Full disclosure: we're Ryz Labs, and we're one of the options on this page. So yes, we're biased. Here's what we'd actually tell a friend, including when you should hire someone else.
A stalled pilot rarely needs a better model. It needs engineers who will wire it into your real systems, data and controls, and stay until it runs. So the deciding question is: who will do that work inside your stack, and what have they already shipped?
Pilots stall for boring reasons. The data lives in a system nobody has wired up. Security has not approved the model provider. The person who built the demo moved on. Nobody owns the on-call rotation. None of that is solved by another strategy deck.
What gets a pilot live is a team with access to your cloud and repos, a clear owner on your side, and a short loop between building and showing. Ask every partner for systems they have actually put into production, with numbers, not pilots they have run.
This is the job our AI pod teams are built for. After a first conversation, we match a dedicated team scoped to your stack: typically about seven senior engineers, including a tech lead, an ML engineer and backend engineers. The pod embeds in your repos, CI and standups and shows progress in weekly demos. Our engineers build on what you already run (AWS, Azure, GitHub, Postgres, Anthropic and OpenAI models). When it's live, the team can grow, or hand the whole system over to your people.
Our pods have production results to point to. One built a fraud detection system for a global fleet company that has scored 244K+ invoices at under 30 seconds each and surfaced $5.94M in confirmed fraud, validated by the client's fraud team. Another built a marketing-compliance system for a global capital management firm: 8,000+ documents, review time cut from days to hours. Others built an AI driver-support agent that works in three languages, and an AI voice platform that has placed 1M+ outbound calls. Clients are not named; see our case studies.
Where we're not the right fit: if you need a proprietary AI platform, a strategy engagement, or hundreds of engineers at once, a larger firm fits better. We have fewer public reviews than the big names, and we don't publish prices up front.
Thoughtworks launched Agent/works in 2026: a governed runtime and control plane for running agents in production. It is an engineering-led consultancy with a long modernization record, including a lending-platform rebuild for an unnamed global financial institution that it says enabled $800M in new revenue.
Where it beats us: if your problem is operating many agents under one set of controls, a runtime is the right tool and we don't sell one. The catch: you are adopting its runtime. See Ryz Labs vs Thoughtworks.
Turing's "Build" offering starts with rapid prototyping to test feasibility and ROI, then moves to production engineering with KPIs set on day one. It reports anonymized results such as a 45% cut in underwriting timelines.
Where it beats us: model-level depth from its frontier-lab data business. The catch: enterprise build is its newer line, and team time zones vary.
Tribe AI's Map, Build and Activate phases cover the work after launch: workflow redesign and adoption. Its build phase uses forward-deployed engineers. Public cases include cutting due-diligence research for a global consulting firm from 7-10 days to under one day.
Where it beats us: the Activate phase. If people are not using what was built, that is its focus. The catch: a contractor-network model and a small scale.
EPAM says DIAL 3.0, its open-source orchestration platform, is aimed at the structural problems of running AI in production at scale. Financial services is its largest vertical.
Where it beats us: scale and a platform for many workloads. The catch: a large-firm engagement floor can be heavy for one stalled pilot. See Ryz Labs vs EPAM.
Globant markets its AI Pods as a way to get AI "to production at enterprise standards" in financial services. The pods are token-metered subscriptions of agents supervised by Globant experts.
Where it beats us: a public company with scale and a productized purchase. The catch: no dedicated named team in your systems, and the model launched in 2025.
If one pilot matters and it needs to be live this year: Ryz Labs. A senior pod embeds in your stack on New York hours, shows working software every week, and leaves you owning the system. Our teams have already done this in production, from 8,000+ compliance documents for a global capital management firm to $5.94M in confirmed fraud surfaced for a global fleet company.
If you are running many agents and need common controls: Thoughtworks, with Agent/works.
If the pilot worked but nobody uses it: Tribe AI.
If you need a platform for dozens of workloads at a large enterprise: EPAM.
Usually integration, data access, security approval or ownership, not model quality. A partner should name which one is blocking you in the first call.
Only if they can work inside your systems and stay to run it. Many pilots are built outside the production environment, which is why they stall.
Not for one system. For many agents with shared controls, a runtime can help. For the broader choice, see forward-deployed engineering partners.
Then security and model risk are usually the bottleneck. Our guide to AI engineering teams for financial institutions covers that buyer.
We checked pricing and services for every provider here in October 2026. This changes often, so double-check before you sign anything.