Pipeline tasks, platform messages and tickets
Team business hours, target by process and severity, send-backs apart
Queue per analyst, time per pass and % on time
Operations TV screen, alerts and AI agent
What it solves
- The leader sees each analyst's queue right now, with what came in today, what is left over and what is overdue.
- Time is measured in the team's business hours, with the right target for each process type and severity.
- Send-backs and reopenings show up as rework, separate from execution time.
Business questions it answers
Ask your agent these questions once the case is live.
What you need
- The pipeline or support tool connected to Nekt: Zeev, Pipefy, Zendesk, ClickUp or the platform database
- The team list with each person's role and period, and the deadline targets per process and severity
- The team calendar: working hours, local holidays and special days
What you get
- A queue table per analyst, refreshed throughout the day
- A table of passes with business time, deadline and SLA status
- Median, p90 and % on time by stage, severity and week
- Deadline alerts and an AI agent that answers about the queue
Where to activate
Queue per analyst refreshed every 2 hours, sorted by team order, with today, carried over and overdue.
Claim or ticket without a reply close to the target, sent to the assignee and the leader before it breaches.
Answers who holds the queue, what is overdue and how long each stage takes, with business hours and rework already applied.
The tool already records tasks and messages. The work is keeping the team list and targets as tables, and computing business hours day by day. The detailed view has the business rules, the pitfalls and the acceptance test. Or send everything straight to your agent with the Nekt MCP.
The pipeline tool stores each task with start, end and assignee, but it does not say who is overloaded now or how many business hours each stage took. Whoever subtracts dates in UTC counts nights, weekends and holidays as delay; whoever adds the sent-back task punishes the analyst for another team's mistake. In claims and support the clock is the conversation, and without separating the customer's message from the operations reply there is no response SLA.
How it is usually done, and the rule
Each row is a mistake that already happened in practice.
Keep the list of members by the tool's user id, with team, role and start and end dates. The queue starts from this list and shows people without tasks at zero. Shared inboxes (the team's scheduling inbox, for example) appear apart from people. Never derive the team only from who showed up in the tasks.
Consider only active tasks. Started today (date in America/Sao_Paulo) is today's queue; started earlier is carried over; with the tool's expected end date less than or equal to now is overdue. Count distinct processes, not rows: a task with two assignees appears twice in the source. Separate main tasks from subtasks.
Walk through each day between start and end and add only the part inside working hours (for example, 9am to 6pm, or 8am to 6pm in customer service), skipping weekends, national, state and municipal holidays and company-wide vacations, with half days where they exist. Moving only the start and end into business hours and subtracting still counts the nights and the weekend in between.
Zendesk and other tools provide business time and calendar time for wait, first reply and resolution. Pick one per metric and use the same in the target, the percentage and the average.
Consecutive tasks from the same team form a pass, opened by one of that team's initiating tasks. When the case goes to another team and comes back, a new pass opens. Inside a pass, each change of assignee opens a sub-pass, to measure who did each part. Flag passes with a review step (double check). A team task without an initiating task before it is orphaned and stays out of the time.
Order messages by process (claim, rent guarantee, ticket). An event starts at the customer's first message after an operations reply, and consecutive customer messages belong to the same event. The event ends at the first operations message after the start. No reply is a status of its own, not zero. Who counts as operations comes from the sender's id or role (claims analyst, brokerage admin), not from the name.
Keep targets in a table: by process (for example, claims 24 business hours and other processes 48) and by ticket severity (for example, critical 60 minutes, high 4 hours, medium 8, low 16). Change a target with a start date, so the past is not reclassified.
A task closed with the result of sending the request back leaves the stage time and enters the rework rate of the team that got it back. A reopened ticket counts as rework for the analyst who closed it. Show rework by analyst and by severity over the last 30 days.
Stage time has a long tail. Show median and 90th percentile per week, as well as the average. A stage's time considers only cases that went through it; a case that did not go through it does not count as zero.
Remove from the support queue internal development subtasks without a company, test cards and duplicates. Tickets without a company or partner filled in stay in the queue but become a metric of their own (% without company), because they hide volume per customer. When the partner comes in the field name as "[code] name", extract the code to join with the registry.
Deadline targets used in practice
Examples of targets real operations adopted. Each goes into the targets table with time basis and validity.
Definitions
Pitfalls that already happened
The team's email list lived in the code; new hires did not appear in the queue and people who left stayed at zero until someone edited the transformation.
Members table by user id, with start and end dates.
A pipeline task was replaced by another from July on, and the stage's history dropped to zero from one month to the next.
Task-to-stage mapping with validity, updated together with the workflow.
A dashboard only clamped start and end to business hours and subtracted; a claim opened Friday afternoon and answered early Monday looked like it took more than 60 hours.
Add business hours day by day on the team calendar.
The first-reply SLA used the team's first comment in ClickUp, which can be an internal note; the customer still had no answer.
Use the message sent to the customer; when only the proxy exists, say so on the dashboard.
Tasks started after 9pm fell into the next day's queue, and the expected deadline was compared with a now in another time zone.
Convert everything to America/Sao_Paulo before comparing with today and now.
Tasks sent back for a missing document entered the average time of whoever got the case back.
Send-backs out of the stage time and into the rework rate.
Acceptance test
The case is done when every item passes.
- Every active team member appears in the queue, including at zero tasks.
- For 5 randomly chosen tasks, the computed business time matches a manual count on the team calendar (difference below 0.1 hour).
- Today's + carried-over queue = distinct processes with an active task for the analyst in the tool, at the same moment.
- No task with a send-back result enters the stage's average time.
- Every response SLA event has a status: within SLA, outside SLA or no reply.
Agent package
Paste into Claude, ChatGPT or any client with the Nekt MCP connected.
SLA for operational pipelines - Time zone: America/Sao_Paulo for today, now and every date shown. - Team: use table <members_table> (user id, team, role, validity). An analyst without tasks appears at zero. - Queue: active tasks only. Today = started today; carried over = started earlier; overdue = expected deadline <= now. Count distinct processes. - Business time: hours inside working hours <start> to <end>, on business days, without national and local holidays, company-wide vacations, and with half days where they exist. - Response SLA: an event starts at the customer's first message after an operations reply and ends at the first operations reply. No reply is a status of its own. - Targets: claims <24> business hours, other processes <48>; tickets by severity according to <targets_table>. - A task with result "devolver solicitação" (send request back) and a reopened ticket are rework, never stage time. - For times, report median and p90 as well as the average, only among cases that went through the stage. - Internal dev subtasks, tests and duplicates are not in the backlog. Exceptions per client: - <client>: task <new> replaced <old> from <date>; both map to stage <stage>. - <client>: Ash Wednesday is a business day only from 2pm; municipal holiday on <date>.
Build the "SLA for operational pipelines" case in my Nekt workspace following the rules at https://academy.nekt.com/en/industrias/financeiros/sla-esteiras-operacionais.md. 1) Find the source tables and confirm the columns the rules use. 2) Create a transformation at the stated grain applying every rule. 3) Run the acceptance test and show me the result of each item. 4) Create the case's context document in the workspace, leaving my clients' exceptions open for me to fill in.
Variations
The same reasoning for stalled requests: stock per SDR and loan officer, average aging and follow-up overdue, on time or missing.
Real estate agency, correspondent or firm extracted from the ticket field, with volume, reopenings and % resolved on time per partner.
Add to the SLA the rate of reviews that found errors (double check) to see whether the fastest team is also the most accurate.