BergLabs.

Managed staffing

We own the queue. You own the outcome.

Quinn staffs specialist pods. Charterr runs the queue. BergLabs tracks SLA, QA and outcomes without added management overhead.

Start a 4-week pilot See the pod model
Managed queueIntake · Owner · QA · Outcome
Managed queue · SLA 4hPod live
  1. TCK-3041Refund approvalCleared in 1h 50mCleared
  2. TCK-3042Address changeCleared in 40mCleared
  3. TCK-3043Chargeback evidenceCleared, sampled by reviewerQA
  4. TCK-3044VIP complaintEscalation owner on standbyEscalate
  • Owner Assigned
  • Reviewer QA 98%
  • Escalation Standby
  • Outcome 3/4 cleared

Intake → Owner → QA → OutcomeQuinn staffs · Charterr runs

Illustrative queue. Ticket IDs and timings are examples; SLA targets are agreed per engagement.
  • Who it is for

    Teams with a queue that needs people, quality and an SLA.

  • What you get

    A staffed pod, a governed queue and a weekly readout.

  • How it is priced

    Scoped to the agreed outcome and SLA, not to hours.

  • 99.2%SLA adherence
  • 98.2%QA pass rate
  • 1.8%Exception rate
  • 300%YoY growth

Representative figures across BergLabs-run operations, not a guarantee for a new queue. Targets are set at scoping.

What we run

Four kinds of work, one managed model.

We assemble people for operating queues, annotation, specialist review and AI-supported work. BergLabs manages the delivery and quality around the team.

  1. 01

    Operating queues

    Support, back-office and review queues, staffed and run to an SLA with an owner on every item.

    Discuss this service
    1. 01IntakeOwner assigned
    2. 02ReviewSampled
    3. 03DeliverWeekly readout
    1. 01Items are classified and routed to a named owner as they arrive.
    2. 02QA samples completed work; exceptions go to the escalation owner.
    3. 03Outcomes are reported weekly against the SLA.
  2. 02

    Annotation and review

    Multimodal labelling and evaluation with specialist review and quality evidence on every batch.

    Discuss this service
    1. 01PrepareCalibrated
    2. 02ReviewTwo gates
    3. 03DeliverWith lineage
    1. 01Guidelines and golden sets are calibrated before the first batch.
    2. 02Every batch passes two QA gates before it ships.
    3. 03Batches are delivered with lineage and QA reports.
  3. 03

    Specialist review

    Domain specialists for the decisions automation should not make alone, from KYC checks to policy calls.

    Discuss this service
    1. 01PrepareVetted
    2. 02ReviewEvidence attached
    3. 03DeliverRationale logged
    1. 01Quinn vets specialists for the domain and the rubric.
    2. 02Cases reach the specialist with the evidence attached.
    3. 03Each decision is logged with its rationale in BergFlow.
  4. 04

    AI-supported work

    People and BergAuto working the same queue, with the confidence score deciding who acts.

    Discuss this service
    1. 01PrepareScored
    2. 02ReviewTrust Gate
    3. 03DeliverAudited
    1. 01Repeatable steps are scored by BergAuto.
    2. 02Below the gate, a person decides.
    3. 03Every action is recorded in BergFlow.

How it runs

Three commitments behind every managed queue.

Written into the scope before the work begins, and reported against every week.

  1. 01Pod deployment

    Quinn assembles a pod sized to the volume: a lead, a QA reviewer, specialists and an escalation owner. The pod is calibrated and run in shadow before it owns the queue.

  2. 02SLA and QA ownership

    The pod lead owns the SLA. The QA reviewer owns the sample and the standard. Neither depends on your team chasing them.

  3. 03Weekly reporting

    Every week you get SLA adherence, QA pass rate, exceptions, volume and the decisions needed from you, on one page.

The pod model

Named roles on every pod.

Every role is a person with a name in the scope, not a seat in a vendor queue.

Pod leadQA reviewerSpecialists ×3Escalation owner

  • Pod lead

    Accountable for the SLA and the outcome

    Runs the queue day to day, owns the weekly readout and is your single point of contact.

  • QA reviewer

    Owns the quality standard

    Samples completed work, calibrates the team on golden sets and records the evidence.

  • Specialists ×3

    Do the work

    Vetted through Quinn for the domain, the language and the rubric; sized to the volume.

  • Escalation owner

    Takes the hard calls

    Decides what the pod should not decide alone, inside limits agreed with you.

Weekly readout

One page, every week, against the targets you agreed.

Week 06 readout · Queue CX-01Representative
99.2%SLA adherence
98.2%QA pass rate
1.8%Exception rate

What the readout covers

  • SLA adherence by item type, with every breach explained
  • QA pass rate from the week’s sample, and what failed
  • Exceptions: how many, which owner took them, how they closed
  • Volume, backlog and the staffing plan for next week
  • Decisions needed from you, named and dated

Representative figures across BergLabs-run operations. Your targets are agreed at scoping and reported against every week.

Questions

Questions about managed staffing

Accountability, timing, visibility and price.

Working with a pod

What does BergLabs manage?

The queue, the people working it and the quality around it. BergLabs staffs a pod, runs the work to an agreed SLA, samples quality and reports the outcome weekly.

Who is accountable for delivery?

A named pod lead owns the SLA and the outcome, a QA reviewer owns quality, and an escalation owner takes the decisions the pod should not make alone.

Do we lose visibility when you run the queue?

No. Every item lives on a governed record in BergFlow, with decisions, versions and evidence you can inspect at any time.

Starting and pricing

How quickly can a pod start?

A pilot starts within four weeks of scoping. Quinn typically staffs a team in about 72 hours once the scope is agreed.

How is managed staffing priced?

Pricing is scoped to the agreed outcome and SLA and set out in the proposal after discovery, not billed by the hour.

Start a 4-week pilot.

One queue, one pod, a weekly readout from week one. Then decide whether to scale.

Start a pilot Talk to BergLabs