Orbit
A dedicated AI team, on your sprints, every week
Most companies treat AI like a project. Build a feature, hand it off, move on. But AI needs continuous attention. Models drift. New use cases surface. Data changes. Orbit gives you a persistent AI team on retainer that ships alongside your engineers, sprint after sprint.
2-3
Dedicated engineers
Every
Sprint ships AI
30d
Notice period
AI shouldn't be a one-off project
AI projects get delivered, then abandoned. Without a persistent team, models degrade, pipelines break, and nobody owns the fix.
Hiring senior AI engineers takes 6+ months. By the time you close the role, your competitors have already shipped three features.
Consulting engagements produce reports, not products. You end up with strategy decks and zero production code.
One-off AI projects create technical debt. Every new vendor brings a different architecture, different conventions, and different blind spots.
What you actually get
Dedicated AI Team
AI engineers, ML ops specialists, and a technical lead who work inside your codebase. They join your standups, use your tools, and commit to your repos.
Sprint-Aligned Delivery
No kickoff docs or SOWs for every feature. Your AI team pulls tickets from the backlog each sprint and ships production-ready work on a rolling basis.
Continuous Model Ops
Monitoring, drift detection, fallback strategies, and cost optimization come standard with every deployment. Your AI features stay reliable at scale.
Rapid Prototyping Pipeline
New AI ideas go from concept to working prototype in days. Test feasibility quickly, then promote the winners to production.
Impact Measurement
Every feature ships with success metrics. Monthly reports show exactly how AI is moving your product KPIs, from time saved to revenue influenced.
Knowledge Transfer
Documentation, runbooks, architecture decisions, and pair programming sessions. Your internal team gets better at AI every month we're embedded.
Productive in weeks, not months
We've done this enough times to have a repeatable onboarding playbook. No 3-month ramp-ups. No discovery phases that drag on forever. Your team starts shipping from week two.
Week 1
Discovery & Onboarding
We audit your stack, map AI opportunities to business goals, and build a prioritized roadmap. The team gets access to repos, tools, and channels.
Week 2-3
First Sprint Delivery
The team joins your sprint cadence and delivers the first AI feature. Early wins build momentum and prove the model works inside your workflow.
Month 2+
Continuous AI Velocity
AI features ship every sprint. The team refines the backlog, proposes new opportunities, and optimizes deployed models based on production data.
Ongoing
Scale & Transfer
As your internal AI capability grows, we hand over knowledge and operational ownership. Scale the team up or down based on your roadmap.
Who this is for
Product Companies
You have a working product and want to add AI features systematically, without hiring a full ML team from scratch.
Growth-Stage Startups
You're scaling fast and need AI capabilities now. But the 6-month hiring cycle for senior AI engineers isn't an option.
Enterprise Teams
Your engineering org is strong but lacks specialized AI depth. You need people who ship, not consultants who produce slide decks.
Platform Companies
You're building developer tools or B2B platforms and need AI features to stay competitive. Think smart search, recommendations, and automation.
The full AI stack, covered
One team handles everything from prototyping and model development to production ops and strategic planning. No handoffs between vendors. No gaps in coverage.
Core AI Development
Operations & Reliability
Strategy & Growth
Let's talk about your AI roadmap
30 minutes. We'll map your AI opportunities and show you what the first sprint looks like.