Find the value
Identify where AI can materially improve revenue, cost, productivity, customer experience, or decision-making.
PRIORITIZED OPPORTUNITY + BASELINE Strategy, validation & proof
Prove what is worth building.
$495 · 45-minute consult · the only price on this site · credited toward anything we build together ·
THE METHOD / 01—04Idea → proof →
Idea → proof →
evidence → decision.
We build only what is necessary to answer the investment question.
Build the proof
Create the smallest useful prototype capable of testing the hypothesis. Do not overbuild.
WORKING PROOF OF CONCEPTTest the case
Put the proof in front of real users and measure the change against an agreed baseline.
MEASURED BUSINESS CASEHand off the path
Give leadership a defensible recommendation: scale it, improve it, integrate it, or stop.
RECOMMENDATION + ROADMAPILLUSTRATIVE EXAMPLE / NOT CLIENT RESULTSWhat proof
What proof
looks like.
Every engagement begins with a hypothesis and ends with evidence.
An 80-person sales organization loses time researching accounts, switching systems, and manually preparing for customer conversations.
AI-assisted account intelligence could reduce preparation time while improving the consistency of sales conversations.
Deploy one focused prototype to a 10-person pilot. Measure research time, preparation time, adoption, quality, satisfaction, and opportunity progression where measurable.
SAMPLE OUTPUT
PLACEHOLDER VALUES
PLACEHOLDER VALUES
4,200HRS / POTENTIAL ANNUAL CAPACITY RECOVERED
$___ESTIMATED ANNUAL PRODUCTIVITY VALUE
__%PILOT ADOPTION
RECOMMENDATIONSCALE / ITERATE / STOP
THE BUSINESS CASEAI needs a
AI needs a
business case.
A working prototype is not enough. We measure whether the idea deserves more investment.
01Current state
→Baseline cost, time, quality, volume, or commercial performance.
02Intervention
→The smallest testable change inside the real workflow.
03Measured change
→Adoption, utilization, time saved, error reduction, or relevant KPI.
04Annualized value
Capacity recovered, cost avoided, or revenue influenced—with assumptions visible.
The measurement changes with the problem. Not every AI initiative has directly attributable revenue—and we will not pretend otherwise.
INDEPENDENT BY DESIGNNot every problem
Not every problem
needs AI.
ZIA does not begin with a model, platform, or predetermined solution. We begin with the business problem.
We don’t sell AI for the sake of AI.
We don’t automate broken processes without understanding them.
We don’t confuse a prototype with ROI.
We don’t recommend scaling something we can’t defend.
Sometimes the right recommendation is not to build it.Have an AI idea?
Before you fund it, prove it.
ZIA / commercial intelligence briefHarborPay
HarborPay
Partners
regional merchant-services provider with a potentially defensible hospitality wedge built around service responsiveness, agent distribution, and funding speed.
modeled opportunitystrong
evidence confidencedirectional
wedge strengthstrong
commercial readinesstesting
01 / signals
what changed
signal → implication → possible action
02 / ranked bets
opportunity map
click a thesis to inspect the evidence, assumptions, and test.
03 / scenario model
economic potential
this is a scenario model, not a forecast.
modeled annual opportunity
range reflects conservative and upside movement around the selected base assumptions.
largest value drivers
- annual residual value
- locations per account
- hospitality conversion
- merchant retention
04 / explainable ratings
commercial scorecard
05 / executive work queue
decisions requiring attention
06 / traceability
strategy graph
how one signal becomes a decision—without hiding the assumptions between them.
market signalnext-day funding
↓implicationless cash-flow friction
↓hypothesishospitality differentiation
↓economic variableretention + conversion
↓experimentmerchant interview cohort
↓decisionvertical GTM motion
prove whether hospitality actually retains better
why the strategy changes materially if hospitality merchants show superior residual quality or retention.
do segment merchant data by vertical; compare retention, residual revenue, support burden, and expansion.
quantify the value of next-day funding
why funding speed is currently a feature claim—not quantified differentiation.
do determine where speed affects payroll, inventory, purchasing, or operating liquidity.
test a hospitality-specific agent play
why agent recruiting plus hospitality relationships creates a highly testable distribution wedge.
do equip a small agent cohort and measure meeting rate, conversion, and objections.
07 / 90-day execution
now / next / later
portfolio analysis · 10 interviews · retention and residual baseline
testingICP · vertical message · proof assets · agent briefing
not startedagent pilot · merchant outreach · conversion evidence
not startedask ZIA
interrogate the thesis
select a question
answers separate what is known, inferred, and still unknown.
ZIA / vertical intelligence / commercial roofingSummit
Summit
Commercial Roofing
regional commercial roofer with a plausible recurring-revenue wedge built around inspection-led service, aging roof portfolios, and disciplined replacement timing.
modeled opportunitystrong
evidence confidencedirectional
service wedgecredible
capacity readinessunproven
01 / operating signals
what changed
the signals that could change backlog, margin, or service demand.
