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CORA Partners — AI Operators for Mid-Market Ops

AI Operators that do the work.And prove they did it right.

Purpose-built agents that take over one operational job, ship with a measurable success rubric, and improve themselves over time. Configured for your operation, owned by your business, accountable to your standards.

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Built for
Numbers from real engagements
$1M+
Business value protected & unlocked
$339K
Annual savings, single client
0.07%
Total recordable case rate
0
Major non-conformances on audits
100%
Client retention & repeats
All figures from actual client engagements. No averages, projections, or "results may vary."
Who this is for

Operations-heavy. Spreadsheet-bound. Out of time.

You run a 50–500 person operations-heavy business — manufacturing, logistics, multi-location services, distribution, field operations, or back-office-intensive professional services. You've outgrown the tools that got you here: critical knowledge lives in spreadsheets, key people's heads, and paper trails. You can feel the friction every day, but you don't have time to solve it because you're too busy running the business.

You don't need another consultant who'll deliver a 60-slide deck and disappear. You don't need another piece of software to add to the pile. You need AI Operators that actually do the work, prove they're doing it correctly, and get measurably better month over month. That's what we deploy.

The CORA Partners Accelerator

Pick where to start. We'll show you where it leads.

Each track is a self-contained engagement with measurable outcomes. They also stack: Track B is the foundation Track C runs on, and Track D keeps the fleet healthy as your business changes.

Track A
Demand Engine
AI-Native Lead Generation
5 days to launch
Stop losing the prospects who land on your site.

We deploy an AI sales agent — not a chatbot — that conducts real discovery conversations, qualifies against a rubric you define, books the call, drafts the follow-up, and hands off to your team with full context. Behind it: a conversion-focused landing page and a 60-second brand video, all live in five days. Every conversation is graded against your qualification criteria, so you can see and improve what's actually working. Weekly dreaming surfaces the patterns your team would never catch — which industries convert fastest, which questions kill deals, which talking points compound trust.

You walk away with

A published landing page, a finished brand video, a deployed AI sales agent trained on your business and graded against your qualification rubric, a weekly Dreaming digest of what your agent is learning, and analytics that tell you exactly what's converting and why.

Track B
AI-Ready Operations Layer
Operational foundation for AI agents
4–6 weeks
Make your business machine-actionable.

We consolidate your fragmented data — spreadsheets, legacy systems, paper trails, vendor portals — into a clean, real-time, AI-queryable substrate. This isn't a database; it's the context layer your AI Operators reason against to take action. Without it, every agent you deploy (ours or anyone else's) leaks accuracy. With it, agents act on truth. We model not just your data but your business logic and vocabulary, so an AI Operator can answer "which orders are at risk this week?" with the same nuance your best ops manager would.

You walk away with

A centralized Postgres/Supabase substrate with your operational data and business logic modeled cleanly, automated pipelines from your source systems, a starter set of metrics and dashboards, and the foundation every AI Operator we deploy from here forward stands on.

Track C
Deploy an AI Operator
A self-improving AI for a specific operational job
3 weeks
Take 10–20 hours per week of repetitive ops work off your team's plate, permanently.

We deploy an AI Operator — a purpose-built agent that takes over one recurring operational job. Examples: a Maintenance Dispatcher that monitors a production line, predicts failures, routes technicians, orders parts, and writes the incident report. An SLA Watchdog that tracks every customer commitment, flags breach risk before it happens, and escalates with full context. An Inventory Operator that triggers reorders, manages exceptions, and reconciles to actuals. Every CORA Partners AI Operator ships with a success rubric — defined with you in week one — and is graded on every output. When it slips, the grader catches it before your customer does, and the operator iterates.

You walk away with

A deployed AI Operator running against your actual workload, a success rubric your team owns and can iterate on, weekly Outcomes reports showing performance against criteria, monthly Dreaming digests of what your operator is learning, and a written playbook for extending the deployment as your needs grow.

Track D
AI Operations Partner
Continuous improvement of your AI Operator fleet
$3,000 / month
Software decays. AI Operators don't have to.

But only if someone's reviewing what they're learning, deciding what to ship, and tightening their rubrics as the business changes. That's what this retainer is. Each month: a Dreaming Review (here are the patterns your operators surfaced, here's what we recommend shipping), an Outcomes Audit (here's how each operator performed against its rubric, here's where it slipped, here's the fix), a 30-minute strategy call, and direct access for anything urgent. You're not buying advisor time — you're buying the function that keeps a fleet of self-improving AI agents healthy and accountable.

