Your enterprise dedicated
AI development team.
Stop waiting six months to hire. Your enterprise dedicated AI development team launches in 10 days. Senior engineers, embedded delivery, and outcome ownership from sprint one.
What delayed AI hiring costs your product roadmap
Every quarter you spend recruiting senior AI engineers is a quarter your competitors ship product. The cost is not just salary. It is your roadmap, your release dates, and your competitive window.
- 3 to 6 months to hire one senior AI engineer. Interview cycles, offer rounds, and notice periods eat your timeline.
- Mismatched skills discovered after onboarding. A generalist developer in an ML-specialist role slows every sprint.
- Ramp-up time measured in months, not days. New hires need 8 to 12 weeks before they ship production work independently.
- Generic outsourcing firms deliver junior talent. Volume-first staffing firms prioritize margin over match quality.
- Team assembled and active in 10 business days. From signed agreement to first sprint in under two weeks.
- Matched by specialization, not availability. ML engineers, LLM specialists, MLOps leads matched to your exact stack.
- Senior floor: no junior engineers on your project. Every engineer on your team has a minimum 5 years of production AI experience.
- Outcome ownership, not hourly billing. Scoped deliverables, weekly demos, and sprint accountability built in.
From signed agreement to active sprint in 10 days
Most AI teams take 90 days to onboard. Yours takes 10. Every step is structured to get your engineers shipping production work as fast as possible.
Days 1 to 2
Team composition
Seniority, stack, and project fit matched against your requirements.
Days 3 to 4
Technical onboarding
Repo access, dev environment, architecture review, and continuous integration and delivery verification.
Days 5 to 7
Sprint 0: architecture and backlog
Architecture decisions locked, backlog scoped, velocity baseline set.
Days 8 to 10
Sprint 1: shipping begins
First pull requests merged, features in review, and a weekly demo already scheduled.
Days 1 to 2: Team Match
Your team match
94% match scoreLi Wei
ML Engineer · Senior · PyTorch, LLM, RAG
Priya Nair
LLM Engineer · Senior · Fine-tuning, RAG, APIs
Marcus Flynn
MLOps Lead · Senior · Kubernetes, MLflow, AWS
Days 3 to 4: Technical Onboarding
Setup progress
88% completeDays 5 to 7: Sprint 0
Architecture and backlog
24 stories scopedDays 8 to 10: Sprint 1 Active
Every AI engineering capability your team needs
Your dedicated AI development team is composed for your specific project. Mix and match from a bench of specialists across every layer of the AI stack.
ML and AI engineering
Train, fine-tune, and evaluate ML and large language models in production. Your team handles everything from model selection and training infrastructure to inference optimization and evaluation pipelines.
LLM integration
Connect enterprise systems to language models with production-grade application programming interface design, prompt engineering frameworks, and output evaluation.
MLOps and deployment
Continuous integration and delivery for ML models, containerized inference, model versioning, drift detection, and performance monitoring in production.
AI data engineering
Feature stores, ETL pipelines, streaming data infrastructure, and data quality frameworks purpose-built for ML systems.
Production APIs
FastAPI, gRPC, and agent orchestration layers that connect models to your product with production-grade reliability.
Engagement models
Three ways to work with your dedicated AI development team
Fixed-term
3 to 6 months. Defined scope, clear deliverables, one price.
Embedded
6 or more months. Your engineers inside your processes, long-term.
Sprint-based
4-week delivery sprints. Pause, resume, or scale at sprint boundaries.
All models: scoped before work starts. Line-by-line pricing. No commitment to receive a proposal.
Pricing is scoped to your project before work starts. See how these engagements are scoped and priced →
What our dedicated AI development team solutions deliver
Corporate Gear
B2B ecommerce platformWhat they do
B2B corporate branded merchandise, serving enterprise clients with complex ordering workflows and personalization requirements.
The challenge
The platform required fundamental user experience overhaul and conversion architecture work to support revenue growth, but lacked the technical team to run structured testing, personalization, and optimization at scale.
Solution delivered
A dedicated development team ran A/B and multivariate testing frameworks across the full platform. Personalized landing pages by segment, conversion-optimized navigation, accessibility improvements, and search engine optimization strategy were executed sprint by sprint.