02 / ranked bets
opportunity map
commercial roofing rewards backlog quality and account lifetime—not lead volume alone.
inspection-to-service engine
use documented roof condition and maintenance risk to create a recurring service relationship before replacement becomes urgent.
portfolio-owner expansion
land with one property, map the owner’s portfolio, and standardize inspections across additional roofs.
bid discipline intelligence
identify which project, system, geography, and customer combinations produce attractive contribution margin.
03 / scenario model
service-contract potential
modeled value from converting inspected properties into annual service relationships.
modeled annual service value
scenario only. excludes replacement projects, emergency work, crew constraints, and contribution-margin differences.
largest unknowns
- inspection-to-contract conversion
- service gross margin
- crew utilization
- portfolio expansion rate
04 / operating truth
backlog quality
quoted backlog$4.8Msynthetic
→probability adjusted$2.1Mmodeled
→capacity feasibleunknowncrew plan missing
→margin confidenceweakjob-cost evidence missing
05 / decision queue
decisions requiring attention
know which work is actually worth winning
connect estimating, job cost, system type, crew performance, and change orders.
prove service contracts create better lifetime value
compare renewal, margin, emergency work, and replacement conversion.
separate weather demand from durable growth
track account quality and repeat work beyond storm-driven pipeline.
rebuild the backlog around probability, capacity, and margin
why quoted revenue is not executable or profitable revenue.
do score every open job by close probability, crew fit, material timing, and expected contribution.
turn every inspection into an account strategy
why the inspection creates evidence for maintenance, replacement timing, and portfolio expansion.
do standardize condition data, next action, budget window, and decision-maker follow-up.
prove the service-to-replacement flywheel
why recurring service is strategic only if it improves retention, margin, or replacement capture.
do compare lifetime economics for service-contract and project-only accounts.
ZIA / vertical intelligence / automotive retailRidgeway
Ridgeway
Auto Group
multi-store dealership group with an opportunity to connect aging inventory, lead quality, service retention, and customer lifetime value into one operating system.
modeled opportunitystrong
evidence confidencedirectional
fixed-ops potentialstrong
data readinessunproven
01 / operating signals
what changed
the signals that could change gross, turn, retention, or service capacity.
02 / ranked bets
opportunity map
the dealership wins when vehicle economics and customer lifetime value reinforce each other.
aging-inventory intervention
score every unit by age, market position, lead activity, carrying cost, and likely gross before value erodes further.
sales-to-service retention
treat every vehicle sale as the beginning of a service relationship, not the end of a transaction.
lead-intent routing
prioritize leads using behavior, vehicle fit, response history, and buying-window evidence instead of source labels alone.
03 / inventory model
aging exposure
modeled gross and carrying-cost exposure—not a forecast.
modeled 90-day value at risk
combines carrying cost and potential gross erosion for the selected aging cohort.
decisions this should inform
- price now versus hold
- retail versus wholesale
- marketing allocation
- acquisition discipline
04 / customer economics
sales → service flywheel
vehicles sold / month184synthetic
→first service booked__%insufficient evidence
→12-month retentionunknownDMS + service data required
→lifetime grossunmodeledownership view missing
05 / work queue
decisions requiring attention
which aging units still deserve patience?
combine market price, leads, test drives, carrying cost, and likely wholesale exit.
where does service retention break?
trace sold customers from first appointment through twelve-month service behavior.
which leads deserve immediate human attention?
compare response speed and appointment outcomes against observable intent.
force a decision on every 60+ day vehicle
why aging inventory silently compounds carrying cost and gross erosion.
do classify each unit: reprice, remarket, transfer, wholesale, or intentionally hold—with an owner and deadline.
make the first service appointment part of delivery
why the most valuable customer relationship may begin after the vehicle sale.
do book service before handoff and measure twelve-month retention by salesperson, store, and vehicle.
stop treating every internet lead equally
why lead volume consumes capacity while high-intent buyers wait.
do route by buying evidence, vehicle availability, response behavior, and appointment likelihood.
ZIA / vertical intelligence / data-center infrastructureNorthGrid
NorthGrid
Infrastructure
regional infrastructure supplier facing a fast-growing but credibility-constrained pipeline of data-center projects, power requirements, and long-lead equipment decisions.
market demandstrong
pipeline confidenceweak
power constraintcritical
pursuit readinessmoderate
01 / market signals
what changed
announcements matter only when they change the probability, timing, or size of executable demand.
02 / ranked pursuits
opportunity map
rank projects by executable demand—not announced megawatts.
power-delivery package
substation, switchgear, transformer, and commissioning demand tied to a credible utility path.
high-density cooling retrofit
existing facilities may need thermal upgrades before they can support denser AI compute loads.
site-readiness intelligence
score announced campuses by land, power, permits, fiber, water, capital, and procurement evidence.
03 / pursuit model
qualified pipeline
modeled supplier revenue across projects that survive credibility and timing adjustments.
modeled qualified pipeline
scenario only. announced load is discounted by execution probability and supplier capture.
largest credibility drivers
- utility interconnection position
- energization schedule
- equipment procurement evidence
- developer capital and tenant commitment
04 / project credibility
announcement → energized load
announced620 MWsynthetic
→utility engaged310 MWdirectional
→procurement visible180 MWsupported
→timing credible95 MWmodeled
05 / decision queue
decisions requiring attention
which announced projects deserve pursuit cost?
require utility, capital, tenant, permitting, and procurement evidence before committing senior resources.
where will long-lead equipment break the schedule?
map transformers, switchgear, generators, and cooling equipment against credible energization dates.
which installed sites need density upgrades?
identify facilities where AI workloads change power and cooling requirements before new builds arrive.
