Service · Industrial & MRO data

Industrial & MRO Data Scraping

With part numbers cross-referenced and price breaks captured as tiers.

Industrial and MRO data scraping is the automated collection of distributor and supplier catalogue data for industrial, maintenance and electronic components — manufacturer part numbers cross-referenced to distributor SKUs, quantity price break tiers, minimum order quantities, lead times, published datasheets and lifecycle status — so cross-distributor comparison holds at the part level.

A single price on an industrial part is nearly meaningless. What matters is the price at your quantity, the minimum you must buy, and when it arrives. Those three vary independently across distributors for the identical manufacturer part.

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✓ Part number cross-referencing✓ Price break tiers captured✓ Free pilot sample in 24 hours

industrial_parts_2026-08-05.jsonl LIVE FEED

{"mpn":"6204-2RS-C3", "manufacturer":"Example Bearings", "distributor":"example-mro.com", "distributor_sku":"EM-771204", "xref_confidence":0.97, "currency":"GBP", "price_breaks":[{"qty":1,"unit_price":6.40}, {"qty":10,"unit_price":5.15}, {"qty":50,"unit_price":4.85}, {"qty":250,"unit_price":4.12}], "moq":10,"pack_multiple":10, "stock_qty_shown":1840, "lead_time_days":2, "lead_time_basis":"in_stock_dispatch", "lifecycle_status":"active", "datasheet_url_captured":true, "compliance_published":["RoHS","REACH"], "compliance_verified":false} {"mpn":"6204-2RS-C3", "distributor":"other-mro.com", "moq":100, "price_breaks":[{"qty":100,"unit_price":3.95}], "lead_time_days":21, "note":"cheaper unit price, 10x MOQ, 3 week lead"}

2 of 12,884,200 part-distributor rows · run 2026-08-05MPN cross-referenced 94.3% · schema v3.2

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Written and reviewed by the Actowiz Solutions Data Engineering team Published 5 August 2026 Last updated 5 August 2026 Reading time 11 min

Key facts at a glance

What it is: Managed collection of industrial, MRO and component distributor catalogue data
Cross-referencing: Manufacturer part numbers matched to distributor SKUs with confidence scoring
Price breaks: Full quantity tier ladder, not a single price
Order constraints: MOQ and pack multiples captured, since they change the real cost
Lead times: Captured with the basis stated: in-stock dispatch versus factory lead
Lifecycle: Active, not recommended for new designs, obsolete, where published
Compliance: RoHS, REACH and similar captured as published, never verified
Who it's for: Procurement, sourcing, engineering, distributors and manufacturers

94.3%MPN cross-reference ratewith confidence scores

Price breaksfull tier laddernot one price

MOQ + pack multiplecapturedthe real cost driver

Lead time basisstated per recordstock vs factory

Key takeaways

What it is: Managed collection of industrial, MRO and component distributor catalogue data
Cross-referencing: Manufacturer part numbers matched to distributor SKUs with confidence scoring
Price breaks: Full quantity tier ladder, not a single price
Order constraints: MOQ and pack multiples captured, since they change the real cost
Lead times: Captured with the basis stated: in-stock dispatch versus factory lead
Lifecycle: Active, not recommended for new designs, obsolete, where published

Last verified 5 August 2026 by the Actowiz Solutions Data Engineering team.

Definition

What is industrial and MRO data, and why is one price useless?

Industrial and MRO data scraping is the automated collection of distributor and supplier catalogue data for industrial parts, maintenance and repair supplies, and electronic components: pricing across quantity tiers, order constraints, availability, lead times, technical documentation and lifecycle status.

This category behaves nothing like consumer retail, and the datasets that treat it as retail are unusable.

Why a single price means nothing here

Price is a function of quantity. The same part costs 6.40 at one unit and 4.12 at 250. A dataset holding one number has picked a quantity arbitrarily.
Minimum order quantity changes the real cost. A distributor quoting 3.95 with an MOQ of 100 is more expensive than one quoting 4.85 with an MOQ of 10, if you need twelve.
Pack multiples constrain further. You cannot buy 15 of a part sold in tens.
Lead time is part of the decision. Cheaper with a three-week factory lead is not cheaper if the line is down.
Part identity is fragmented. The same manufacturer part carries a different SKU at every distributor, often with suffix variations that may or may not be significant.

How we structure it

Every record is a part-distributor combination keyed on the manufacturer part number with the distributor SKU retained, cross-referenced with a confidence score. Pricing is a price_breaks array covering the full tier ladder, alongside moq and pack_multiple. Lead time carries lead_time_basis so in-stock dispatch is never confused with factory lead.

