AI Talent
Embedded pods built for execution, not advice.
Synq AI pods are small, specialized teams deployed directly inside your operations. They own the build, run the system, and continuously improve it - aligned to your business KPIs, not their billable hours.
Four roles. One operating pod.
Each pod is purpose-built for a specific engine or workflow. Small enough to move fast. Complete enough to own the outcome end-to-end - from design to production to improvement.
Workflow Lead
Owns: Business outcome accountabilityThe workflow lead is the senior operator embedded in your team. They translate your business goals into operating system design - defining success metrics, managing the pod, and owning the outcomes that get measured.
Day-to-day
- Maps your workflows and designs the automation architecture tied to your business metrics
- Defines KPIs and success criteria in alignment with your operations leadership
- Manages the AI pod sprint cadence and coordinates between roles
- Owns weekly performance reporting and drives the scale-out roadmap
AI Engineer
Owns: System architecture and reliabilityThe AI engineer builds and maintains the core automation - LLM chains, agent architectures, prompt systems, and evaluation pipelines. They own system performance and uptime.
Day-to-day
- Builds and maintains LLM chains, agentic pipelines, and prompt systems
- Designs evaluation frameworks and manages systematic prompt iteration
- Owns system uptime, performance monitoring, and incident response
- Runs evaluation cycles and implements model improvement based on QA findings
Integrations Engineer
Owns: Stack connectivity and data flowThe integrations engineer connects Synq AI systems to your existing tools - CRM, ATS, data warehouse, communication platforms, and any custom internal systems.
Day-to-day
- Integrates AI systems with your CRM, ATS, data warehouse, and communication tools
- Builds data pipelines, API connections, and webhook infrastructure
- Maintains sync reliability, data quality, and real-time data flow
- Handles custom integration requirements unique to your environment
Evaluator / QA Analyst
Owns: Output quality and systematic improvementThe evaluator reviews AI outputs, scores quality against defined rubrics, identifies failure patterns, and drives the improvement cycles that compound performance over time.
Day-to-day
- Reviews and scores AI outputs against defined quality rubrics every sprint
- Identifies failure modes, edge cases, and systematic error patterns
- Runs structured improvement cycles based on output quality data
- Builds evaluation datasets that compound model performance over time
How a pod engagement works.
Embed and audit
The pod embeds in your team, runs the workflow audit, maps your integration landscape, and identifies the first engine to deploy. Deliverable: prioritized deployment plan.
Design and build
Architecture approved, integrations built, human review workflows configured, system live in production. Deliverable: first engine running with KPI instrumentation.
Measure and optimize
KPIs instrumented, evaluation loops running, quality scores trending up, performance improving systematically. Deliverable: ROI report and optimization roadmap.
Scale or transfer
Expand coverage to new workflows, or transfer full system ownership to your internal team with complete documentation, runbooks, and evaluation datasets.
Is a pod right for you?
You have a clear workflow to automate but no in-house AI engineering capability to execute.
You have an AI strategy signed off but need a team to actually build and run it.
You're running high-volume manual workflows and need to move faster than your current headcount allows.
You want to pilot AI in one function before committing to a broader rollout.
You have a full in-house AI engineering team and only need tooling or infrastructure.
Deploy your first pod.
Tell us which workflow you want to automate. We'll scope the pod, define the KPIs, and design the first sprint within the working session.
Book a working session