AI development company: senior AI pod teams that ship to production

Ryz Labs is an AI development company that delivers teams: dedicated AI pods of senior engineers who build agents, retrieval systems, LLM features and machine learning models in your cloud and repos, then ship them to production. Our engineers are the top 1% of the tens of thousands we have interviewed, they work on US business hours, and Fortune 500 engineering teams trust them with production systems.

What we build

Our AI development services cover the engineering between a model API and a measurable business result, where most pilots stall:

• Agents that take actions. Tool-calling workflows that read from and write to your systems, with step limits, human approval for risky actions and a trace of every call.
• Retrieval over your documents. Ingestion, chunking, hybrid search and reranking on pgvector, OpenSearch or Pinecone, with citations and per-user permission filters.
• LLM features in applications you already run. Summaries, drafting, classification and structured extraction wired into existing screens, APIs and batch jobs.
• Predictive models. Gradient-boosted and deep learning models for fraud, risk, churn and forecasting, with feature pipelines and drift monitoring.
• Chat and voice assistants. Support and internal help assistants that hand off to a person when they should, and voice agents on telephony.
• Evaluation harnesses. Labeled test sets, automated graders and regression runs in CI, so every prompt or model change is measured before it ships.
• The platform underneath. Model gateways, secrets handling, cost tracking, logging and deployment pipelines on AWS or Azure.

AI services we offer

Each service has its own page with the specific components, stack and failure modes.

• AI consulting: scoping a use case, ending in a build plan the same engineers carry out.
• AI agent development: multi-step agents that call tools and APIs under guardrails.
• Generative AI development: drafting, summarization, extraction and multimodal features.
• AI integration: connecting models to your CRM, ERP, ticketing and data platforms.
• Machine learning development: classical and deep learning models trained on your data.
• LLM development: applications and pipelines built around large language models, including open-weight models you host.
• OpenAI integration: adding OpenAI models to existing products, directly or through Azure OpenAI.
• AI chatbot development: support and internal assistants with retrieval and human handoff.
• RAG development: retrieval-augmented generation that cites sources and respects access controls.
• LLM fine-tuning: adapting models to your formats, tone or domain when prompting is not enough.
• MCP server development: Model Context Protocol servers that expose internal tools and data to agents.
• Computer vision development: detection, classification and document image processing.
• NLP development: entity extraction, classification and search over text.
• AI voice agent development: phone agents built on speech-to-text, LLMs and text-to-speech.
• AI legacy modernization: LLM-assisted code migration with human review.
• MLOps: training pipelines, model registries, deployment and monitoring.

How to choose the right AI service

Start from the output you need, not the technology. If the answer is a number or a probability learned from historical records, such as a fraud score or a demand forecast, that is machine learning. If the answer comes from your documents, start with retrieval. If the system must do something, such as file a ticket, update a record or send a message, you need an agent with tool access and approvals. If you have a working application and want a model behind one feature, that is an integration project.

Two questions come up on almost every call. Should you build or buy? If a vendor tool covers the use case and your data can go there, buy it; build when the workflow is specific to you or the data cannot leave your cloud. Our build vs buy guide goes deeper. Should you fine-tune? Usually not first. Prompting plus retrieval solves most problems, and fine-tuning helps with format, tone and latency once you have evals that show the gap.

How an engagement works

1. Talk. A scoping call about the use case, your data, where it lives, who owns the result and what success looks like in numbers.
2. Match. We propose a pod or individual engineers for your stack and the problem, with names, a plan and a price.
3. Join. The team works in your repos, CI and cloud accounts, attends your standups and demos progress weekly.
4. Grow. Add people or disciplines as scope expands, or hand the system over to your engineers with runbooks and tests.

In week 1, the team gets access, reads the data and writes the first evaluation questions with your domain experts. By month 1, there is usually a working slice running on real data in a non-production environment, with eval scores tracked from the first run. By month 3, typical work is hardening: monitoring, cost controls, security review, rollout to a first group of users and fixes driven by their feedback.

