Custom AI Solution Development

A custom AI solution is scoped to one specific business problem — one model, one integration point, one measurable outcome. A platform build spans multiple systems and use cases across the organisation. Most businesses need the former far more often than the latter.

Most engagements range from $60,000 to $180,000 depending on data complexity and integration surface — materially less than an enterprise platform build because the scope is deliberately narrow.

Most point solutions deploy in 8–12 weeks from kickoff to production, including data assessment, prototyping, and integration into the target system.

We flag scope changes during the feasibility sprint before committing to a fixed-scope build. If the problem genuinely warrants a broader platform, we say so rather than force-fitting it into a point solution.

No. The data assessment step identifies what's available for this specific problem and what needs light remediation — a full enterprise data cleanup is not a prerequisite for a scoped build.

You do. The model, code, documentation, and any data pipelines built for the solution are fully client-owned upon final payment.

Off-the-shelf tools are built for the average case across many customers. A custom solution is trained and tuned on your own data and business logic, which matters most on exactly the edge cases generic tools miss.

Yes. Several point solutions we've delivered became the first phase of a broader roadmap once the business case was proven — we can support that transition through our AI consulting engagement when you're ready.

It depends on the problem — classical ML point solutions run on Python, TensorFlow, and PyTorch; generative or reasoning-heavy solutions build on OpenAI or Anthropic Claude models. We choose per use case rather than standardising on one stack regardless of fit.

The same access-control and audit-logging standard applies regardless of project size — scoped data access, encryption in transit and at rest, and no broader system access than the specific solution requires.

Yes. Point solutions can be deployed inside your existing cloud account, VPC, or on-premise environment where required by data residency or compliance policy — this is scoped during discovery, not assumed.

We offer a post-launch monitoring period to catch real-world edge cases the initial build didn't anticipate, plus an optional ongoing maintenance retainer if the solution needs periodic retraining or tuning.

Yes — this engagement type is often the best fit for startups specifically, since it solves one high-value problem without the overhead of a full platform. Every engagement starts under mutual NDA regardless of company size.