You walk away with

A monthly written Dreaming Review and Outcomes Audit, a weekly 30-minute check-in, priority queue access for new requests, and direct Slack/email access to a senior operator who knows your business and your fleet.

Why an operator, not a feature

Most AI being pitched to mid-market ops is a feature. We deploy operators.

Salesforce Agentforce, Microsoft Copilot, the AI button bolted onto whatever SaaS you already pay for — those are features. They make your team faster inside the host application. They're useful for what they do. But they're built for the average customer of their vendor, you can't change what they consider success, you can't audit why they decided what they decided, and the day you outgrow that vendor or get acquired by a company on a different stack, the capability goes with them. You're renting AI that lives inside someone else's box.

Closed vendor feature
  • Built for the vendor's average customer
  • Vendor defines what success looks like
  • Opaque decisions, no audit trail
  • Locked into the host application
  • Improvements set by vendor roadmap
  • Capability leaves when you leave the vendor
CORA Partners AI Operator
  • Configured to your operation, your data
  • Your rubric, defined with you in week one
  • Every output graded; failures caught before they reach a customer
  • Portable, documented, owned by your business
  • Improvements driven by your operational data
  • You own the system, not a subscription

We build on Claude Managed Agents because it's the best production platform shipping right now — but the operator we deploy is configured to your operation, owned by your business, and answerable to your standards. The difference isn't ideological. It's whether, three years from now, the AI you adopted is helping you run your business or holding it hostage.

How an AI Operator works

You define done. The operator gets there.

Every CORA Partners AI Operator runs the same loop. You set the goal and the success rubric in week one. The operator monitors, decides, acts, and reports — and keeps going until the work is actually finished.

01Trigger

Something happens worth acting on.

A sensor reading drifts out of range. A customer commitment is at risk of breach. Inventory at a location falls below reorder threshold. The operator is watching continuously, so the trigger gets caught immediately — not at the next weekly review.

Example: vibration on Press #4 spikes above the rolling baseline at 2:47 AM.

→
02Goal Loop

The operator works toward your defined outcome.

Reasons about the situation, checks the rubric, decides the right action, takes it, and verifies the result. When the output doesn't meet the rubric, it iterates. Every output is graded against your standards before it leaves the system.

Example: identifies vibration pattern as bearing wear, pages the qualified technician with the parts to bring, opens the work order, notifies the shift lead.

→
03Done

The work is complete. Your team has what they need.

The operator writes up what happened, posts to your channel of choice, updates the system of record, and closes the loop. You get a clean handoff — not a stack of half-finished alerts waiting for someone to chase down.

Example: incident report written, production schedule updated, supervisor Slacked the summary, work order closed.

Featured case studies

What transformation looks like in practice.

Each engagement starts with a baseline and ends with a measurable outcome. Three from the last few years.

Quality & Compliance

Two failed audits to zero non-conformances in 16 weeks

A NJ food packaging manufacturer had failed two consecutive SQF audits. We rebuilt the QMS from the documentation up — redesigning workflows, retraining staff, and embedding ownership before the next audit cycle.

0Major non-conformances
16 wksTo audit-ready
$500KRevenue protected
Read the full case study →
Process Engineering

$339K in annual savings without cutting headcount

A mid-market manufacturer facing margin pressure needed sustainable cost reductions. We applied Lean Six Sigma methodology — mapping the value stream, redesigning the highest-impact workflows, and installing measurement systems to lock in the gains.

$339KAnnual savings
10×Engagement ROI
3+ yrsSustained
Read the full case study →
Safety & Operations

From reactive incidents to a 0.07% recordable rate

A production facility with recurring incidents needed a safety culture, not another policy. We installed a behavioral-based safety program with observation protocols, near-miss infrastructure, and supervisory accountability that made safety daily practice.

0.07%Total recordable rate
Top %Industry percentile
↑Near-miss reporting
Read the full case study →
William McCann, Founder of CORA Partners
About the operator

I've been on the floor. That's why I build for it.

Hi — I'm William McCann. I spent 40 years running operations across packaging, food manufacturing, distribution, and industrial businesses, solving the same problems most ops-heavy companies face: spreadsheet sprawl, knowledge trapped in someone's head, the wrong person paged at the wrong time, and no clean way to see what was actually breaking.

I founded CORA Partners (the operations consulting arm of the CORA family) because the tools that finally make AI useful on the floor have arrived — and most consultants are still selling 2018 dashboards and 2023 chatbots. I deploy AI Operators because that's what mid-market operations actually need: not slides, not software to pile on, but agents that do the work, prove they're doing it, and get measurably better month over month.