06 / traceability
strategy graph
the path from a public signal to a pursuit decision.
market signal180 MW load request
↓implicationutility path forming
↓hypothesisproject becoming executable
↓economic variablerevenue / MW
↓experimentmilestone validation
↓decisionpursue / monitor / reject
stop treating announced MW as pipeline
why land and press releases do not establish power, timing, capital, or procurement.
do require evidence gates before assigning pursuit resources or forecasting revenue.
own the long-lead constraint map
why equipment availability can determine which projects energize—and which suppliers gain leverage.
do connect lead times, specifications, deposits, and energization milestones across every credible project.
pursue retrofit demand before greenfield certainty
why existing facilities may need power and cooling upgrades sooner than announced campuses become real.
do identify installed sites with density, cooling, and electrical constraints tied to AI workloads.
ZIA / vertical intelligence / semiconductor suppliersAxiom
Axiom
Thermal Systems
specialty semiconductor-equipment supplier deciding where AI-driven demand, advanced packaging, and customer concentration justify scarce engineering and production capacity.
market demandstrong
design-win qualitymoderate
capacity pressurehigh
revenue confidenceunproven
01 / demand signals
what changed
the signals that could change qualification priority, capacity allocation, or customer risk.
02 / ranked bets
opportunity map
prioritize opportunities by qualification evidence and lifetime economics—not market excitement.
advanced-packaging qualification
win a repeatable process position in high-density packaging before tool-of-record decisions harden.
installed-base service expansion
turn field performance, calibration, spares, and uptime evidence into recurring revenue and account defense.
customer concentration reduction
reuse qualified capability across adjacent customers without fragmenting engineering resources.
03 / design-win model
qualified revenue potential
scenario value after qualification, production ramp, and supplier-share assumptions.
modeled qualified annual revenue
scenario only. discounts design wins by qualification probability and production realization.
largest value drivers
- qualification yield
- customer production ramp
- content per tool or line
- service and spares attachment
04 / funnel integrity
interest → production revenue
customer interest14synthetic
→evaluation active8directional
→qualified3supported
→production ramp1verified internally required
05 / resource allocation
decisions requiring attention
which evaluations deserve applications engineering?
score technical fit, customer commitment, qualification path, content, and lifetime value.
how much capacity should one customer control?
model upside against forecast volatility, dependency, and strategic leverage.
which installed systems can create recurring value?
connect uptime, calibration, spares, service response, and upgrade eligibility.
06 / portfolio exposure
concentration map
growth is less attractive when one customer, node, end market, or qualification path controls the outcome.
top customer46%modeled revenue exposure
AI infrastructure58%end-market exposure
single process family63%technical concentration
unqualified pipeline71%commercial confidence gap
rank every design win by probability-adjusted lifetime value
why engineering attention is scarce and customer interest is not production revenue.
do score commitment, qualification milestone, content, ramp timing, service value, and concentration risk.
protect the company from its best customer
why concentrated growth can quietly transfer pricing and capacity leverage to one account.
do model allocation, forecast error, cancellation exposure, and adjacent-customer reuse before expanding capacity.
turn installed equipment into an intelligence asset
why field performance can improve service revenue, product development, qualification speed, and account defense.
do unify uptime, fault, calibration, spares, and service evidence by tool and customer.
ZIA / vertical intelligence / commercial HVACMetroCore
MetroCore
Mechanical
commercial mechanical contractor with an opportunity to turn equipment history, service agreements, technician evidence, and replacement timing into a more predictable revenue engine.
installed-base valuestrong
service penetrationweak
replacement signalmoderate
technician capacityconstrained
01 / operating signals
what changed
the signals that could change service demand, technician load, replacement timing, or account risk.
02 / ranked bets
opportunity map
the installed base becomes valuable when service evidence leads to the right maintenance and replacement decision.
service-agreement conversion
convert reactive commercial accounts into planned relationships using asset risk, downtime, and response economics.
replacement-readiness engine
rank equipment by age, failure history, repair cost, efficiency, criticality, and budget timing.
technician-capacity release
reduce low-value dispatch and improve routing so scarce expertise reaches the highest-consequence work.
03 / recurring-revenue model
service-agreement potential
modeled annual value from converting eligible commercial accounts into planned service.
modeled annual agreement value
scenario only. excludes replacement revenue, emergency work, gross margin, and technician-capacity constraints.
largest value drivers
- agreement conversion
- renewal and account retention
- agreement gross margin
- replacement capture
04 / installed-base intelligence
asset → decision path
known assets1,840synthetic
→service history linked61%directional
→risk scored24%modeled
→replacement planunknownbudget evidence missing
05 / work queue
decisions requiring attention
which accounts should move from reactive to planned?
combine asset criticality, emergency history, repair spend, downtime, and decision timing.
which repairs should become replacement conversations?
compare repair economics, failure probability, efficiency, remaining life, and customer budget.
where is technician expertise being wasted?
separate diagnostic work from dispatch, documentation, parts, scheduling, and repeatable maintenance.