What we cannot see

Contract and negotiated pricing. Distributor list pricing is public; what your organisation actually pays under a supply agreement is not, and neither is anyone else's. We collect published pricing and are explicit that it is list rather than landed cost.

What we collect

Six categories of industrial and MRO data

Price break ladders and MOQ are the fields that make comparison valid. Lifecycle status is the most under-collected.

Part identity & cross-reference

The layer everything else depends on.

Manufacturer part number as published
Distributor SKU retained
Cross-reference with confidence score
Suffix and variant handling
Manufacturer name normalisation

Quantity price breaks

Price as a ladder, not a number.

Full price break tier array
Unit price at each quantity
Currency per distributor and market
Price change detection per tier
List versus promotional pricing where distinguished

Order constraints

What you must actually buy.

Minimum order quantity
Pack multiple or increment
Stock quantity where displayed
Backorder acceptance where indicated
Reel, box and bulk packaging options

Lead times

With the basis made explicit.

Lead time in days as published
Basis: in-stock dispatch or factory lead
Availability date where quoted
Lead time change detection
Expedite options where published

Documentation & specification

The technical layer.

Datasheet capture where publicly downloadable
Published specification attributes
Dimensional and material data as listed
Certification documents where public
Documentation revision detection

Lifecycle & compliance

Obsolescence risk and published claims.

Lifecycle status where published
Not-recommended-for-new-design flags
Obsolescence and last-buy dates
RoHS, REACH and similar as published
Replacement part references where given

Service scope

What the ecommerce data scraping service includes

A managed engagement, not a tool licence. We own the pipeline and everything that breaks in it.

✓ Included in every engagement

Manufacturer part number as the primary key with distributor SKU retained
Full quantity price break ladders rather than a single price
MOQ and pack multiple captured, since they change real cost
Lead time basis stated so stock dispatch is never confused with factory lead
House-brand equivalents recorded as relationships, not merged as the same part
Source discovery, scoping and a written collection plan
Free pilot on your own sources before any commitment
Full pipeline build, hosting and proxy infrastructure
Schema design, validation and sampled human QA on every run
Ongoing maintenance when source layouts change — our cost, not yours
Delivery to your warehouse, bucket, SFTP or API endpoint
Documented methodology and compliance notes for your legal review

× Not included — stated upfront

Trade account creation or use of client credentials to reach gated pricing
Contract or negotiated pricing, which is not published for anyone
Submitting quote requests or RFQ forms to extract pricing
Verification of published compliance marks
Anything behind a login, paywall or credentialed session
Personal data beyond a documented lawful basis
Licensed third-party datasets we do not hold rights to
Guarantees about fields a source simply does not publish

Schema

Industrial and MRO fields you receive

Every engagement delivers a documented schema. These are the core fields; the full dictionary runs to 120+ and is agreed during scoping.

Deliverable schema — v3.2 core fields (full dictionary: 120+ fields)
Field	Type	What it captures	Refresh
`mpn / manufacturer`	string	Manufacturer part number and normalised manufacturer, the primary key	Every run
`distributor / distributor_sku`	string	Distributor and their own SKU, retained for ordering	Every run
`xref_confidence`	decimal	Confidence in the part cross-reference, since suffix variations may be significant	Every run
`price_breaks`	array	Full quantity tier ladder with unit price at each break	Daily
`moq / pack_multiple`	int	Minimum order quantity and purchase increment, which change the real cost	Daily
`stock_qty_shown`	int	Stock quantity as displayed, which many industrial distributors do publish	Daily
`lead_time_days / lead_time_basis`	int / enum	Lead time and whether it reflects stock dispatch or factory lead	Daily
`lifecycle_status`	enum	active, nrnd, obsolete or as published, for obsolescence risk	Weekly
`last_buy_date`	date	Where a manufacturer or distributor publishes a final order date	Weekly
`datasheet_url_captured`	boolean	Whether technical documentation was publicly downloadable and captured	Weekly
`compliance_published / compliance_verified`	array / boolean	Compliance marks as published, with verified constant false	Weekly

Unlike consumer retail, many industrial distributors publish actual stock quantities. We capture stock_qty_shown as displayed. It is still a display figure rather than an audited inventory count, and we do not treat it as one.

Coverage

Distributors and categories we cover

Industrial distribution is fragmented and regional. Coverage is built to your supplier and part set.

Broadline industrial distributorsElectronic component distributorsMRO and facilities suppliersBearings and power transmissionFasteners and fixingsHydraulics and pneumaticsElectrical and automationTooling and abrasivesSafety and PPE suppliersLaboratory and process suppliesRegional and national distributorsManufacturer direct cataloguesMarketplace industrial sellersPDF catalogue and price list sources

Many industrial distributors gate trade pricing behind a login. We collect published list pricing only and do not create trade accounts. Where a distributor publishes nothing without login, we say so rather than substituting another source. Request a source we don't list →

Markets served

Countries and markets where this service is in highest demand

We deliver into 40+ countries. These are the markets where this particular service is requested most, and the reason demand concentrates there.

