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Our engineers have built and shipped for the world's leading tech companies. Get proven talent embedded in your team — not contractors, not freelancers. A dedicated pod that stays for years.
Hiring takes 3–6 months
We assemble pods in 5 business days
35% annual engineer attrition
Our attrition rate is <5%
Generalist talent pools
AI-trained, domain-specific specialists
No IP or compliance coverage
ISO 27001 + SOC 2 Type II certified
We're built for AI transformation — obsessed with outcomes, not headcount.
Our engineers have firsthand experience fine-tuning models, shipping LLM applications, and deploying scalable GenAI systems — not just using ChatGPT prompts.
Every candidate passes multi-stage technical challenges, peer code reviews, and our proprietary AI scoring — so only the top 3% join the network.
Skip traditional recruiting delays. We match, scope, and deploy embedded pods in 5 business days — so you start shipping from day one, not month three.
Our engineers stay. That means accumulated domain knowledge, zero re-onboarding cycles, and consistent delivery velocity across your entire engagement. With <5% annual attrition, you get the same team for years.
Enterprise-grade security baked in. Every pod operates within your compliance perimeter with full NDA coverage, IP assignment, and audit-ready processes.
No 12-month lock-ins. Scale pods up or down as your roadmap evolves. You pay for execution, not bench time.
On a marketplace, you hire individuals who juggle multiple clients, disappear between projects, and leave when a better gig comes along. We're different. We build dedicated teams that stay.
Marketplaces: Individual freelancers with multiple clients
Wegile: Dedicated engineers assigned to one client, full-time
Marketplaces: 35%+ annual attrition (industry standard)
Wegile: <5% annual attrition. Same engineers, for years
Marketplaces: You manage vetting, interviews, contracts
Wegile: We vet, assemble, and manage — you just approve
Marketplaces: No collective memory, constant re-onboarding
Wegile: Your pod accumulates deep product context over time
Every role is filled by engineers with 5–15 years of domain expertise.
Full-stack, backend, frontend, and mobile specialists. Production-proven across modern frameworks, cloud infra, and AI toolchains.
Specialists in LLM fine-tuning, RAG architectures, agentic workflows, and production-grade ML pipelines — not just API wrappers.
UX researchers, product designers, and design system architects who craft experiences users love — across web, mobile, and AI interfaces.
Professionals who build the data foundations AI systems depend on — pipelines, warehouses, feature stores, and analytical models.
Automation engineers who ship quality — Selenium, Playwright, Cypress, load testing, and CI/CD integration across your full release pipeline.
Infrastructure experts who build the platforms your AI systems run on — Kubernetes, Terraform, multi-cloud architectures, and zero-downtime deployments.
We map your stack, team dynamics, roadmap, and SLAs.
AI matching + human review surfaces the perfect team. You approve every engineer.
Your pod joins Slack, accesses repos, and sets up environments. Security verified.
Your pod studies the codebase, plans first tickets, and completes compliance checks.
First PRs, first deploy to staging, daily standups live. Real velocity.
Most clients start with a single architect to validate fit. The ones who stay — and nearly all of them do — grow into dedicated pods. Here's the typical path.
A single dedicated architect embedded in your team. They learn your codebase, ship their first features, and prove the model works. Low commitment. High signal.
Solo Architect — $4,500/moOnce development velocity is proven, most clients add a Business Analyst Architect. Now you have development execution and roadmap planning running in parallel — nothing falls through the cracks.
Duo Pod — $8,500/moAs the product grows, the pod grows with it. Add mobile, QA, DevOps — whatever your roadmap demands. The same core architects stay. Your architectural context compounds.
Team Pod — $12,000/mo+Wegile assembled a 5-person AI pod within 5 business days. They were in our Slack, pushing code, and attending standups by day two. It felt like hiring full-time engineers without the 3-month lead time.
James Mitchell
CTO, ChargedUp
We needed LLM integration expertise fast — Wegile had engineers with production RAG experience deployed within two days. The quality bar is genuinely different from typical offshore vendors.
Sarah Reynolds
VP Engineering, Finova
We started with one architect to validate our approach. Six months later we had a full pod of three. The same lead architect has been with us the whole time — she knows our codebase better than we do. That continuity is what makes the pod model work.
David Chen
Head of Product, MediSync
Get a dedicated engineering pod — assembled in 5 business days, retained for years, and priced like a subscription.