/ market intelligence
Market intelligence for decisions where a rough guess is too expensive.
Claorova builds custom forecasting models, supply-chain risk systems, scrapers and pipelines, custom indices, and procurement or competitive-intelligence dashboards for semiconductor, metals, energy, and commodity teams. We also brief buyers from research and models we already hold when a greenfield build is not required. Work is led from Tempe, Arizona, and delivered remotely across the United States. Spot history alone is rarely enough: capacity commitments, lead-time distributions, and yield uncertainty often move the decision before the headline price does.
The problem
Enterprise buyers drown in vendor slides and still lack a defensible range.
Procurement, supply-chain, ops and strategy, and technical-investor teams sit between licensed data terminals, consultant decks, and internal spreadsheets that disagree. Consensus reports describe the industry. They rarely encode your single-source exposure, your inventory policy, or the go / no-go threshold your leadership will actually use.
The expensive failure mode is false precision: a single number that looks decisive in a slide and collapses under the first shock. Claorova's market intelligence practice exists for that gap. We quantify the uncertainty, name the assumptions, and attach a decision rule your operators can use on Monday. Buyers who already hold strong vendor subscriptions still come when the remaining question is custom: a SKU-level risk map, a capacity scenario under yield variance, a procurement dashboard that joins licensed feeds with internal exports, or a briefing from models we already maintain.
What we track
Signals that change procurement, capacity, and hedging decisions.
Coverage is strongest where we have methodology and public proof: semiconductor supply chains, steel and metals, energy and power, and cross-cutting commodity forecasting. Custom MI engagements include forecasting models, supply-chain risk, pipelines and scrapers, custom indices, and procurement or CI dashboards. If a vertical is outside our depth, we say so.
Commodity and price forecasting
Probabilistic forecasts with explicit confidence bands for semiconductor-adjacent inputs, steel and metals, energy, and industrial commodities. Point estimates without uncertainty are not the deliverable.
Supply-chain risk and capacity
Utilization, lead-time distributions, allocation risk, single-source exposure, and bottleneck mapping across fabrication, inventory, and assembly stages when the data supports it. Yield variance belongs in the model, not in a footnote.
Demand and scenario intelligence
Base, upside, and downside branches tied to the decision you actually make: procurement timing, hedging posture, capacity commitments, or go / no-go on a build.
Competitive and procurement surfaces
Custom indices, scrapers and alternative-data pipelines, and dashboards that put licensed and internal data where operators can use it. We name licensed vendors such as ICIS, CRU, and Bloomberg as sources; we do not republish their figures.
Custom indices and monitoring pipelines
When the question is continuous, we design ingestion, normalization, and refresh so the decision rule stays live across weekly or monthly reviews. Pipelines track signals that change procurement or capacity calls, not vanity data lakes.
Intelligence already held
Some buyers need a briefing from research and models Claorova already maintains, not a greenfield build. We put existing work in front of the decision and only open a custom engagement when the question outruns what we hold.
How delivered
Briefings, models, and live surfaces on a cadence that matches the decision.
Cadence is scoped to the decision: one-off briefings for a discrete question, or monthly and quarterly refresh when the model needs to stay live. Format follows how your team decides. Related commissioned analyst work lives on the market research and data forecasting service page when you need a scoped report or model rather than an ongoing practice.
Executive briefings
Decision-ready memos that state the question, the range, the assumptions, and the rule for acting when the market moves.
Models and workbooks
Auditable forecast models (Python or spreadsheet) your team can challenge, refresh, and own after handoff. Assumptions and data lineage sit next to the numbers.
Live dashboards and pipelines
Ongoing monitoring when the question is continuous: ingestion, normalization, scenario refresh, and a surface for weekly or monthly review by procurement or ops.
Methods include time-series models, Monte Carlo simulation, scenario and sensitivity analysis, and statistical process control when the data warrants them.
Build vs buy
When a vendor subscription is the better answer.
Honesty converts. If TrendForce, CRU, ICIS, Bloomberg, or a similar licensed product already answers your question with consensus coverage and maintained series, keep that subscription. Custom market intelligence is slower to stand up than reading a report you already pay for.
