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Section 01Customers · Anonymized for trust

The work, in production
across operator-led businesses.

We anonymize every case study by default. Our customers are operators in regulated industries — hospitality, freight, capital, healthcare, energy. They don't need a logo on our site. They need calibration that holds. The studies below are written with their permission, the numbers verified by their own audit teams.

Production deployments · operator sectors · 6 featured studies below
Production deployments · operator industries · 6 featuredAll sectors · anonymized by default
  1. Operations

    Sports & Entertainment · Operator A

    A 47-venue group cut Monday operations meetings from four hours to thirty minutes.

    "The agents are right often enough that we now spend the meeting on the cases they escalated."

    — COO, Operator A · Engaged month 14
    Decisions per week
    14,200
    Operator override rate
    2.4%
    Time-to-deploy
    11 weeks
  2. Margin

    Specialty Logistics · Operator B

    A North American freight desk replaced two-thirds of its routing decisions with calibrated agents.

    "The system told us the night dispatcher was actually the best router in the building. We promoted her."

    — VP Operations, Operator B · Engaged month 9
    Routing decisions / day
    6,800
    Margin lift, sustained
    +11.4%
    Time-to-deploy
    9 weeks
  3. Leverage

    Multi-strategy Capital · Operator C

    A discretionary fund built an agentic research desk that authored 41% of intake memos by month six.

    "It does not replace the analyst. It removes the part of the analyst job nobody became an analyst to do."

    — Head of Research, Operator C · Engaged month 6
    Memos / month
    480
    Analyst hours returned
    2,100 / mo
    Time-to-deploy
    14 weeks
  4. Throughput

    Healthcare Infrastructure · Operator D

    A national imaging network compressed prior-authorization from days to hours, system-wide.

    "The clinicians said it the most clearly: the agent argues with the insurer so they do not have to."

    — CTO, Operator D · Engaged month 11
    Prior auths / week
    38,000
    Median latency
    2.4h
    Time-to-deploy
    13 weeks
  5. Quietude

    Outdoor Retail · Operator E

    A 320-store retailer used a swarm to rebuild its weekly assortment and markdown decisions.

    "Buying was the most political room in the company. Now it is one of the quietest."

    — Chief Merchant, Operator E · Engaged month 8
    Decisions / week
    92,000
    Markdown waste
    −19%
    Time-to-deploy
    10 weeks
  6. Resilience

    Energy & Grid · Operator F

    A regional generator handed peak-load dispatch to a calibrated swarm in fourteen weeks.

    "The board wanted to know the failure mode. The honest answer was: we no longer have a single point of failure."

    — Plant Director, Operator F · Engaged month 12
    Dispatch decisions / day
    11,400
    Cost-to-serve
    −8.6%
    Time-to-deploy
    14 weeks
Section 02 · Featured study · Operator A
Sports & Entertainment Group · 47 venues

"We stopped having
the same fight every Monday."

The brief

Operator A runs a 47-venue group across hospitality, live events, and food & beverage. Monday operations meetings averaged four hours and resolved the same nine recurring categories of issue every week — staffing, inventory, service-recovery, supplier disputes.

The installation

Eleven-week deployment. Three calibration engineers on the account. A swarm of nine specialist agents covering the recurring decision categories, with the operations team on the loop and explicit escalation thresholds.

What changed

The Monday meeting now starts with the agent log, not a pile of issues. The team reviews the 12 to 18 cases the swarm escalated, signs off on a handful of edge calls, and is out the door in thirty minutes. The agents handle the rest before the meeting starts.

Where we are now

Fourteen months in. Operator override rate has stabilized at 2.4%. The calibration team meets monthly. Operator A has begun rolling the same swarm pattern out to two adjacent businesses in the holding company.

Fig. A · Override curveMonths 1–14
M00M04M08M12M1420%10%5%2.4%TARGET BAND · 2.4%
Operator override rate, weekly, normalized against month-one baseline. The system reaches the calibration target in week 9 and holds it through month 14. Source: Operator A internal supervisor logs.
Section 03 · Across the practice

Production installations,
across forty-one industries.

  • Hospitality & Entertainment38
  • Logistics & Freight31
  • Capital & Asset Management29
  • Healthcare Infrastructure24
  • Retail & Commerce22
  • Energy & Industrials19
  • Insurance & Risk17
  • Professional Services14
  • Public Sector12
  • Other11
11.2 weeks
Median time-to-deploy
2.8%
Median override rate, M6+
94
Calibration engineers on staff
5.4%
Walk-away rate, '25–'26

A study of your operation
is the first conversation.

We open every engagement with a thirty-minute working session and a calibration estimate within a week. No deck, no pitch — your actual decision data, replayed against the agents we would propose.

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