The stack our teams work in

How we get AI systems to production

Most AI projects fail in the same few places. A senior pod plans for each of them from the first week:

• The demo was tuned on 20 friendly examples. We build an evaluation set from real inputs, including the ugly ones, before tuning anything, and report scores on every change.
• Nobody defined "good enough." We agree on a metric and a threshold with the business owner, such as extraction accuracy or the share of tickets resolved without escalation.
• Costs grow faster than usage. We log tokens per request, cache stable prompt prefixes, route easy requests to smaller models and set budget alerts.
• Untrusted text steers the model. Documents, emails and web pages can carry prompt injection. Tools get least-privilege scopes, and writes that matter need approval.
• Data access is an afterthought. Permission filters apply at retrieval time, PII is redacted before logging, and data stays in your cloud.
• The model changes under you. We pin model versions and rerun the eval suite before any upgrade.

This is how our pods have shipped production systems such as fraud detection for a global fleet company, which surfaced $5.94M in fraud confirmed by the client's own fraud team, and an AI driver-support agent that covers about 218,000 driver calls a year in three languages. See the case studies.

Team shapes and cost

Ryz engineers typically cost $7,000 to $15,000 per engineer per month: mid-level (comparable to Amazon L5) at $7,000 to $10,000, senior (comparable to Amazon L6) at $10,000 to $15,000, and leads from $15,000. Common shapes for AI development:

• Pilot pod: a tech lead plus 2 senior engineers. 1 × $15,000+ plus 2 × $10,000 to $15,000 = about $35,000 to $45,000+ per month.
• Production pod: about 7 senior engineers, including a tech lead, an ML engineer and backend engineers. 1 × $15,000+ plus 6 × $10,000 to $15,000 = about $75,000 to $105,000+ per month.
• Added capacity: 1 to 2 senior AI engineers joining your existing team at $10,000 to $15,000 each per month.

Project cost is team size × duration × monthly rate, so a pilot pod for three months comes to roughly $105,000 to $135,000+. Before you start, you get a scoped plan, a price and the names of the people who would do the work.

Dedicated team or staff augmentation?

Choose an AI pod team when you want one team to own an outcome, from data to deployment, and you do not have AI engineers to staff it. Read more about how forward-deployed engineers work alongside your people. Choose staff augmentation when you already have an AI roadmap and a lead, and need more hands: hire AI engineers or machine learning engineers who work on your team and report to your leads.

When Ryz isn't the right fit

If you want a proprietary AI platform to license, or a board-level transformation program run by a strategy firm, a platform vendor or a large consultancy will suit you better. If you need engineers in European or Asian time zones, or follow-the-sun coverage, a global network fits. If you want hourly gig work through a self-serve marketplace, Ryz is not set up for that.

Related

• Guide: partners to take an AI pilot to production
• Guide: AI pod providers
• RAG vs fine-tuning
• OpenAI vs Anthropic for enterprise
• AI engineer rates

FAQ

What does an AI development company actually deliver?

At Ryz, a team that writes and ships the code: data pipelines, retrieval, model calls, agents, evals, monitoring and deployment, all in your repos and cloud. You own everything the team builds, and you can keep the team or take over the system.

How much do AI development services cost?

Ryz engineers typically cost $7,000 to $15,000 per engineer per month, depending on seniority, with leads from $15,000. A three-person pilot pod runs about $35,000 to $45,000+ per month, and project cost is team size × duration × monthly rate. Quotes are scoped per team.

How fast can an AI team start?

After the scoping call we propose a team with names. Most of the timeline after that depends on scope and on how quickly your side can grant access to repos, cloud accounts and data.

Which models do your teams use?

Our teams work with Anthropic and OpenAI models, directly or through AWS Bedrock and Azure OpenAI, plus open-weight models when you need to host them. We pick per task using your eval set and keep the code portable between providers.

Will our data leave our environment?

The team builds in your cloud accounts with your security controls. Which model endpoints receive data is your decision, and private options such as Bedrock or Azure OpenAI in your own tenancy are common choices.

Questions we didn't answer? Email info@ryzlabs.com.