during the audit.

Yes. Regional inference is available from the major providers, and self-hosted open-weight models are a realistic option for many use cases. Self-hosting adds roughly half again to the build cost. There are genuine trade-offs in capability too, and we will set them out honestly rather than steering you toward whichever is easier for us to build.

It depends on what the system does, who uses it and which markets you operate in, and it is a legal determination rather than an engineering one. Broadly, obligations cluster around disclosing that a user is dealing with an AI system, marking generated content, keeping records, and maintaining human oversight on consequential decisions. Your counsel makes the assessment. We build to whatever it concludes.

We evaluate against a test set drawn from your real cases and give you measured numbers rather than an impression, including where it fails. You decide whether that accuracy is good enough for the decision it is supporting, and where the human review threshold sits. Some use cases need ninety-nine percent. Some are useful at eighty with a person checking the rest.

It happens, and we build so it is survivable. Model choice is configurable rather than hard-wired, prompts live outside the deployed code, and the evaluation set means you can test a replacement against your own cases rather than trusting a benchmark. This is a real risk and we would rather name it than pretend it away.

Most of what we build takes repetitive work off people rather than replacing them, and the human review layer means someone stays accountable for the output. If your plan is headcount reduction, say so at the audit, because it changes what we build and how much oversight the system needs.

Two. Model and infrastructure usage, billed to you directly by the provider with no markup from us, estimated before the build. And optionally a support retainer from $900 a month covering monitoring, updates and tuning. Worth planning for: AI features need more attention after launch than conventional software, because the models change, your data changes, and real usage will surface cases nobody anticipated.

We start with whatever you have. Often the prototype's logic survives and everything around it is new. Sometimes the approach itself needs rethinking, and the audit will tell you which, in writing, before you commit to a build.