Your FDE team, deployed this week.
Start production work while you recruit. Solertiq embeds a senior, vendor-neutral AI engineering pod, ships against a measurable blocker, and transfers the code, runbooks, and operating context to your permanent team.
Immediate delivery without permanent dependency.
Solertiq is not a staffing marketplace. A coordinated pod owns a production outcome, works inside your engineering system, and creates the conditions for a clean transfer.
Define the blocker
Establish the production objective, baseline, acceptance test, delivery dependencies, and whether the work should start before the permanent hire arrives.
Run a focused production sprint
The pod removes one measurable blocker such as integration, evaluation, latency, cost, reliability, or enterprise workflow adoption.
Hand ownership to your team
Incoming hires pair with the pod and receive the code, architecture record, runbooks, evaluation evidence, backlog, and operating context.
The transfer is part of the product.
Documentation is produced during delivery, not reconstructed at the end. This package gives the permanent team enough context to own the system without depending on Solertiq.
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.
Start with one measurable blocker.
Targets are set after a baseline review. Solertiq does not guarantee performance before inspecting the system.
Latency and experience
Define the correct metric, establish a baseline, and redesign the serving path around a validated target.
Reliability and evaluation
Build evaluation evidence, guardrails, observability, and failure handling around real workflows.
Cost and unit economics
Measure cost per useful outcome, then test routing, caching, model, and workflow changes.
Find the first useful deployment move.
Use the infrastructure grader or scope parser before committing to a full pod.
What is the current status of your team's AI deployment?
No speculative reservations.
Assessment scheduling is open. A production start is proposed only after technical review confirms the blocker, customer dependencies, required roles, and acceptance test.