06 / capacity truth
technician allocation
high utilization is not automatically healthy when the wrong work consumes scarce skill.
emergency response34%capacity allocation
planned maintenance29%capacity allocation
diagnostic + repair27%capacity allocation
avoidable friction10%repeat visits, parts, admin
build a replacement calendar from the installed base
why equipment age alone does not establish urgency, value, or customer readiness.
do combine failure history, repair spend, criticality, efficiency, budget timing, and decision ownership.
prove which service agreements create lifetime value
why recurring revenue is strategic only when renewals, margin, retention, and replacement capture improve.
do compare agreement and reactive accounts across lifetime economics and service burden.
protect senior technician capacity
why the most constrained expertise should not disappear into preventable dispatch and administrative friction.
do route work by consequence and skill while instrumenting repeat visits, parts delays, and documentation load.
ZIA / vertical intelligence / freight brokerageVectorLine
VectorLine
Logistics
regional freight broker with growing shipment volume but incomplete visibility into lane contribution, customer quality, carrier reliability, and the cost of operational exceptions.
volume momentumstrong
margin qualityweak
lane densitymoderate
service reliabilityunproven
01 / operating signals
what changed
the signals that could change contribution, service risk, or account value.
02 / ranked bets
opportunity map
profitable density matters more than undifferentiated load count.
lane-density advantage
concentrate selling and carrier development where repeat volume improves pricing, coverage, and service reliability.
customer contribution reset
reprice, redesign, or exit accounts whose exceptions, payment behavior, and operating burden destroy nominal margin.
carrier reliability intelligence
match carriers using lane evidence, acceptance, service history, claims, and facility fit—not cost alone.
03 / contribution model
recoverable margin
modeled annual contribution available through pricing, density, and exception control.
modeled annual margin recovery
scenario only. models recoverable operational leakage; it does not assume additional load growth.
largest economic drivers
- accessorial recovery
- lane-level pricing discipline
- carrier acceptance and falloff
- customer operating burden
04 / commercial truth
revenue → contribution
gross revenue$18.4Msynthetic
→booked margin$4.7Mdirectional
→exception adjusted$3.9Mmodeled
→fully loadedunknownlabor allocation missing
05 / work queue
decisions requiring attention
which customers destroy contribution?
allocate exceptions, touches, disputes, payment cost, and service recovery to the account.
which lanes deserve density investment?
combine repeat demand, carrier depth, reload potential, service reliability, and margin stability.
where is cheap capacity actually expensive?
include rejection, falloff, claims, tracking burden, and recovery cost in carrier decisions.
06 / network quality
lane portfolio
every lane should earn its place through density, reliability, strategic value, or contribution.
core density22%repeatable + attractive
growth lanes31%promising / needs density
transactional29%volatile economics
exit / redesign18%burden exceeds value
recalculate customer value after operational burden
why booked margin can disappear through exceptions, manual touches, disputes, and slow payment.
do produce a fully loaded contribution view by customer, facility, and lane.
build density where service and economics reinforce each other
why repeatable lanes improve carrier relationships, price confidence, and recovery options.
do rank lanes by demand recurrence, carrier depth, reloads, contribution, and service performance.
price the exception before accepting the freight
why preventable accessorials and facility friction convert commercial wins into operational losses.
do surface expected detention, handling, appointment, and documentation burden during pricing.
ZIA / vertical intelligence / commercial insuranceKeystone
Keystone
Risk Partners
independent commercial agency with an opportunity to connect renewal risk, coverage evidence, producer attention, account rounding, and carrier strategy into one defensible client-growth system.
retention positionmoderate
account depthweak
producer capacityconstrained
carrier leverageunproven
01 / portfolio signals
what changed
the signals that could change retention, coverage adequacy, producer priorities, or carrier access.
02 / ranked bets
opportunity map
the strongest opportunity improves the client’s risk position—not merely the agency’s product count.
renewal-risk command center
prioritize accounts using loss history, price movement, carrier appetite, relationship strength, and decision timing.
evidence-led account rounding
surface plausible coverage gaps created by business growth, operational change, contracts, people, property, or cyber exposure.
producer-attention allocation
direct scarce producer time toward accounts where retention, relationship, and lifetime economics justify senior involvement.
03 / agency economics
retention value at risk
modeled commission value affected by account retention—not total insured premium.
modeled annual revenue preserved
scenario only. uses agency revenue—not premium—and excludes margin, commission variability, and new-business effects.
largest value drivers
- retention of high-value accounts
- agency revenue per account
- renewal intervention effectiveness
- account rounding and relationship depth
04 / renewal truth
book → retained value
renewing revenue$3.1Msynthetic
→relationship secure72%directional
→market viable61%appetite modeled
→retained profitunknownservice cost missing
05 / work queue
decisions requiring attention
which renewals require executive intervention?
combine client value, relationship risk, loss trend, price movement, carrier options, and timing.
which coverage conversations are actually defensible?
connect observed business change to exposure and explain why the question matters.
which accounts consume more than they contribute?
allocate service workload, claims advocacy, remarketing effort, and producer attention.
