Revex is an AI development company that builds production AI software — LLM integrations, AI agents, retrieval-augmented generation, AI-native web apps — and ships it in weeks. You get working software, not a research deck. You own the code, the prompts, and the model keys.
Every AI development engagement ships working software on a production stack. We build four categories of AI software, and most projects combine two or more.
Add AI capabilities to your existing product without rebuilding it. We integrate large language models into your current stack — summarization, classification, extraction, content generation, semantic search — wired into your database, your API, and your UI. The model does the work; your users see the result inside the product they already know.
Examples: auto-categorize support tickets, generate listing descriptions from structured data, summarize deal notes for a CRM dashboard, extract line items from uploaded invoices.
Agents that take actions, not just answer questions. We build autonomous and semi-autonomous AI agents with tool use, structured outputs, persistent memory, and explicit human-in-the-loop checkpoints. Agents read from your systems, make decisions, call tools, and report back — with every run logged and replayable.
Examples: ops agents that reconcile Stripe against QuickBooks, support agents that resolve tickets end-to-end, research agents that monitor competitors and post weekly digests to Slack.
Retrieval-augmented generation systems that answer questions from your data — not the internet's data. We build the full pipeline: document ingestion, chunking, embedding, vector storage, retrieval, and a generation layer that cites its sources.
Examples: internal policy Q&A for HR, product documentation copilot for support teams, investment memo search for fund managers.
Full-stack web applications where AI is the core product, not a feature bolted on. We build the frontend, the backend, the model layer, and the infra — designed from day one around AI workflows. Products where the AI is the value proposition.
Examples: AI business strategy platform, AI industrial documentation system, AI-powered pricing engines.
We are not stack-religious. We are stack-opinionated. For most AI development projects in 2025–2026, this is the fastest path to production-grade software:
Anthropic Claude — primary reasoning model. Tool use, structured outputs, large context windows. OpenAI when the use case demands it (embeddings, image generation, specific fine-tuned models). Model-agnostic architecture so you can swap providers without rewriting your app.
Supabase — Postgres you own, Row-Level Security for multi-tenant isolation, edge functions, auth, storage, and realtime. Vector storage for RAG via pgvector. One platform instead of five services duct-taped together.
Next.js on Vercel — server components, API routes, preview deploys, edge caching. Zero-config CI/CD. The frontend stack that ships fastest without sacrificing production quality.
Cursor + Lovable + Claude Code — our development workflow uses AI-native tools to write, review, and ship code 3–5x faster than traditional development. This is how we deliver in weeks what other agencies quote in months.
n8n — self-hosted workflow orchestration for agent triggers, scheduled pipelines, webhook handling, and cross-system data flows. Version-controlled, observable, yours to own.
No mystery. No six-month discovery phase. Here is how we work:
15-minute call. You tell us what you are building and why. We tell you if we are the right team, what the build looks like, and what it costs. If the fit is not there, we say so.
We map the system: data model, model selection, API design, tool layer, auth, and deployment target. You get a written architecture doc and a sprint-by-sprint build plan with deliverables and dates. No ambiguity about what ships when.
Heads-down development in 2-week sprints. Async updates in Slack or Linear. You see working software at the end of every sprint — deployed to a preview environment, not a slide deck. Unlimited revisions within sprint scope.
Production deploy, observability setup, error monitoring, and a clean handoff. You get the repo, the infra credentials, the system prompts, the eval set, and a runbook your team can follow.
Monthly support retainer for iteration, model upgrades, new features, and monitoring. $5,000/sprint. No contract lock-in — month to month.
We price on scope, not on hours. Transparent, fixed-fee engagements.
Every engagement includes full code ownership, model key ownership, and a production handoff. No lock-in.
We build with Cursor, Lovable, and Claude Code every day. Our development workflow is AI-native — we use the same models and tools we ship to clients. This is not a traditional dev shop that added an “AI services” page last quarter.
No offshore bench. No junior developers copy-pasting ChatGPT output. Senior engineers who understand model architecture, prompt engineering, tool-call patterns, and production deployment do the work. The person on your Slack channel is the person writing the code.
AI-native development tools let us move 3–5x faster than traditional agencies. A 4-week MVP with us is a 4-month engagement at a legacy shop. We commit to a date on day one and ship to it.
We have built and shipped AI products in production: AI industrial documentation platforms, AI business strategy platforms, real estate investment tools, fintech applications, and internal operations systems. Products with users, data, and revenue.
Code, prompts, model API keys, infra credentials, eval sets, runbooks. Handed over on day one of production. No vendor lock-in. Your next team can pick up exactly where we left off.
Common questions about AI development services, pricing, and how we work.
Most AI development projects with Revex cost between $8,500 and $60,000 depending on scope. A simple LLM integration into an existing product starts at $8,500. A full AI-native MVP runs $10,000–$25,000. Complex multi-agent systems or enterprise RAG builds go $40,000–$60,000+. We quote on a written spec, not a vague brief — book a call and we will scope it on the spot.
LLM integrations ship in 2–3 weeks. AI-native MVPs ship in 4 weeks. Single-workflow AI agents ship in 4–6 weeks. Multi-agent systems or complex RAG pipelines ship in 8–12 weeks. We commit to a delivery date on day one and build to it. Anyone quoting 6+ months for a v1 is either solving a much harder problem or running up the bill.
Primarily Anthropic Claude for reasoning and tool use, with OpenAI for embeddings and specialized tasks. We build on Supabase (Postgres + pgvector), Next.js, Vercel, n8n, and Railway. Our development workflow uses Cursor, Lovable, and Claude Code. We are model-agnostic in architecture — if your use case needs a specific provider, we wire it in.
Yes. You own the repo, the system prompts, the fine-tuned models (if applicable), the API keys, the infra, and the eval set. We hand everything over cleanly at launch. No lock-in, no proprietary wrapper you cannot leave.
Yes. About half our engagements are AI integrations into existing products — adding summarization, classification, search, or generation capabilities to a working application without rebuilding it. We adapt to your existing stack and database. The AI layer plugs in; it does not replace what works.
Three things. First, we are an AI-native team — we build with AI tools every day, not a traditional agency that added AI to the website. Second, senior engineers do the work, not offshore juniors. Third, we ship in weeks with fixed pricing and full code ownership. No six-month timelines, no hourly billing surprises, no vendor lock-in.
