In your environment
The pod works where the work happens: your systems, your data, your meetings. Access, governance, and performance questions get solved in the room, not filed as tickets.
Transformation / Forward-Deployed Engineering
AI doesn't fail in the demo. It fails in deployment.
Forward-deployed engineers work inside your environment, on your data, your systems, and your constraints, until AI is running real work in production. Then they hand your team the keys.
Talk to deploymentWhy forward-deployed
Production is the point.
Enterprise AI rarely stalls for lack of models or ideas. It stalls in the last mile: data access, governance, integration, adoption. Forward-deployed engineering exists to close it.
Measured by go-lives
The engagement is accountable for workflows running in production. Working software is the milestone. Everything else is scaffolding.
Control ends up with you
Your engineers and admins work alongside the pod from day one. When it steps back, the capability stays with your team, along with every plugin, agent, and integration in your library.
The platform team, in person
The pod deploys the product it builds. There is no discovery phase to learn a vendor's stack, and the gaps your rollout uncovers get fixed in the platform itself.
What the pod builds
Into your stack, not a sandbox.
Custom plugins
The connectors and skills your workflows need, packaged, versioned, and governed in your library.
Custom agents
Super Agents and workflow agents built for roles nobody else has, tested against your real work.
Integrations
Deep integrations with your systems of record, including the ones without APIs.
Data engineering
Warehouse connections, semantic layers, and the cleanup that makes AI answers trustworthy.
Application development
Full-stack software around the platform: internal tools, customer-facing surfaces, and the glue between.
Voice deployments
Voice Agents configured on your scripts, integrated with your scheduling and CRM, governed like everything else.
How an engagement runs
Embed → Ship → Scale → Hand over.
Embed
Inside your environment
The pod sets up where the work happens: access, data, and governance sorted, and workshops with the teams who own the workflows, so what gets built is what the business actually runs on.
Ship
Production before scale
Start with your data, find what is worth building, and get it running real work in production. Value is proven on a live workflow, not projected on a slide.
Scale
Across workflows, not functions
What works in one team rolls out across the workflows it touches: hardened, governed, and measured in Command Center as it spreads.
Hand over
You take the controls
Your team runs the platform; the pod steps back. Everything built stays in your library, versioned and yours, with training run through Fig Academy along the way.