If we work together, you'll deal with me directly. No account managers between you and the work.

— William, Founder, CORA Partners

How we build

Built on the platform setting the standard for production AI agents.

CORA Partners AI Operators are built on Claude Managed Agents with Dreaming, Outcomes, and multi-agent orchestration — Anthropic's platform for production AI agents that work, improve, and prove their work. We were among the first firms to deploy operators on this stack, and we configure it for mid-market operations specifically.

Measurable, not promised

Every AI Operator we ship is graded on every output against criteria you define. Performance is reported, not assumed — you see how it did, where it slipped, and what we changed.

Self-improving

Operators review their own work and surface patterns. They get more useful month over month, not less. The work bench gets sharper while you sleep.

Where the technology is going

The platform we build on is the one Harvey used to 6× its task completion rate and Wisedocs used to cut document review 50% — not yesterday's no-code tooling.

Configured for ops, not for tech

We don't sell AI to AI companies. We deploy operators for plant managers, COOs, and ops directors who need the work done — and need it done right the first time.

We picked the platform whose published customer base — Harvey, Wisedocs, others — proves the technology. We deploy it where the operational impact actually compounds: on factory floors, in distribution centers, and across field operations. The same infrastructure that 6×-ed Harvey's task completion is the infrastructure dispatching your next maintenance call.

6× Harvey task completion rate 50% Wisedocs review-time cut 10–20 hrs/wk typical Track C savings
Connected to your stack

Your AI Operator works inside the systems you already run.

We integrate with the operational systems running your business — ERPs, CMMS, WMS, scheduling, quality, communications. No rip-and-replace. Your operator reads, writes, and acts where the work already happens.

ERPSAP · Oracle · NetSuite · Dynamics · Sage
CMMS / MaintenanceMaximo · eMaint · Fiix · UpKeep · Limble
Warehouse / InventoryNetSuite WMS · Fishbowl · Manhattan · Körber
CRM / ServiceSalesforce · HubSpot · ServiceNow · ServiceMax
CommunicationsSlack · Microsoft Teams · Gmail · Outlook · Twilio
ProductivityGoogle Workspace · Microsoft 365 · Notion · Airtable
Data SourcesPostgres · Supabase · Snowflake · BigQuery · Excel
IoT / TelemetryPLCs · MQTT · OPC UA · sensors · device APIs

Don't see your stack? We deploy on Claude Managed Agents, which connects to any system with an API or data export. If it produces data, we can wire your operator into it.

Operators we deploy

Three of the operators we ship most often.

Each one is configured to your data, your rubric, and your team. The names below are templates — what we actually deploy is yours.

M
Maintenance Dispatcher
Production · Light industrial

Monitors equipment telemetry, predicts failures before they happen, routes the right technician with the right parts, and writes the incident report when the work is done. Reduces unplanned downtime and ends the hunt for "who knows how to fix this one."

Typical outcome: 15–30% reduction in unplanned downtime within 60 days
S
SLA Watchdog
Services · Distribution

Tracks every commitment you've made to customers, flags breach risk before it happens, and escalates with full context (account, history, value at risk, recommended action). Stops your team from finding out about misses from the customer.

Typical outcome: SLA breaches caught 3–5 days earlier on average
I
Inventory Operator
Multi-location · Field ops

Monitors stock across locations, triggers reorders against business rules (not blanket thresholds), handles exceptions like vendor delays and substitutions, and reconciles to actuals. Replaces the weekly spreadsheet ritual with continuous management.

Typical outcome: 10–20 hours/week back to your ops team

Outcome ranges reflect typical engagements on the Claude Managed Agents platform. Your actual results depend on your data, your rubric, and the operator we deploy together.

Industries we serve

Six segments where AI Operators land hardest.

Every operational job in every industry has its own rhythm. These are the segments where our operators do the most useful work today — and where 40 years of operational pattern recognition pays for itself in week one of deployment.

M
Manufacturing
Packaging · Food · Light industrial

Equipment monitoring, predictive maintenance, quality control, shift handoffs. We deploy operators that catch failures before they shut down a line.

Typical outcome: 15–30% reduction in unplanned downtime
D
Distribution & Logistics
3PL · Warehousing · Last mile

Inventory exception handling, order routing, SLA tracking, vendor escalation. Operators that keep the flow moving without your dispatchers refreshing screens.

