AI software development: senior teams that build AI into your product
Senior engineers who design, build and ship AI features inside the software your customers use: copilots, AI-native workflows and product features that hold up in production.
By the Ryz Labs team · Updated October 2026
Ryz Labs does AI software development through senior product engineering teams and AI pods that build AI features into the software your customers use: in-app copilots, AI-native workflows, natural-language search and document features that ship to production behind your own release process. 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 code.
This page is about AI-powered software development for product teams: the feature lives in your app, real users touch it every day, and it has to be fast, affordable per user and trustworthy. If you need to connect models to back-office systems such as a CRM or ERP, see AI integration services. For the full range of what our AI pods build, start at the AI development hub.
What we build
- In-app copilots. A side panel or command bar that knows the screen, the record and the user, answers questions about their data and takes actions through your existing APIs, under that user's permissions.
- AI-native workflows. Draft, review and approve flows where the model does the first pass (a reply, a report, a mapping, a summary) and the user edits, accepts or rejects it, with every decision captured.
- Document features. Upload a PDF, spreadsheet or scanned form and get structured fields back, validated against a schema and shown with the source location so users can check it.
- Natural-language search and query. Hybrid keyword and vector search over product data on pgvector or OpenSearch, and text-to-SQL against read-only views with row-level filters, so "show me overdue accounts in Texas" returns the right rows.
- Smart defaults and classification. Auto-tagging, routing, deduplication and field suggestions that run in the background and show up as suggestions, not silent changes.
- Per-tenant AI controls. Admin settings to turn features on or off, choose data retention, pick a model region and set usage limits, which enterprise customers ask for during procurement.
- Usage metering and plan gating. Token and request metering per tenant, tied to your plans and entitlements, so AI features can be priced, capped and reported.
- Feedback and evaluation loops. Thumbs up or down, edit distance on accepted drafts and acceptance rate per feature, wired into product analytics and into an offline eval set.
How an engagement works
- Talk. We look at your product, your users and the feature you want, and we agree how success is measured: acceptance rate, time saved per task, tickets deflected or conversion.
- Match. We propose a team with names and a price, usually a tech lead, a senior full-stack engineer who knows your front end and a senior AI engineer.
- Join. The team works in your repos, CI, design reviews and sprint rituals, and ships behind your feature flags like any other squad.
- Grow. Add a second feature, add product design or data people, or hand the feature and its eval suite to your own team.
In week 1, the team sets up local environments, reads the code paths the feature will touch and builds a first eval set from real (redacted) user inputs. By month 1, a working version is typically live for internal users behind a flag, with latency, cost and quality dashboards. By month 3, typical work is a staged rollout to customer cohorts, tuning from usage data and a second feature on the same foundation. Scope and access set the pace.
The stack our teams work in
| Layer | Tools we use | Notes |
|---|
| Front end | React, Next.js, TypeScript, Vercel AI SDK, server-sent events | Streaming responses, stop and retry controls, inline citations. |
| Application back end | Python (FastAPI, Django), Node.js (NestJS), Java, Go | AI calls go through your existing services and auth, not around them. |
| Models | Anthropic and OpenAI models, AWS Bedrock, Azure OpenAI | Chosen per task on your eval set; code stays portable between providers. |
| Retrieval and data | Postgres with pgvector, OpenSearch, Pinecone, Redis | Tenant and user filters applied at query time. |
| Evaluation and tracing | promptfoo, Langfuse, LangSmith, OpenTelemetry | Regression runs in CI on every prompt or model change. |
| Flags and analytics | LaunchDarkly, Unleash, PostHog, Amplitude | Per-tenant rollouts and acceptance metrics per feature. |
| Infrastructure | AWS, Azure, Terraform, Kubernetes, GitHub Actions | Everything in your accounts and pipelines. |
How we keep AI features reliable in a product
An AI feature in a product fails differently from an internal tool. Thousands of users see the output, many of them paying customers, and nobody is there to explain a bad answer. These are the failure modes a senior team designs around:
- Output that looks right but is wrong. We build an eval set before the feature ships, grade outputs automatically where the answer is checkable and with rubric-based LLM graders plus human spot checks where it is not, and block releases that regress.
- Latency that breaks the interaction. Every feature gets a latency budget. We stream text, run slow steps in the background, cache stable prompt prefixes and use smaller models for simple steps.
- Cost per user that kills the margin. We measure tokens per action and per active user from day one, cap usage by plan and route requests by difficulty. Product and finance see the number before the feature launches, not after.
- Data crossing tenants or users. Context assembly runs as the requesting user, retrieval filters by tenant and permission, and tests assert that one customer's data can never reach another's prompt.
- Prompt injection through user content. Uploaded files, emails and pasted text can carry instructions. Tools the model can call get least-privilege scopes, and writes that matter require the user to confirm.
- Model upgrades that change behavior. Model versions are pinned, and the full eval suite runs before any provider or version change reaches users.
- Features nobody uses. We instrument acceptance, edits and abandonment, and design the UI so users can see sources, correct the output and undo, which is what earns trust.
Our pods have shipped AI that customers and the public interact with directly, including an AI real-estate agent that works 24/7 and an AI voice platform that has made more than 1M outbound calls. 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.
- Feature pod: a tech lead, a senior full-stack engineer and a senior AI engineer. 1 × $15,000+ plus 2 × $10,000 to $15,000 = about $35,000 to $45,000+ per month.
- Product AI pod: a tech lead plus 4 senior engineers across front end, back end and AI, owning several features. 1 × $15,000+ plus 4 × $10,000 to $15,000 = about $55,000 to $75,000+ per month.
- Added capacity: 1 or 2 senior AI engineers working on your existing product squad at $10,000 to $15,000 each per month.
Project cost is team size × duration × monthly rate, so a feature pod for four months comes to about $140,000 to $180,000+. 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 AI feature end to end, from eval set to rollout, and your product squads are busy with the roadmap. Choose staff augmentation when your squad already owns the feature and needs AI experience on it: hire AI engineers or LLM engineers who work on your team, in your standups, reporting to your leads. Many product companies start with a pod for the first feature and keep one or two engineers on the squad afterward.
When Ryz isn't the right fit
If your software vendor's built-in assistant already covers the need, turn it on before building anything. If you want a proprietary AI platform to license, or engineers in European or Asian time zones for follow-the-sun coverage, other providers fit better. If you want hourly gig work through a self-serve marketplace, Ryz is not set up for that.
Related
FAQ
What is AI software development?
It is building AI capabilities into a software product: copilots, AI-native workflows, document understanding, natural-language search and smart defaults that users interact with directly. The work covers the model calls plus everything around them: UX, permissions, evals, latency, cost metering and rollout.
How is this different from AI integration?
AI integration connects models to the business systems you already run, such as a CRM, ERP or service desk, mostly for internal workflows. AI software development puts AI into the product your customers use, so user experience, per-tenant controls and cost per user matter as much as the model.
How much does AI software development cost?
Ryz engineers typically cost $7,000 to $15,000 per engineer per month, with leads from $15,000. A three-person feature 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 a team start building?
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, environments and sample data.
Can you build AI features into an existing product without a rewrite?
Yes, and that is the usual case. The team adds AI behind your existing services, auth and feature flags, ships to internal users first and rolls out by customer cohort, so the rest of the product keeps working as it does today.
Questions we didn't answer? Email info@ryzlabs.com.