06 / portfolio resilience
concentration map
retention quality depends on more than headline book size.
top 20 accounts38%agency revenue exposure
single-line clients44%relationship-depth gap
top carrier31%placement concentration
unpriced service burdenunknownactivity costing absent
rank every renewal before the market ranks it for us
why late intervention converts manageable client and carrier risk into an emergency.
do score relationship, loss, price, appetite, timing, revenue, and coverage complexity 120 days out.
turn business change into a risk conversation
why account rounding is credible only when new operations create a real exposure question.
do monitor hiring, locations, contracts, vehicles, systems, ownership, and cyber dependency for review triggers.
allocate producer attention like scarce capital
why senior relationship capacity should protect the accounts and decisions where it changes the outcome.
do separate work requiring producer judgment from service, data collection, remarketing, and follow-up.
ZIA / vertical intelligence / staffing + recruitingForgePoint
ForgePoint
Talent
specialized staffing firm with more open requisitions than delivery capacity—and incomplete evidence about which jobs, clients, candidates, and recruiter actions create profitable placements.
demand signalmoderate
job qualityweak
delivery capacityconstrained
redeployment potentialstrong
01 / delivery signals
what changed
the signals that could change fill probability, recruiter load, margin, or client priority.
02 / ranked bets
opportunity map
the job order is not the unit of value—the profitable, repeatable placement is.
requisition-quality scoring
rank jobs by urgency, access, compensation fit, talent supply, feedback speed, competition, and client commitment.
candidate redeployment engine
match known, proven talent to upcoming demand before assignments end and relationships decay.
client lifetime economics
identify clients whose fill rate, margin, speed, repeat demand, and operating behavior justify deeper investment.
03 / placement model
recoverable gross profit
modeled value from directing recruiter capacity toward higher-probability requisitions.
modeled incremental placement GP
scenario only. assumes improved prioritization affects fill rate; excludes recruiter cost and placement timing.
largest value drivers
- requisition fill probability
- gross profit per placement
- recruiter capacity allocation
- client feedback and speed
04 / funnel integrity
job order → retained placement
open requisitions86synthetic
→qualified intake49directional
→active shortlist27supported
→retained placementunknownfalloff evidence missing
05 / work queue
decisions requiring attention
which jobs should recruiters stop working?
require compensation fit, access, urgency, feedback commitment, talent supply, and realistic competition.
which candidates should be redeployed now?
connect assignment end dates, performance, availability, preferences, and forecasted demand.
which clients deserve the strongest delivery team?
compare fill rate, margin, repeat demand, speed, exclusivity, falloff, and operating burden.
06 / capacity allocation
recruiter portfolio
activity is not productivity when low-quality searches consume scarce delivery time.
high-probability work29%protect capacity
developing searches34%needs client evidence
speculative demand25%limit investment
redeployment12%underallocated
make every requisition earn recruiter capacity
why open jobs create no value when the client, economics, talent market, or feedback loop makes a fill improbable.
do score every search and stop, renegotiate, or deprioritize the weakest work.
treat redeployment as a primary revenue motion
why proven talent carries evidence, relationship value, and lower acquisition friction.
do start matching workers to forecasted openings before their current assignment ends.
measure clients by lifetime contribution—not requisition count
why repeat demand can still be unattractive when fill difficulty, falloff, service burden, or payment destroys margin.
do allocate sales and delivery investment using fully loaded client economics.
ZIA / vertical intelligence / industrial distributionFoundryLine
FoundryLine
Supply
technical distributor with broad product access but incomplete visibility into customer wallet share, quote quality, inventory productivity, supplier dependency, and where specialist attention changes the commercial outcome.
market positionmoderate
wallet penetrationweak
inventory qualityunproven
technical advantagestrong
01 / commercial signals
what changed
the signals that could change wallet share, quote probability, inventory exposure, or supplier leverage.
02 / ranked bets
opportunity map
the distributor creates value when product, availability, technical judgment, and customer workflow reinforce each other.
customer whitespace intelligence
identify plausible categories a customer buys elsewhere using installed equipment, operating profile, purchase history, and peer patterns.
quote-probability routing
allocate pricing and technical effort using relationship, specification fit, timing, competition, and response behavior.
inventory-purpose model
decide which SKUs protect service, enable strategic accounts, create margin, or simply trap cash.
03 / wallet model
penetration potential
modeled incremental gross profit from credible category expansion inside existing accounts.
modeled incremental gross profit
scenario only. assumes verified category whitespace and excludes inventory, freight, rebate, and service-cost effects.
largest value drivers
- credible customer whitespace
- category capture rate
- gross-margin quality
- technical-sales capacity
04 / quote integrity
request → profitable order
quote requests214synthetic
→qualified opportunity128directional
→commercially viable84modeled
→profitable orderunknowncost-to-serve missing
05 / work queue
decisions requiring attention
which quotes deserve technical and pricing effort?
require customer intent, timing, specification fit, relationship, competition, margin, and next action.
which inventory is strategically necessary?
separate service-level stock, account commitments, long-lead protection, margin bets, and obsolete exposure.
where can existing accounts buy more credibly?
use installed equipment, applications, recurring purchases, and adjacent categories—not generic cross-sell lists.
