Skip to main content

NEW: Build Your AI Workforce. Deployed in your tools, backed by experts.

ROCKETFUEL

Accelerate Your Roadmap.
Ship in Weeks.

Deploy AI agents into Slack, Linear, and GitHub with enterprise-grade controls. Or bring in our engineers to build your product from scratch.

Trusted by teams shipping real products.
Featured in Apple Keynote.

Trusted by teams at

Collab.Land logo
Hume logo
NFL logo
Verizon logo
Yahoo Sports logo
Yup.io logo
KYVE logo

Pick Your Path to Shipping Faster

NEW AI WORKFORCE

Build Your Own AI Workforce

Deploy AI agents that handle engineering tasks end to end, backed by senior engineers who step in when they're needed most. Scale your output without scaling your team.

PRODUCT STUDIO

Hire a Full-Stack Product Team

Our engineers own your product delivery from architecture through launch, amplified by AI at every step. The team you'd build if you had six months and a perfect hiring pipeline.

Not sure which fits? Start a conversation and we'll figure it out together.

HOW IT WORKS

Your AI workforce in 4 steps

CHOOSE YOUR DEPLOYMENT

On-premises for enterprise security. Managed cloud for speed.

SELECT YOUR TEAM

Code reviewers. DevOps. QA. PM. Pick the roles you need.

CONNECT YOUR TOOLS

We integrate with Slack, Linear, GitHub, Jira. Wherever your team already works.

LAUNCH & ITERATE

We deploy, train on your workflow, and keep improving. You get better every week.

YOUR NEW TEAM

AI agents trained for real work

CODE REVIEWER

PR reviews in minutes. Context-aware bug detection.

DEVOPS ENGINEER

CI/CD pipelines. Infrastructure as code. Deployment automation.

QA ANALYST

Test coverage. Regression prevention. Quality gates.

PROJECT MANAGER

Ticket triage. Sprint planning. Status updates.

TECHNICAL WRITER

Documentation. API references. Changelogs.

SECURITY AUDITOR

Vulnerability scanning. Compliance checks.

TRUST & CONTROLS

Enterprise Controls That Scale

AI is powerful. But power without controls is liability. Every RocketFuel AI workforce comes with the operational infrastructure serious teams require.

AUDIT LOGS

Every agent action logged, traceable, accountable. Know exactly what your AI team did, when, and why.

MISSION CONTROL DASHBOARD

Real-time visibility into your AI workforce. Task status. Performance metrics. Activity feeds.

APPROVAL GATES

Define which tasks need human sign-off. Start conservative. Expand autonomy as trust builds.

BRANCH PROTECTIONS

Agents work in isolated branches. Nothing merges without your review workflow.

COST CONTROLS

Budget caps and usage alerts. No surprise compute bills.

ESCALATION PATHS

When agents get stuck, they escalate to named engineers. Not a ticket queue. Real humans, real response times.

MISSION CONTROL
Live

3

Active

2

Reviewing

47

Completed

1

Escalated

This week: 43 PRs merged
12 deploys 99.2% uptime

Activity Feed

14:32 Code Reviewer approved PR #312 — merged to main
14:28 DevOps Agent deployed v2.4.1 to staging
14:15 QA Analyst caught auth regression — escalated to human
14:02 Project Manager triaged 6 tickets from backlog

Pending Approvals (2)

Production deploy — v2.4.1 awaiting CTO
DB migration — add_user_prefs awaiting review

YOUR A-TEAM

Expert Oversight, Built In

You don't need a bigger engineering team. You need a better system. That's an AI workforce with humans in the loop: engineers who know your codebase, respond to escalations, and continuously train your agents on what works.

These aren't support staff. They're the same senior engineers who built token launch infrastructure for Collab.Land, shipped AR experiences for the NFL, and architected Web3 platforms for Hume. When they back your AI workforce, they bring that depth. When you need them to build something directly, they bring that too.

We train agents on your workflow. Not a one-time setup. Not a rigid framework. We iterate with you, tuning agent behavior, expanding capabilities, refining outputs, until your AI team operates exactly how you need.

Named A-Team engineer on every account
48-hour maximum escalation response
Continuous tuning and workflow training
Full-stack product capability when you need it

The People Behind Your AI Team

Victor

Victor

Architecture

Harry

Harry

Infrastructure

Dimitris

Dimitris

3D / Immersive

Chris

Chris

Product Ops

Alex

Alex

Web3 Full-Stack

George

George

AI Full-Stack

Mickael

Mickael

Quality Assurance

PRODUCT STUDIO

Beyond AI agents

Before we built AI workforces, we built products. Since 2019, our team has shipped token launch platforms, 5G AR experiences, Web3 music ecosystems, and enterprise tools used by millions. That track record is what makes our AI agents credible. And it's still available to you directly.

Need something built from the ground up? Our engineers work embedded with your team, from architecture through launch. Same people. Same standards. Full ownership of delivery.

CUSTOM DEVELOPMENT

Full-stack product engineering. Web, mobile, and backend systems built for scale.

WEB3 & DEFI

Token infrastructure, smart contracts, NFT platforms, decentralized applications.

AR/VR & IMMERSIVE

Augmented reality experiences, 3D platforms, spatial computing applications.

ENTERPRISE PLATFORMS

Admin systems, data pipelines, internal tools, API architectures.

