Turn a clear AI use case into production.
Solertiq provides the FDE pod to scope, build, and ship one defined AI workflow inside your systems, then hand the code, runbooks, and operating context back to your team.
One use case. Production delivery. Clean handoff.
Solertiq is for leaders with a real AI use case, a named business outcome, and a delivery gap. A coordinated pod owns one production outcome, works inside your engineering system, and prepares the work for your team to take over.
Define the use case
Agree on the user, workflow, systems, success metric, data path, and internal owner who will inherit the work.
Run a focused production sprint
The pod removes one measurable blocker: a stuck integration, unreliable output, slow workflow, rising model cost, or customer-facing deployment gap.
Hand ownership to your team
Your team receives the code, runbooks, evidence, open risks, and operating context needed to run the system without ongoing dependence on Solertiq.
Different leaders buy the same thing for different reasons.
The common signal is simple: an AI initiative has a real owner, a real deadline, and no clear path from prototype to dependable production.
CEO, GM, or product sponsor
Protect the customer commitment, turn the AI mandate into shipped capability, and avoid another quarter of promising progress without adoption.
CTO, VP Engineering, or AI lead
Add senior execution without handing over architecture. The pod works in your repo, your controls, and your release process.
CFO, COO, or operating partner
Reduce the cost of delay: stalled revenue, unused platform spend, vendor overflow, and workflows waiting for senior AI execution.
No black box at the end.
The work is built to be taken over. Documentation is created during delivery, so your team inherits decisions, context, and next steps rather than a finished artifact they cannot safely change.
Review the delivery playbookArchitecture decision record
System boundaries, tradeoffs, data flows, model choices, and rejected alternatives.
Baseline and benchmark
Metric definition, test method, before-and-after results, and known limitations.
Production runbook
Deployment, monitoring, incident response, access, and routine maintenance procedures.
Ownership transfer backlog
Open risks, prioritized improvements, technical debt, and named customer owners.
Every sprint leaves evidence, not just activity.
Targets, tradeoffs, and remaining risks are written down while the work is happening. That gives executives confidence in the outcome and gives engineering a practical path to maintain it.
Baseline evidence
The starting metric, test method, dataset or workflow sample, and known limits are captured before optimization work begins.
Decision trail
Architecture choices, rejected alternatives, integration assumptions, and ownership boundaries are recorded for later review.
Transfer checklist
Runbooks, open risks, backlog items, monitoring expectations, and named customer owners are packaged before handoff.
Find the smallest move that creates progress.
Use the readiness grader or scope parser to decide whether you need an assessment, focused sprint, full pod, or no outside help.
Where is your AI initiative today?
Send the blocker. We will tell you what is worth doing.
A production start is proposed only after review confirms the business need, system access, required roles, and acceptance test. If a pod is not the right move, the recommendation should say that.