06 / working-capital truth
inventory portfolio
every stocked item should protect service, enable growth, produce margin, or have an exit plan.
service critical27%protect availability
productive core39%repeat demand + margin
strategic bets14%requires evidence
excess / obsolete20%cash trapped
make every quote earn scarce specialist attention
why high quote volume can conceal poor intent, margin, timing, and win probability.
do score requests before assigning technical, sourcing, and pricing effort.
give every inventory dollar a declared purpose
why availability creates advantage only when stock protects service, growth, or contribution.
do classify every material SKU and assign replenishment, transfer, return, discount, or exit action.
build account plans from operational evidence
why credible wallet expansion begins with what the customer operates and repeatedly consumes.
do map installed equipment, applications, buying patterns, substitutions, and unresolved service needs.
ZIA / vertical intelligence / private-equity rollupsIronwood
Ironwood
Field Services
eight-location services platform with an acquisition thesis built on local growth, operating discipline, shared procurement, and management leverage—but inconsistent evidence across the portfolio.
thesis progressmoderate
location comparabilityweak
integration riskhigh
management capacityconstrained
01 / portfolio signals
what changed
the signals that could change thesis confidence, integration priority, or capital allocation.
02 / value-creation map
opportunity map
rank initiatives by EBITDA impact, confidence, time to value, organizational burden, and reversibility.
location performance normalization
create comparable views of organic growth, gross margin, labor productivity, service mix, and working capital.
procurement compliance engine
convert negotiated terms into realized savings through SKU mapping, supplier adoption, exception control, and field evidence.
integration-load management
sequence systems, process, brand, finance, and leadership changes around actual local capacity and risk.
03 / value-creation model
modeled EBITDA opportunity
scenario value from margin normalization across the existing portfolio—not acquisition growth.
modeled annual EBITDA opportunity
scenario only. excludes implementation cost, timing, taxes, financing, and acquisition effects.
largest value drivers
- comparable operating baseline
- initiative realization
- local adoption and management capacity
- gross-margin sustainability
04 / thesis integrity
underwritten → realized value
underwritten initiatives$14.2Msynthetic
→baseline verified$9.1Mdirectional
→execution funded$6.4Msupported
→run-rate realizedunknownbenefit tracking incomplete
05 / work queue
decisions requiring attention
which locations have a performance problem?
normalize mix, seasonality, labor, pricing, acquisition timing, and one-time adjustments before comparing.
which synergies are real?
connect negotiated change to adoption, realized unit economics, run rate, and P&L evidence.
where is integration destroying local execution?
measure initiative load against leadership capacity, customer impact, employee turnover, and operational variance.
06 / portfolio control
location scorecard
synthetic operating states designed to expose where leadership attention belongs.
outperforming2protect + replicate
stable3monitor
recoverable2focused intervention
thesis at risk1decision required
rebuild the operating baseline before declaring performance
why incomparable definitions turn location rankings and synergy claims into noise.
do standardize revenue, margin, labor, working capital, service mix, and organic growth definitions.
force every synergy to reach the P&L
why negotiated contracts and launched initiatives are not realized value.
do assign baseline, owner, adoption measure, economic bridge, timing, cost, and finance validation.
sequence integration around management capacity
why too many simultaneous changes can weaken customers, employees, and local execution.
do rank initiatives by value, urgency, dependency, burden, and reversibility—then stop the rest.
ZIA / vertical intelligence / Bitcoin miningFrontierHash
FrontierHash
Energy
multi-site Bitcoin miner whose operating advantage depends on power cost, fleet efficiency, uptime, curtailment value, and disciplined capital allocation—not Bitcoin price alone.
fleet economicsmoderate
power advantagestrong
site resilienceweak
upgrade readinessunproven
01 / operating signals
what changed
the signals that could change machine contribution, power strategy, or capital timing.
02 / ranked bets
opportunity map
the strongest operating decision changes with power, machine efficiency, network economics, and site constraints.
machine-level dispatch
run, throttle, or shut down fleet cohorts using real-time contribution rather than site-wide averages.
curtailment optimization
compare grid or contractual curtailment value against avoided power cost and foregone mining contribution.
fleet replacement discipline
sequence machine upgrades using efficiency, repair burden, residual value, power availability, and deployment readiness.
03 / operating model
mining contribution sensitivity
modeled monthly contribution under adjustable fleet and power assumptions—not a Bitcoin forecast.
modeled monthly operating contribution
scenario only. excludes pool fees, downtime, overhead, debt service, taxes, hedges, and capital expenditure.
largest value drivers
- hash revenue
- realized power cost
- fleet efficiency
- uptime and curtailment
04 / fleet truth
nameplate → productive hash
nameplate fleet8.0 EH/ssynthetic
→energized7.4 EH/sdirectional
→available6.9 EH/smodeled
→economically dispatchedunknownmachine contribution missing
05 / work queue
decisions requiring attention
which machines should be running right now?
compare hash revenue, efficiency, realized power, repair state, curtailment value, and thermal constraint.
which site has the strongest durable advantage?
evaluate power contract, congestion, uptime, climate, regulation, labor, network, and expansion capacity.
when should old machines be replaced?
model continued contribution, repair burden, resale value, new-machine efficiency, deployment time, and capital cost.
