PRODIGY AI PODS
AI EXECUTION, BUILT TO SHIP.
Small, senior teams built around one specific problem. Yours. Fixed scope. 60 to 90 days. A Chief AI/ML Architect stays embedded from discovery through delivery. You walk away with an asset worth more than the engagement cost.
.webp)
You already know where AI should help. Getting there is the problem.
Engineering knows where AI fits. Data knows where the leverage is. Sales has the use case. So do operations, finance, and customer experience.
The architecture has been sketched. The business case has been made. Leadership keeps asking when it turns into results.
But the roadmap is already full. The internal team is not in place. And the work still has not shipped.
That's exactly what we built this for.
One problem. One team.
One clean deliverable.
A Chief AI/ML Architect sits with your team, decides what’s worth funding, scopes the build, and writes the statement of work. The Pod builds against it. When it’s done, you own everything that shipped.
We learn your environment, talk to the people closest to the problem, evaluate the use case, and determine what is actually worth building. The output is a technical approach and delivery scope you can fund with confidence. Nothing gets committed until we know what we are building and why it matters.
A formal statement of what gets built, by whom, by when. The scoping document becomes the foundation of the engagement. Fixed scope. Fixed price where the scope supports it. No vague roadmap. No “we’ll figure it out as we go.”
The same architect stays involved from discovery through delivery. The team builds against the scope. Your stakeholders stay close to the work. Decisions happen quickly. Scope stays protected. In one quarter, you own a working system designed to create more value than it cost to build.
Problems we solve
real hours
Analysts and engineers spending days pulling, reshaping, and formatting data that should be automated. We build the pipeline and the output layer that replaces it.
you need
Contracts, invoices, claims, intake forms. Structured information locked in unstructured files. We build the extraction layer and plug it into your workflow.
you need
Customer emails, support tickets, inbound messages piling up faster than humans can read them. We build the classification, routing, and draft-response layer that gives the team hours back.
Account research, personalized outreach, lead triage, content production. The work your team would do more of if it didn't take all day. We build the layer that does it forthem, in their voice.
Engineers, analysts, ops — the tools they'd build for themselves if they had a quarter to spare. We build them to your data, your stack, your security posture.
Something promising that can't ship. Or shipped and nobody trusts it. We bring the architecture and evaluation discipline that gets it trusted, deployed, and owned.
The specifics change. The pattern doesn't.
Three things that actually matter.
In a typical consulting engagement, senior architects help sell the work, show up early, and then disappear after kickoff. That's where many AI projects start to drift. In an AI Pod, the Chief Architect stays in the work from discovery through delivery. Use case selection, architecture, team design, technical decisions, delivery reviews, and handoff all stay connected to senior judgment. That seat is included at no additional cost. Other firms charge for it at a premium. We build it into the model because architectural control is what protects the outcome.
We don't carry idle consultants between engagements and pass that cost through inflated bill rates.
Every Pod is built from scratch around the specific problem in front of it. Not whoever is available. Not “close enough.” Not a padded team built to increase hours.
The team is right-sized to the use case, and the savings stay with the client.
Traditional consulting often rewards time spent. AI Pods are structured around the deliverable.The goal is not to produce a roadmap, extend the engagement, or create dependency. The goal is to ship a working system the client owns. The deliverable is the deliverable. Not the timesheet.
THE CHIEF ARCHITECT
Every AI Pod is led by a Chief AI/ML Architect embedded across the full engagement. Not a name on a slide. Not a kickoff appearance. Not a final review.
A working architect. The Chief Architect pressure-tests the use case, scopes the engagement, designs the technical approach, vets the team, owns the architecture, and stays involved until the system ships. That is the structural difference.
What the Chief Architect brings:
- More than a decade of production engineering across startups, scale-ups, and enterprise environments.
- AI/ML systems architecture, agent orchestration, production ML, LLM integration, and applied automation.
- Cloud-native architecture across modern infrastructure environments.· Graduate-level depth in AI/ML, computer science, and technical systems thinking.
- A track record of shipping complex systems that hold up beyond the demo.
%201.avif)
What this means
- Clearer technical direction before work begins.·
- A tighter build plan around the highest-value use case.·
- Fewer handoffs between strategy, architecture, engineering, and delivery.
- Less strategy drift. More delivery discipline.
The result
- A working AI system your business owns, understands, and can build on
- Not a deck. Not a demo. Not another roadmap that waits on an already-full engineering team.
- We deliver a fixed-cost AI system built around a defined business outcome, with the architecture, documentation, and handoff needed to make it usable inside your business.
- When the work is complete, you own the system, the logic behind it, and the production learning that tells you what to improve, automate, or build next.
When the engagement ends,
so does the bill.
The standard is simple: when the work is done, the asset you own should be worth more than the cost to build it.
You are not paying for endless discovery, inflated bench overhead, ongoing platform access, or a dependency that only works while a vendor stays attached.
You are paying for a working system, the architecture behind it, the documentation to understand it, and the production insight to know what should come next.
NEED A TEAM TO OWN IT?
WE CAN BUILD THAT TOO
Some companies only need the system built. Others need the internal team to support it, extend it, and keep improving it after launch. That is where Prodigy’s technical recruiting engine becomes part of the value. We can help identify, vet, and place the engineers, data talent, ML specialists, product leaders, and technical operators needed to take ownership of the system once it is live. So the engagement does not end with a handoff. It can become the foundation for a stronger internal AI capability.
Have a problem you want fixed?