Many teams start with a product build, then transition to an AI workforce for ongoing development and maintenance.

PROVEN IN PRODUCTION

Shipped by our team

PRODUCT STUDIO Collab.Land

TOKEN LAUNCH INFRASTRUCTURE

Collab.Land needed to launch tokens to 2M+ wallets. We built the claim site, command center, and marketplace.

8M+ connected wallets. 80M+ Discord reach.

Case Study Coming Soon
PRODUCT STUDIO Hume

WEB3 MUSIC PLATFORM

Hume needed fans to shape metastar lives in real-time. We built The Spot, NFT drops, and integrated Admin CMS.

10+ successful drops. Featured on Base chain.

Case Study Coming Soon
PRODUCT STUDIO NFL + Verizon

5G AR EXPERIENCE

Verizon needed an AR fan experience for NFL 5G launch. We built the iOS/Android app with patented field tracking.

Featured in Apple 2020 Keynote.

Case Study Coming Soon

Questions You're Already Asking

Why not just use Devin / Factory / Cursor?

Those are great tools. We use some of them ourselves. But tools need deployment. They need to plug into YOUR Jira, YOUR Slack, YOUR GitHub workflows. Someone has to configure approval gates, watch output quality, and jump in when things go sideways. That's us. We're not the hammer, we're the construction crew.

How do you prevent bad code from shipping?

The same way any good engineering team does: - Agents work in branches, never main - PRs require review (human or senior agent) - Approval gates on high-risk operations - Audit logs on every action - Rollback capability on everything You set the boundaries. We enforce them.

What about security and IP?

We have options for every risk profile: - On-premises deployment. Your infrastructure, your data - Private model fine-tuning. Nothing leaves your environment - SOC 2-aligned practices - NDA and IP assignment standard in every engagement We will figure out the right setup on your first call.

What happens when an agent gets stuck?

It escalates. Not to a ticket queue. To your named A-Team engineer. Response time: 48 hours max, usually same-day. Critical issues get a direct line to your account lead. This is not fire-and-forget. It is a partnership.

Is this just a wrapper around ChatGPT?

No. We orchestrate multiple models, tools, and custom-trained agents to handle real engineering workflows. Think of it like comparing a Formula 1 team to an engine manufacturer. The engine matters, but the car, the driver, and the pit crew are what win races.

Can you just build the product for us?

Yes. Our Product Studio handles full-stack development, from architecture through launch. Web3 platforms, AR/VR experiences, enterprise systems, we've shipped all of them. Many clients start with a product build, then deploy an AI workforce for ongoing development. Others use both simultaneously. We'll figure out the right model on our first call.

How do the two modes work together?

Think of it as a spectrum. On one end, our engineers build your product directly. On the other, AI agents handle the day-to-day work with our engineers stepping in as needed. Most engagements land somewhere in between: we might build your MVP, then deploy an AI workforce to iterate on it. Or your in-house team leads development while our AI agents handle code review, testing, and documentation. We design the right mix based on what you're shipping and how your team works.

FROM CALL TO LAUNCH

We move fast

Here's what to expect:

Managed Cloud

Day 1 Discovery call. Understand your stack and goals
Day 2-4 Integration setup. Connect your tools and repositories
Day 5-8 Agent training. Tune to your codebase and workflow
Week 2 Soft launch. Agents working, you reviewing every output
Week 3 Ramp-up. Expand task scope, reduce review overhead
Week 4+ Full autonomy. Agents ship independently, you steer direction

Setup in 2 weeks. Full autonomy by week 4.

On-Premises / Enterprise

Week 1 Discovery + architecture review
Week 2-3 Infrastructure deployment + model setup
Week 4-6 Integration + workflow training + pilot team
Week 7-8 Soft launch with full audit logging
Month 3+ Full autonomy. Expand across teams and workflows

Live in 8 weeks. Full autonomy by month 3.

Product Studio

Week 1 Discovery + scope definition
Week 2-3 Architecture + technical design
Week 4-8 Build sprints. Bi-weekly demos
Week 8-10 QA, polish, launch preparation
Week 10-12 Launch + handoff + optional AI workforce deployment

MVP in 10-12 weeks. Production-ready with handoff.

HOW PRICING WORKS

Transparent, flexible, built for value

Whether you're deploying an AI workforce or engaging our Product Studio, pricing reflects the scope and complexity of your needs. No surprises. No hourly billing traps.

AI Workforce

BASE PLATFORM

Your AI team deployment. Roles, integrations, infrastructure.

Depends on: team size, deployment model, tools connected.

USAGE

Agent compute and model costs.

Scales with: volume of work, complexity of tasks.

A-TEAM SUPPORT

Human oversight, escalation response, workflow training.

Depends on: support tier, response time requirements.

Product Studio

SCOPE DEFINITION

Clear deliverables and milestones defined upfront.

No vague estimates. No moving targets.

FIXED TIMELINE

Budget and schedule agreed before work begins.

Depends on: project complexity, team size required.

NO SCOPE CREEP

Changes require mutual agreement and re-scoping.

Protects both sides from runaway costs.

Most teams see ROI within the first month. A typical AI Workforce engagement costs less than one senior hire and ships more than a full team.

Ready?

Whether you need an AI workforce to multiply your team's output or engineers to build your next product, we're ready.

Let's figure out the right model for you.