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.
Clarvia vs Turing, category by category
Each row states both positions and then what it implies, including the cases where Turing is the better answer.
AI consultancy with the factory already built. We design, build, and ship complete AI systems, from strategy through deployment. You get a working system, not a roster of engineers.
Global talent marketplace and staff augmentation platform. Turing connects companies with remote engineers and teams; the buyer retains responsibility for product direction and day-to-day delivery.
Core Offering
End-to-end AI product development. We own the entire lifecycle: research, design, architecture, build, deployment, and iteration. You describe the problem; we deliver a product that solves it.
Talent sourcing and staff augmentation. Turing matches companies with individual engineers or teams from their global talent pool. You manage the engineers, define the work, and own the product direction.
Delivery Model
Dedicated product team working toward a defined outcome. We operate in focused sprints with a clear roadmap. You get weekly demos and a shipped product, not timesheets.
Staff augmentation model. Turing provides engineers who embed into your existing team. You direct the work, run the standups, and manage delivery. They handle sourcing, vetting, and payroll.
AI Specialisation
AI is all we do. Every project involves machine learning, LLMs, computer vision, or AI-driven automation. Our entire team thinks in terms of AI product architecture from day one.
AI is one skill category among many. Turing sources engineers across the full stack: frontend, backend, DevOps, data science, and AI/ML. Their AI training data business is separate from their talent marketplace.
Speed to Working Product
The delivery schedule is set after scope, data, integrations, evaluation, and review gates are understood. Progress is assessed against named artifacts and acceptance criteria.
A talent marketplace can shorten sourcing, but time to a working product depends on the buyer's scope, leadership, onboarding, and delivery management.
Pricing Structure
Project-based pricing tied to deliverables and milestones. You pay for outcomes, not hours. Scope and cost are agreed before work starts.
Hourly or monthly rates per engineer, typically competitive with US market rates. Costs scale linearly with headcount. You also pay for management overhead on your side.
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
Common questions
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. The full scope of that model is set out under our AI development agency services.
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. For LLM, retrieval, and agent work specifically, that scope is described under generative AI consulting services. 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. The AI product cost guide lists the components to hold both models against.
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.
Is Turing a direct competitor to Clarvia?
Not really. Turing is a talent marketplace; they help you hire engineers. Clarvia is a product agency; we build the product for you. The overlap is that both serve companies wanting AI capability, but the delivery model is fundamentally different. With Turing, you manage the team. With Clarvia, we manage the delivery.
Can I use Turing to build an AI product without my own product team?
Technically yes, but it is difficult. Turing provides engineers, not product leadership. Without someone on your side defining requirements, managing sprints, and making architecture decisions, you will likely end up with hired talent but no clear direction. That is where an agency model like Clarvia adds value: we bring the product thinking alongside the engineering.
Is Clarvia more expensive than hiring through Turing?
Hourly rates do not compare the same deliverable. A fair comparison includes scope, internal product leadership, onboarding, management, rework, evaluation, and operating costs. Price both models against the same accepted outcome rather than assuming either one is cheaper.
What if I need ongoing AI development after the initial product is built?
Both models work for ongoing work. Clarvia offers retainer engagements for continuous product development. Turing offers long-term placements for ongoing engineering support. The right choice depends on whether you want ongoing product ownership (Clarvia) or ongoing engineering capacity you manage yourself (Turing).
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.