A solution engineer who ships alongside your team.
ManyLayers solution engineers embed with your team for the rollout — building connectors, designing evals, implementing agent workflows, and transferring the patterns your team needs to operate independently.
Engagement model
Named engineer
Scope
Rollout to handover
Custom connectors
Included
Eval design
Included
Everything needed for a successful AI deployment.
Embedded Solution Engineers
A named ManyLayers solution engineer joins your team for the duration of the engagement. They attend your stand-ups, review architecture decisions, and are accountable for a successful rollout — not just a handover document.
Custom Connector Builds
Need a connector your stack requires that is not in the standard library? Solution engineers scope, build, and test custom connectors for proprietary data sources, internal APIs, or specialised document formats.
Eval Design & Benchmarking
Your team defines what good looks like; our engineers build the evaluation suite. We design task-specific benchmarks, wire them into the ELO leaderboard, and help interpret results to guide model and prompt decisions.
Workflow & Agent Implementation
Solution engineers design and implement your first agent workflows and knowledge base pipelines. We transfer methodology and patterns so your team can build subsequent workflows independently.
Ongoing Technical Support
After rollout, a dedicated support channel connects your team directly to engineers who know your deployment. Quarterly reviews assess performance, cost efficiency, and roadmap alignment.
Defined Engagement Outcomes
Every engagement begins with agreed success criteria: models serving, connectors live, evals passing, and team trained. Outcomes are documented and verified before the engagement closes.
From scoping to handover in three defined stages.
Discovery & scoping
We spend time with your engineering and AI leads to understand your use cases, existing infrastructure, data sources, and quality requirements. We produce a scoped plan with timelines and milestones.
Embedded implementation
A solution engineer joins your team. Gateway configuration, knowledge base connectors, agent workflows, and evaluation suites are built alongside your team — with working sessions and knowledge transfer throughout.
Verification & handover
Each milestone is verified against agreed success criteria before sign-off. Your team runs the final deployment with our engineer present. Documentation, runbooks, and eval baselines are handed over at close.
Teams that want the outcome, not just the software.
Embedded Engineering is designed for organisations making a significant AI investment who need technical expertise alongside them for the first rollout. Whether your team is new to LLMOps or experienced but time-constrained, the embedded model accelerates time-to-production and reduces deployment risk.
- Named engineer accountable for your rollout — not a ticket queue
- Custom connector builds for proprietary or specialised data sources
- Eval suite designed around your specific quality requirements
- Knowledge transfer structured so your team operates independently post-engagement
- Available as part of enterprise plans — contact us to scope an engagement
“The embedded engineer felt like a senior addition to our AI team for six weeks. They built our custom SharePoint connector, designed the eval suite, and by the end our engineers were running new workflows completely independently.”
VP of Technology — Professional Services Firm