LIMS/ELN implementation and support specialist
Here's what AI can do for this role — and what still needs a human. Built straight from ENSEK's own job advert, running live on my_db.fisher_demo.lims_support_tickets. Not a slide about AI. The job, getting done.
Every line on the left is lifted from ENSEK's actual job ad. If a card lacks a harvested JD line, it is omitted. On the right is the AI doing it — with eligible cards running live against the warehouse and offline inspection clearly labelled in the workspace.
“ticket volume by system and priority — support load distribution for capacity planning and escalation prioritisation.”
How is support ticket volume distributed across lab systems and priorities — where is the heaviest support burden and where are the P1 hotspots?
bar chart“SLA breach rate by category and site — support quality heat map for service improvement targeting.”
Which category × site combinations have the worst SLA breach rates — the support quality heat map for service improvement targeting?
table“CSV impact incidents requiring requalification — unplanned requalification demand triggered by support incidents on validated systems.”
How many CSV-impact incidents require requalification — and which systems and sites are generating unplanned requalification demand?
deviation“root cause Pareto — top 3 root causes account for ~80% of P1/P2 tickets. The 5-Why analysis the Informatics Specialist uses to focus improvement effort.”
What are the top root causes for P1 and P2 tickets — the Pareto (80/20) analysis that focuses improvement effort on the highest-impact causes?
bar chart“validation cycle time for change requests — regulated change throughput signal. Identifies bottleneck systems in the GAMP 5 change control pipeline.”
What is the resolution time for Validation and Change ticket categories — the regulated change throughput signal?
tableThe honest other half. AI does the analysis; a person owns the decision — especially where regulation, fairness and accountability bite.
A plain-English question — the same one the job ad describes — is translated to SQL by the agentic backend.
Curated cards run server-side against MotherDuck when eligible. The workspace separately labels any local inspection path.
Runs against my_db.fisher_demo.lims_support_tickets. No synthetic numbers.
Each figure carries a falsifier — recomputed from the result set, not a stored number, so it can't quietly drift.
It's the role getting done: curated questions run live server-side against the warehouse; local inspection is labelled inside the workspace.
Open the live workspace →Provenance. Offline path: SYNTHESISED labelled 3,000-row LIMS/ELN support ticket dataset (ITS-169, seed 20260615). Schema mirrors my_db.fisher_demo.lims_support_tickets. GAMP 5 / EU Annex 11 CSV impact context. NOT real support data. Live path (dormant): server-side MotherDuck query against fisher_demo.lims_support_tickets.
It's Sorted — I took ENSEK's job ads and didn't write a report on what AI could do. I built it. Get the rest sorted →
I'm trained on this proof and the real ENSEK: the Ignition meter-to-cash platform (seven modules), the move under Centrica in 2024, 7M+ energy accounts migrated for suppliers like British Gas and Utility Warehouse, and the Ofgem framing. Ask me how the Data Analyst function changes shape, or which open roles map to which Ignition module.