Highest-demand markets for this service, and why demand concentrates there
Market	Why demand concentrates here
Germany	The deepest industrial distribution base in Europe, and the market where published list pricing and price break ladders are most complete.
United Kingdom & Netherlands	Strong broadline and specialist distribution with good published pricing transparency.
United States	Large distributor base with widely published stock quantities and lifecycle status, particularly in electronic components.
India & Southeast Asia	Rapidly formalising industrial distribution with growing online catalogues and significant regional supplier long tail.

North America

United StatesCanadaMexico

United Kingdom & Ireland

United KingdomIreland

Western Europe

GermanyFranceNetherlandsBelgiumSpainItalySwitzerlandAustria

Nordics

SwedenNorwayDenmarkFinland

Middle East

United Arab EmiratesSaudi ArabiaQatarKuwaitIsrael

Asia Pacific

SingaporeAustraliaNew ZealandJapanSouth KoreaMalaysiaIndonesiaThailandVietnamPhilippines

South Asia

IndiaBangladeshSri LankaPakistan

LATAM

BrazilArgentinaChileColombia

Africa

Spare part sourcing under downtime pressure needs stock and lead time visibility across suppliers at once.

What we deliver

Stock quantity and lead time with basis stated across your distributor set on the parts you actually hold.

Metric that moves

Downtime hours

Use cases

How industrial data gets used in practice

Four patterns, with the outcome each is judged on.

True cost comparison at your quantity

Price break ladders are collected with MOQ and pack multiples, so distributors are compared at the quantity you actually buy rather than at whichever tier a single-price dataset happened to capture.

Outcome: Sourcing decisions made on cost at real order quantity rather than on headline unit price.

Obsolescence risk monitoring

Lifecycle status, not-recommended-for-new-design flags and last-buy dates are tracked across the part set with change detection.

Outcome: Obsolescence surfaced with time to act rather than discovered at the next order.

Distributor consolidation modelling

Cross-referenced part data across the distributor set with lead times and stock supports scenario modelling for supplier rationalisation.

Outcome: Consolidation scenarios costed at part level instead of estimated.

Competitor tier pricing for distributors

Full price break ladders on matched manufacturer part numbers reveal where competitors price aggressively by tier rather than overall.

Outcome: Tier-level pricing strategy set against observed competitor ladders.

Engagement examples

Two engagements, anonymised

Clients rarely permit naming. These are real engagement shapes with identifying detail removed, so you can judge whether the work resembles your situation.

Manufacturer · DE

Distributor comparison used a single price per part

Situation

Sourcing compared distributors on unit price without accounting for quantity tiers or minimum order quantities, so the cheapest quote frequently was not.

What we ran

Full price break ladders with MOQ and pack multiples on cross-referenced manufacturer part numbers across the distributor set.

Result

Comparison moved to cost at actual order quantity, reversing several sourcing decisions.

Equipment maker · UK

Component obsolescence was discovered at order time

Situation

Lifecycle status was buried in distributor pages and never monitored, so end-of-life parts surfaced as failed orders requiring redesign.

What we ran

Lifecycle status, not-recommended-for-new-design flags and last-buy dates tracked with change detection across the part set.

Result

Obsolescence surfaced with months of lead time rather than at the point of failure.

Examples are anonymised at client request. Named references are available on request under NDA. See published case studies →

The 24-hour sample — run on your sources, not ours

Before you commit to anything, we run this service against your own sources and send you the output. If the coverage isn't there, the sample will show you that too — which is the point. We would rather lose the deal at the pilot than at month three.

Real extraction from your actual sources
Returned inside two business days
Coverage and QA note included
You keep the data either way
No card, no trial clock
Named engineer on the call

Get my free sample Book a 20-min scoping call Reply within one business day. Reference calls available under NDA.

How we engage

Three ways to engage us

Same collection pipeline and QA underneath. The difference is who holds the schedule and how the data reaches you.

Managed service (most common)

We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.

Dedicated engineer assigned to your account
Site changes fixed by us, not reported to you
Scheduled delivery to your warehouse or S3
Named contact on Slack or email

Best fit: Teams who need the data, not the infrastructure.

API access

The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.

On-demand and scheduled endpoints
Rate limits agreed to your load profile
Sandbox keys for integration testing
Versioned schema with deprecation notice

Best fit: Product and engineering teams building on live data.

One-time or project extraction

A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.

