From idea to a product people can actually use: we build your proof of concept, your MVP, and the version you launch.
Proof Pod, Production Pod, Scale Pod. Scoped in a call, in writing, before any work starts.
Meet the AI Pods →
Every engagement fits one of three shapes. We pick the one that matches your workflow, not the one that's trendiest this quarter.
Talks to your customers, vendors and team across chat, email, voice and CRM, without a human sitting on every thread.
Connects to every system of record you already run and gives a straight, cited answer instead of another search result.
Reads unstructured documents at scale, makes the call, and escalates only the exceptions that actually need a human.
AI Pods
Senior leadership stays on the workflow with you. Delivery capacity scales with its complexity. Every pod ships into your own cloud account and is measured against one business metric agreed before the first line of code.
To a live proof of concept
400 pod hours / month
Validate and launch one bounded, high-value AI workflow.
Best for: your first AI system, or rescuing a pilot that stalled between demo and deployment.
Your pod
To governed production
800 pod hours / month
Own one production workflow from discovery through adoption.
Best for: a proven use case that has to survive a security review and a real user base.
Your podEverything in a Proof Pod, plus:
Ongoing capacity, set to the roadmap
1,600 pod hours / month
Run two AI workstreams with shared senior leadership.
Best for: teams past their first workflow who don't want a new procurement cycle each time.
Your pod
Across the industry, most AI initiatives stall somewhere between demo and deployment, usually because nobody owned the path to production. We take that risk off your plate: if the agreed metric hasn't moved within the impact window, we keep building at no additional fee. No exceptions, no fine print.
Work with us →FAQ
A small, senior, cross-functional team that owns one AI workflow end to end — picking the workflow, baselining the business metric, building and evaluating the system, deploying it inside your own cloud, and proving the number moved. One accountable owner, no junior layer.
Staff augmentation sells you seats and hands you the management problem. A pod has one accountable senior lead, a metric agreed before any code, and its own evaluation and platform engineers. You set priorities; the pod owns delivery, quality gates and deployment.
Scoped in a call, in writing, before any work starts. In a 30-minute call we agree the workflow, the metric and the pod shape, and you get a written scope and commercials. Most first engagements are sized like one senior hire for a quarter.
A live proof of concept on your real data in 3 to 6 weeks, and governed production inside 90 days — integrated, evaluated, monitored, behind your SSO, with your team trained in the workflow.
Everything. Code, prompts, evaluation datasets and runbook, in your repository, written continuously rather than handed over in a final week. The system runs in your cloud account and there is no OrbitumAI platform to migrate off later.
Most vendors sell one motion. We give growing companies two, because the risk profile of your first AI workflow isn't the same as your fifth.
One workflow. We agree the scope and the price in a short conversation, then it's locked in before we write a line of code. If the metric hasn't moved by the impact window, we keep optimizing at no additional fee.
Most companies that adopt AI get stuck using it in just 1–3 functions. The Scale Pod is how we keep building the next one with you, without re-signing a new contract every time. Capacity is set once we understand the roadmap.
Scoped in a call, in writing, before any work starts. One workflow, one metric, one accountable owner.
Every engagement runs the same three phases. The difference is what "production" means for your business. We define the metric before we write a line of code.
Production systems from our delivery bench, across startups, growing companies and enterprise teams. Client names and locations are withheld; figures are drawn from delivery records and approved business cases.
Reps were losing roughly a third of the week to manual data entry, and the pipeline itself couldn't be trusted: stage, value and next steps were typed in from memory, hours after the conversation that actually changed them.
Every manufacturer sent paperwork in a different format, some as blurry scans or phone photos. The team spent 2+ hours a week manually reformatting it into company-branded templates, with an error rate around 10%, well above the 1% they needed.
The path from a customer demand signal to a consultant on the ground ran through 70+ manual handoffs across multiple teams, taking weeks where it should take days. Every day of delay was revenue left on the table.
Critical answers lived across SharePoint, Confluence, policies, tickets and tribal knowledge. Employees spent hours searching and still couldn't verify whether an answer was current or authorized.
Scaling expertise across 70,000+ diagnosis codes and 1,000+ agents was nearly impossible. High attrition and long training curves led to inconsistent claim handling, and unstructured agent notes broke both audits and automation.
Client names and locations stay withheld. Every number below comes from a delivery record and is stated exactly as measured. A verifiable outcome without a logo beats a logo without one.
