What pushes teams to consider an alternative to Turing
Honest reasons teams shopping in this category move on. None of these mean Turing does bad work, just that the fit is wrong for the project.
You need an AI product, not engineering capacity
Turing offers a talent-marketplace model. That can fit when you have product leadership, an AI delivery playbook, and the management bandwidth to direct the team. A delivery partner is a different fit when you need one party accountable for a scoped outcome.
You do not have an internal AI delivery playbook
AI products fail differently from traditional software. Evaluation harnesses, prompt and retrieval architecture, model fallbacks, drift monitoring, latency budgets, human-in-the-loop patterns. Hiring strong engineers does not solve this if no one on the team has shipped production AI before. You need a partner who brings the playbook with them.
You want outcome accountability, not timesheets
In a marketplace model, the engineers do their job and the product outcome is your problem. For an AI feature where the architecture, evaluation, and deployment cadence are the hard parts, ownership of the outcome should sit with the team building it. Productized delivery puts outcomes on us, not on your management overhead.
You want predictable scope, not open-ended hourly billing
Marketplace billing scales linearly with headcount and time. For a focused AI build, a project engagement with named deliverables, a defined timeline, and exit ramps gives you control over cost and outcomes that hourly engagements do not.
How Clarvia approaches AI product delivery
Not presented as universally better than Turing. The comparison focuses on delivery shape, named artifacts, operating controls, and the evidence a buyer should verify.
We ship products, not engineers
Discovery Sprint, Build Sprint, Automation Rollout, Audit and Hardening. Each engagement has a defined start, a defined end, and a measurable thing that exists at the close of it. You hire us once for an outcome, not on rolling weekly timesheets.
AI-specific delivery, by default
The delivery model is focused on AI work. Evaluation methodology, model and provider selection, prompt or retrieval architecture, monitoring, and human review are included where the scope requires them.
Senior team, end-to-end ownership
A small senior team that owns design, engineering, and delivery together. No layered handoffs between strategy, design, and engineering. No analyst-led discovery followed by a different team building.
Transparent partnership
Weekly demos, a shared backlog, a published delivery cadence, and acceptance criteria written down before the work starts. You always know what is shipping next and why.
When Turing is still the right choice
You have strong product and technical leadership and just need to add vetted engineering capacity quickly
Your work is general software engineering with AI as one feature, not the core of the product
You prefer per-engineer hourly or monthly billing rather than a project engagement
You need engineers across many disciplines (frontend, backend, mobile, data) and AI is one of several
You also need AI training data or annotation services for training your own models
Clarvia vs Turing
If you want a fair, category-by-category comparison covering offering, delivery model, pricing structure, speed, and AI specialisation, read the full comparison page.
Common questions
How is this different from your Clarvia vs Turing comparison page?
The /compare/clarvia-dev-vs-turing page is a side-by-side covering offering, delivery model, pricing structure, and AI specialisation. This page is for teams who have already considered Turing and want to understand what choosing a productized AI agency instead actually looks like.
Is this just staff augmentation with AI branding?
No. This is a project-delivery model rather than per-engineer staffing. Each engagement is scoped against deliverables, evidence, decision gates, and handover responsibilities.
What if we already have engineers and just need AI expertise?
Our Build Sprint and Audit and Hardening engagements work well alongside an existing team. We lead the AI delivery patterns and your engineers embed alongside us so the playbook transfers. After the engagement your team can carry it forward.
Are you more expensive than hiring through Turing per hour?
We do not bill hourly. The models price different responsibilities, so a per-hour conclusion is not reliable. Compare the same scope, internal management effort, acceptance evidence, rework, and operating requirements.
Do you provide AI training data or annotation services?
No. Turing has a separate business line for training data services that we do not compete with. If your need is data labelling at scale for model training, Turing is set up for that and we are not.
Get a working AI product, not a hiring funnel.
Book a free 15-minute call. We will scope what shipping your AI feature actually looks like and tell you honestly whether productized delivery or a marketplace model fits your situation better.