06 / fleet allocation
machine portfolio
every cohort needs a run, curtail, relocate, upgrade, or retire policy.
efficient core48%protect uptime
conditional fleet27%dispatch by economics
upgrade candidates17%capital decision
retire / liquidate8%value at risk
dispatch every machine on contribution—not averages
why site-level economics conceal which cohorts create or destroy cash at the current power and hash revenue.
do calculate machine-level run and shutoff thresholds with curtailment alternatives.
treat power flexibility as a commercial product
why load flexibility can create value beyond mining when grid and contract economics are explicit.
do reconcile every curtailment event against avoided cost, grid payment, lost production, and restart impact.
make fleet replacement a portfolio decision
why upgrading the wrong site or retiring too early can destroy more value than machine inefficiency.
do rank cohorts using contribution, reliability, resale, power access, deployment readiness, and capital return.
ZIA / vertical intelligence / enterprise technologyVertex
Vertex
Computing Group
global device and infrastructure manufacturer balancing enterprise refresh demand, channel inventory, AI-capable hardware, configuration complexity, services attachment, and component exposure.
enterprise demandmoderate
channel healthunproven
AI-device thesisdirectional
services potentialstrong
01 / market signals
what changed
the signals that could change refresh timing, sell-through, product mix, or service opportunity.
02 / ranked bets
opportunity map
hardware demand becomes strategic when deployment, utilization, and lifecycle value are visible.
enterprise refresh intelligence
rank account fleets by device age, performance, support status, security, workload, budget timing, and deployment friction.
AI-device use-case proof
identify roles and workflows where local AI capability creates measurable productivity, privacy, latency, or cost value.
lifecycle-services attachment
connect deployment, management, security, support, recovery, and refresh services to enterprise operating needs.
03 / refresh model
enterprise opportunity
modeled gross profit from a qualified device-refresh cohort—not total installed base.
modeled refresh gross profit
scenario only. excludes channel incentives, inventory cost, services, deployment, returns, and working-capital effects.
largest value drivers
- qualified refresh cohort
- configuration and product mix
- channel sell-through
- services attachment
04 / demand integrity
shipment → customer value
manufactured100%synthetic cohort
→shipped to channel91%sell-in
→sold through74%directional
→deployed + utilizedunknowntelemetry absent
05 / work queue
decisions requiring attention
where does channel shipment exceed demand?
connect sell-in, sell-through, weeks of supply, incentives, returns, configuration, and end-customer pipeline.
which AI-device use cases justify premium hardware?
require role, workflow, adoption, performance, privacy, latency, cost, and measurable outcome evidence.
which configurations create complexity without value?
compare demand, margin, inventory, forecast error, returns, support burden, and strategic differentiation.
06 / portfolio complexity
configuration map
every configuration should serve a segment, workload, margin, channel, or strategic purpose.
productive core36%demand + margin
strategic AI mix19%proof required
channel-specific28%inventory exposure
simplify / exit17%complexity exceeds value
stop treating channel shipment as customer demand
why sell-in can inflate apparent momentum while inventory, discounting, and returns build downstream.
do manage every commercial configuration through sell-through, deployment, utilization, and renewal evidence.
build the enterprise-refresh calendar from the installed base
why device age is only one part of a credible replacement decision.
do combine support status, performance, security, workload, budget, fleet standardization, and deployment readiness.
prove which AI workloads deserve premium hardware
why AI branding does not establish customer value or willingness to pay.
do pilot role-specific workflows and measure adoption, time, quality, privacy, latency, and total economics.
working lab / public-data POCs
proof,
not promises.
working proofs of concept built around real business problems using public or synthetic data.
what these areindependent demonstrations of how I frame, build, and test an idea.what these are notclient deployments, production systems, or claims of measured business results.
business problem
processors, ISOs, agents, gateways, and vertical platforms compete for the same merchant economics with increasingly similar claims.
hypothesis
public market evidence can reveal where a provider has a credible wedge—and where its positioning is only noise.
working POC
turns synthetic provider signals into a market position, merchant segment, competitive pressure map, and outreach thesis.
what it demonstrates
competitive research, evidence synthesis, commercial segmentation, and traceable recommendation design.
production would require
verified public sources, approved CRM data, pricing and residual context, win-loss evidence, evaluation, monitoring, and human judgment.
interactive demo · synthetic companies and signals
business problem
pipeline judgment lives in inconsistent notes and updates, making risk difficult to compare and forecasts difficult to defend.
hypothesis
extracting consistent evidence and uncertainty from updates can improve the quality of pipeline review.
working POC
turns synthetic deal updates into structured risks, missing evidence, next actions, and a reviewable forecast rationale.
what it demonstrates
signal extraction, structured reasoning, uncertainty handling, and human review of model recommendations.
production would require
CRM definitions, historical outcomes, governance, manager calibration, access controls, and measurement against forecast accuracy.
business problem
individual experimentation rarely becomes a shared operating capability, leaving teams with uneven methods and no reliable learning loop.