- Multivariate testing at scale across all major conversion paths
- Tailored landing pages per customer segment and use case
- Security enhancements supporting enterprise-level transactions
Result
million in annual revenue scaled
through continuous team-driven platform optimization
A dedicated technical team executing sprint by sprint. Structured testing, personalization infrastructure, and data-driven optimization that turned platform potential into $120M in annual revenue for Corporate Gear.
AI-native engineering across the full stack
Your team brings production depth across every layer, from data ingestion to deployed inference endpoints. No generalist developers filling specialist roles.
Data layer
Model layer
API layer
Infra and MLOps
Not on this list? We match engineers to your stack, not the other way around.
Why enterprise teams choose Redefine over typical AI staffing partners
Most AI staffing engagements are built around cheap labor and hourly billing. Yours should be built around outcomes.
Capability
Typical AI staffing partner
Redefine
Onboarding speed
60 to 90 days
10 days
Seniority floor
Pricing model
Time and materials
Outcome-scoped
AI specialization
Generalist developers
Deep specialization
Sprint accountability
Status calls
Weekly demos
IP ownership
Negotiated
Client owns all
Common questions about dedicated AI development teams
Straight answers on assembly speed, stack coverage, engagement models, and IP, before you sign.
From signed agreement to active sprint in 10 business days. Team composition starts on day 1, technical onboarding runs through day 4, and Sprint 0 architecture is locked by day 7.
Your team covers the full AI stack: ML engineering with PyTorch, JAX, and Transformers; LLM fine-tuning and RAG systems; MLOps with Kubernetes, MLflow, and AWS EKS; and production application programming interface design with FastAPI and gRPC.
Three models are available: Fixed-term (3 to 6 months), Embedded (6 or more months), and Sprint-based (4-week delivery cycles). All models include weekly sprint demos and a named delivery lead who owns outcomes.
Outcome ownership means your team is accountable to deliverables, not hours logged. Each sprint has defined acceptance criteria. If a feature does not ship, the team carries it into the next sprint at no additional cost.
Yes. Sprint-based engagements allow team size adjustments at sprint boundaries with two weeks notice. Adding a specialist is handled without restarting the engagement.
All code, models, data pipelines, and documentation produced during the engagement belong to you. Full IP assignment is included in the standard agreement.
Teams operate under NDA from day one. Data handling follows SOC 2 Type II aligned practices. Security review is built into the Day 3 to 4 technical onboarding phase.
Is a custom dedicated AI development team right for your project?
Good fit
Chief Technology Officer or VP Engineering with a defined AI project backlog
You know what you need to build. You need the team to build it.
3 or more months of AI engineering work ahead
Enough scope to justify a dedicated team with real velocity.
Company scaling AI product capability faster than you can hire
You need AI specialists now, not in Q3 after a hiring cycle closes.
Enterprise team needing scalable AI engineering capacity
Multiple AI workstreams, complex stack requirements, or security-first environments.
Not the right fit
One-time AI experiment under two weeks
A dedicated team is built for sustained delivery, not a quick prototype.
No defined AI roadmap or project requirements yet
Start with an AI strategy engagement to define the brief first.
Needing a single freelance developer for one feature
A team has overhead that a single-task engagement does not justify.
Not sure which model fits your situation? Tell us your situation and we'll be straight with you.
Tell us about your AI project
Submit your brief and receive a scoped proposal within 3 business days. No retainer required to get started.
Response within 48 hours. A senior advisor reviews your brief before we call.
Scoped proposal in 3 days. Team composition, seniority mix, and pricing with no placeholders.
Sprint 1 within 1 week of sign-off. Senior engineers in your codebase in as few as 10 days.
Your team's time investment across a full build is typically 3 to 4 hours per week, one sprint review, async feedback on deliverables, and a final QA sign-off.
Submit your brief
Takes about 5 minutes. No sales call required to receive the proposal.
Call within 48 hours → scoped proposal in 3 days → Sprint 1 within 1 week of sign-off
Brief received
We'll review your situation and send a scoped proposal within 3 business days. Expect a call within 48 hours from an advisor who has read your brief, not a sales script.
Response within 48 hours
Proposal in 3 days
100+ projects delivered
You own all IP
Your AI project shouldn't wait six months for a hire
An enterprise dedicated AI development team built for your stack, your roadmap, and your timeline. Senior engineers ready in 10 days.