Hire a custom engagement when the decision needs your constraints: allocation under yield uncertainty, a procurement dashboard that joins internal exports with licensed feeds you already hold, a scenario tree for a capacity commitment, or a briefing from models Claorova already maintains. We name vendors as vendors. We do not compete by republishing their tables. If the right answer is keep your terminal and refine the internal decision rule, that is what we say.
Methodology
Sources to model to uncertainty to decision rule.
- Sources. Map what you can share: internal exports, public series, and licensed vendor data your team already holds. Document lineage; refuse to invent coverage.
- Model. Choose the lightest method that answers the decision: time-series, scenario trees, Monte Carlo, or a simulation engine when stage-level uncertainty matters.
- Uncertainty. Deliver ranges and sensitivity, not a single heroic point. Confidence bands are the product when the cost of being wrong is high.
- Decision rule. Attach an explicit rule for acting when the market moves: when to accelerate procurement, hold, or revisit capacity.
Proof
Public artifacts, not logo walls.
Flagship artifact
Yield-uncertainty supply-chain simulator
A numerical simulation engine that models a three-stage semiconductor supply chain, from fabrication to die bank to assembly, under uncertain manufacturing yields. It sweeps uncertainty levels, pinpoints tipping points where ignoring uncertainty costs profit and service, and distills decision rules. Built in Python with PuLP, CBC, NumPy, and Matplotlib. This is the same class of probabilistic work we bring into commissioned engagements.
Public methodology writing covers semiconductor, steel and metals, energy, and commodities. Client engagements stay confidential unless a buyer authorizes a named case study. We do not invent metrics, AUM, or enterprise logos.
Limits
Who we do not serve.
We are not a replacement for a licensed data terminal, not a content mill that republishes ICIS, CRU, Bloomberg, or other vendor figures, and not a fit for buyers who want a single point forecast with no uncertainty band. We also decline work that asks us to fabricate metrics or stretch semiconductor, metals, energy, and commodity methods into unrelated markets without saying so. If your existing subscription is enough, we will say that in the briefing conversation.
FAQ
Questions buyers ask before a briefing.
- It is an enterprise practice for custom forecasting models, supply-chain risk systems, procurement and competitive-intelligence dashboards, and decision-ready briefings. Coverage is strongest in semiconductor, steel and metals, energy, and commodities. Work is led from Tempe, Arizona, and delivered remotely across the United States.
- Vendor subscriptions excel at licensed datasets, consensus coverage, and recurring industry reports. Claorova is for custom questions those products do not answer: your SKU mix, your single-source exposure, your capacity commitment, your decision rule. We name those vendors; we do not republish their figures.
- Procurement leads, supply-chain directors, operations and strategy teams, and technical investors who need a defensible range before committing capital, inventory, or capacity.
- Semiconductor supply chains, steel and metals, energy and power, and cross-cutting commodity forecasting. If a vertical is outside our methodology depth, we say so before scoping.
- Our flagship public proof artifact: a numerical simulation engine that models a three-stage semiconductor supply chain under uncertain manufacturing yields. It runs controlled optimization experiments, finds tipping points where ignoring uncertainty costs profit and service, and distills decision rules. Built in Python with PuLP, CBC, NumPy, and Matplotlib.
- No. We may recommend or reference licensed vendors as data sources when your team already holds a subscription, but we never republish proprietary vendor figures on this site or in public materials. Client deliverables respect your license terms.
- One-off briefings for a discrete decision, or monthly and quarterly model refresh when the question stays live. Format follows how your team decides: memo, workbook, Python model, or dashboard.
- When you need broad consensus coverage, licensed price series, or recurring industry reports that a subscription already delivers well. Custom intelligence is for decisions that need your constraints, your uncertainty, and a rule your operators can run on Monday.
- We are a poor fit for teams that want a single heroic price target with no uncertainty, for buyers who need us to republish licensed vendor tables, and for questions outside semiconductor, metals, energy, and commodity depth. We also decline work that asks us to invent coverage or metrics we do not have.
- Email the studio or schedule a briefing with the decision, the data you can share, and the cadence you need. We will tell you whether a custom engagement, a one-off analysis from intelligence we already hold, or a vendor subscription you already pay for is the right answer.