hypothesis
a role-specific practice system can turn isolated learning into repeatable team behavior and visible progress.
working POC
organizes practical challenges, examples, feedback, and reusable patterns into a guided enablement experience.
what it demonstrates
learning architecture, workflow-specific guidance, feedback design, and reusable knowledge capture.
production would require
role definitions, approved tools and policies, internal examples, ownership, adoption measures, and ongoing content governance.
business problem
buyers face too many options and too little decision support, while generic search does not capture needs, constraints, or tradeoffs.
hypothesis
a transparent guided recommendation can reduce decision friction while preserving buyer control.
working POC
uses public or synthetic catalog data to translate buyer requirements into ranked options with explicit rationale and tradeoffs.
what it demonstrates
requirements capture, recommendation logic, explainability, and a decision-centered customer experience.
production would require
authoritative catalog data, business rules, analytics, inventory or pricing integration, evaluation, and customer testing.
ZIA / proof lab / 01
merchant intelligence
synthetic market demonstration · no client or proprietary data
brief status evidence assembled
market thesis
wedge strengthdirectional / not predictive
primary segment
commercial motion
visible wedge
competitive heat
THE MANDATE
A polished demo is not proof. A strategy deck is not a product plan.
Before you fund production, you need evidence that the problem, economics, and experience hold up.
THE 90-DAY MANDATE
One valuable problem.
Proven before production.
MID-MARKET / $10M–$100M REVENUE$495 to start
One 45-minute consult. The mandate itself is scoped together after the call.
Working POC + source code + investment case + production roadmap.
EXAMPLE USE CASES
The right target is narrow enough to prove and valuable enough to matter.
Revenue intelligence
Turn calls, email, and CRM activity into deal risk, next actions, and forecast evidence.
Knowledge copilot
Give teams reliable answers across policies, product documentation, and institutional knowledge.
Document operations
Extract, evaluate, route, and draft high-volume documents with human review built in.
Service triage
Classify requests, assemble context, recommend responses, and escalate the exceptions.
01DAYS 01—30
Discover
Define the business problem. Map the workflow, baseline, data availability, and opportunity.
DECISION GATE → WHAT IS WORTH PROVING?02DAYS 31—60
Prove
Build the smallest useful prototype. Test with users, iterate, and establish measurement infrastructure.
DECISION GATE → DOES THE EVIDENCE MOVE?03DAYS 61—90
Decide
Evaluate pilot results, ROI, and risk. Deliver the executive recommendation and implementation roadmap.
DECISION GATE → DOES IT DESERVE DAY 91?At Day 90, you should know whether the idea deserves Day 91. Scale it, iterate, integrate, or stop—with evidence before the expensive part begins.
START WITH A $495 CONSULTCREDITED TOWARD THE MANDATE
FOCUS PLANNER
Choose the value.
Build the mandate.
Define the business problem, the people affected, and what would have to change. The tool will turn your inputs into an initial proof hypothesis—not a claim that anything has been validated.
01 / BUSINESS OBJECTIVE
0 / 4 SELECTED
Choose at least three priorities to generate your plan.
Start with the objective. Then define the problem and success metric.
THIS PLAN
CLEAR BY DESIGN
I prove the investment.
You productionize it.
MY RESPONSIBILITY
Opportunity selection, ROI model, product vision, working POC, source-code handoff, stakeholder validation, technical direction, and the production roadmap.
YOUR RESPONSIBILITY
Production engineering, enterprise integrations, security approval, deployment, organizational rollout, maintenance, and ongoing operations.
WHAT “WORKING POC” MEANS
A usable proof around one defined workflow, built with representative or approved data, tested against agreed criteria, and handed over with source code, setup guidance, architecture notes, and documented limitations.
WHAT IT DOES NOT MEAN
A production-ready enterprise application, complete edge-case coverage, certified security, organization-wide deployment, or guaranteed financial returns.
FOCUSED ENGAGEMENTS
Start wheretheconstraint is.
Every module solves a complete, standalone problem. If you need the whole system, we scope one integrated mandate. Paying repeatedly for context and handoffs creates no value. Every path starts with the same $495 consult.
ENGAGEMENT CONFIGURATOR
Build the right
starting point.
Select the work you think you need. The recommendation updates as the scope becomes more connected. No totals here—the first step is always the $495 consult.
MID-MARKET · $10M–$100M REVENUEBigger companies don’t pay more for the same problem. They pay for the complexity surrounding it.
Cross-functional problems with multiple stakeholders, systems, and decision owners.
FIRST STEP · $495 CONSULTYOUR SCOPE
Select a module to begin building your engagement.
YOUR FIRST STEP
$495
45-minute consult. Credited in full toward whatever we scope together.
A focused start
Scope and pricing beyond the consult are confirmed together before contracting. No payment is taken on this site.
Choose a module to see the recommended path.
THE PRACTICALS
01
Software, infrastructure, API, vendor, travel, and extraordinary expenses are paid separately by the client.
02
The working POC and agreed source code are handed over. Production engineering, enterprise integration, deployment, and ongoing operation require a separate client budget.
03
Financial outcomes are modeled transparently using agreed assumptions. Results are not guaranteed.