Typical outcome: 10–20 hours/week saved on routine exception work
S
Multi-Location Services
Franchise · Healthcare · Property

Site-level performance monitoring, compliance tracking, inventory reordering across locations. Operators that give corporate the visibility no human team can scale.

Typical outcome: Issues caught 3–5 days earlier across the network
F
Field Operations
Service · Construction · Utilities

Technician dispatching, parts staging, customer commitment tracking, post-job documentation. Operators that handle the coordination layer behind the people in trucks.

Typical outcome: First-time-fix rate improvements of 8–15%
P
Back-Office Professional Services
Accounting · Insurance · Legal ops

Document intake and review, deadline tracking, exception escalation, status reporting. Operators that handle the volume work so professionals do the judgment work.

Typical outcome: 2–4× throughput on routine document review
R
Regulated Industries
Food & Beverage · Medical Device · Pharma

Compliance monitoring, audit prep, non-conformance tracking, documentation governance. Operators built with regulated-industry rigor — backed by SQF, BRC, and FDA experience.

Typical outcome: Audit-ready posture year-round, not the week before
Frequently asked

Questions buyers ask before saying yes.

How is an AI Operator different from a chatbot or a workflow automation?
A chatbot answers questions. A workflow automation runs a script. An AI Operator does the work — reads the data, makes a judgment, takes the action, and is graded on the result. When the rubric isn't met, it iterates. When patterns emerge, it learns. Workflow tools follow rules; operators handle exceptions, which is most of operations.
What if our data is a mess? Most of it is in spreadsheets and people's heads.
That's exactly what Track B (AI-Ready Operations Layer) exists for. We don't pretend AI Operators work without a clean substrate — they don't. If your data is fragmented, we build the foundation first. If you've already got a reasonably clean warehouse or operational database, we can skip Track B and go straight to Track C.
Do we need a technical team to maintain the operator?
No. That's what Track D is for — ongoing operation, monitoring, and improvement of the operators we've shipped, run by us. Your team uses the operator's outputs (alerts, dispatches, reports) without ever touching its internals. If you'd rather take over after launch, we provide a written playbook and training as part of the Track C handoff.
How do you measure success?
We define a success rubric with you in week one of any engagement — specific, observable criteria for what "the operator did its job" looks like. Every output is then graded against that rubric by an independent evaluator. You see weekly performance reports showing pass rate, where it slipped, and what we changed. Outcome metrics (hours saved, SLA breaches caught, downtime reduced) sit on top of the rubric and are reported monthly.
What if the AI Operator makes a mistake?
The grader catches most failures before they reach a customer or the floor — it scores the output and routes failures back for another pass. For the failures that get through, we set up human-in-the-loop checkpoints on high-stakes actions during the first 30–60 days, then loosen them as confidence is earned. Dreaming surfaces patterns of failure so we can tighten the rubric or add guardrails. We don't promise zero errors. We promise measurable, improving error rates with you in the loop on what matters.
Can we start with one track and add others later?
Yes. The most common starting bundle is Track C (Deploy an AI Operator) plus Track D (AI Operations Partner) — ship one operator, keep it healthy. Track B becomes the natural next step when you want to deploy a second or third operator and need shared infrastructure. Track A is independent and can be run any time.
What does the engagement look like, week to week?
For Track C: Week 1 is rubric definition and operator scoping with your team. Week 2 is build and integration. Week 3 is shadow mode — the operator runs against your real workload while a human reviews every output, and we tune. At the end of week 3 the operator goes live. From there, Track D (if you've signed up) takes over with monthly Dreaming Reviews and Outcomes Audits.
Resources

Free tools to start moving the needle today.

A masterclass, a calculator, a community, and a blog — all built for operations leaders, all free.

M

AI for Operations Leaders

4 modules, 12 lessons, ~90 minutes. Vendor-neutral. Certificate at the end. No credit card.

Start the masterclass →
$

AI ROI Calculator

Plug in your numbers and see the realistic payback for an AI Operator deployment in your operation.

Run the numbers →
#

Ops Leaders Network

Slack community for operations leaders working through real problems. Direct access. No fluff.

Join the network →
B

The Operations Floor

Field notes on AI Operators, mid-market ops, and what's actually working on real production floors.

Read the latest →

Ready to deploy an operator that earns its keep?

Book a 30-minute discovery call. We'll diagnose the highest-leverage place to start and show you the rubric we'd build it against — no slides, no generic templates.

Book a Free Strategy Call