Fixed scope agreed in writing upfront
Single delivery with full QA report
Methodology documented for your records
Converts to managed if you want continuity

Best fit: Research, strategy and diligence work with a deadline.

Pricing

Every engagement is quoted individually, because the honest answer depends on your scope: how many sources, how many records, how often, and how the data reaches you. We scope it with you, run a free pilot on your own sources, and then quote a fixed monthly figure — no per-request metering and no overage billing when volumes move. Request a quote and you will have a number after one call.

Build vs buy

Should you build industrial data collection in-house or hire it as a service?

Part number cross-referencing across distributors is continuous modelling work, not a one-time mapping.

In-house build vs self-serve tool vs Actowiz managed service
Consideration	In-house scraping team	Generic proxy / DIY tool	Actowiz managed feed
Time to first usable data	6–12 weeks of engineering before anything is trustworthy	Days, but output needs manual cleanup before use	Free pilot in 24 hours, production in 5–10 business days
Who fixes it when a source changes	Your engineers, at the cost of their roadmap	You do — tools report failures, they don't resolve them	We do, same business day, inside the retainer
Data quality assurance	Whatever your team has time to build	None beyond HTTP success	Schema validation plus sampled human QA on every run
Compliance documentation	Rarely produced, then requested urgently by legal	Not provided; terms risk sits with you	Sources, method and lawful basis documented for review
Accountability	Distributed across a team with other priorities	A support ticket queue	A named engineer and an account owner
True annual cost	Engineer salaries, proxies, hosting, ongoing maintenance	Low licence fee plus significant hidden analyst time	One fixed monthly retainer, quoted after scoping

Part number cross-referencing is the whole job

Everything useful in industrial data depends on knowing that a part at distributor A is the same part as at distributor B. That sounds trivial and is the hardest part of the category.

Why it is hard

Suffixes may or may not matter. A trailing C3 can denote a genuine specification difference or a packaging variant. Treating them as equal merges different parts; treating them as different fragments the same one.
Distributors reformat part numbers. Spaces, hyphens and case are inconsistent, and some distributors strip characters entirely.
Manufacturer names vary. Legal entity variants, acquisitions and abbreviations, the same problem as assignee normalisation in patent data.
House brands complicate it. Distributors sell equivalents under their own brand, which are functionally comparable but not the same part.
Some distributors publish only their own SKU with the manufacturer part number in the description or a datasheet.

How we handle it

Cross-referencing combines normalised part number matching, manufacturer normalisation, specification comparison where published, and datasheet extraction where the part number appears only there. Every link carries xref_confidence.

The failure modes are asymmetric, so we bias against merging. A wrong merge produces a price comparison between two different parts, which is worse than a missing comparison because it looks correct. Low-confidence links arrive flagged rather than applied.

House-brand equivalents are recorded as equivalence relationships rather than as the same part, so you can include or exclude them deliberately. For procurement that distinction is often the whole analysis.

What is behind a login stays behind it

Industrial distribution has a stronger tradition than most sectors of putting real pricing behind a trade account. It is worth being explicit about what that means for this service.

What we collect

Published list pricing, price break ladders, MOQ, stock and lead times as shown to an anonymous visitor. Many industrial distributors publish all of this openly, which is why the category works at all.

What we decline

Trade account creation. We do not register accounts to reach gated pricing.
Client credentials. We will not log in as you, even when offered, and clients do offer.
Contract pricing. Your negotiated rates are yours; nobody else's are obtainable and we would not try.
Quote request automation. We do not submit RFQ forms to extract pricing, which would put fabricated enquiries into a supplier's pipeline.

What we do instead

Where a distributor publishes nothing without login, we state that during scoping and exclude them rather than substituting a different source and letting the gap go unnoticed. Roughly speaking, list pricing is available broadly enough that a useful comparison set exists without touching gated data.

And list pricing is genuinely useful even when you buy on contract: it establishes the reference from which your discount is measured, and tracking list movement tells you when to reopen a negotiation. Detail on bespoke sources in custom data extraction.

How it works

How an industrial data engagement goes live in 5 to 10 business days

Distributors, part set and whether cross-referencing is required are scoped first, and cross-referencing is tuned against your own known equivalents during the pilot.

Scope the sources and fields

You send us target sites, regions, SKUs or keywords. We return a field-level schema proposal, coverage estimate and refresh recommendation — usually within two working days.

Pilot sample, free

We extract a real sample from your actual targets so you can inspect field fill rates, edge cases and match quality before any commitment.

Production build and QA harness

Our engineers build extractors, then wire validation rules: type checks, range checks, duplicate detection and golden-record comparison against a manually verified subset.

Scheduled delivery into your stack

Feeds run at your chosen cadence and land in the warehouse or bucket you already use. Schema changes are versioned and announced before they ship.

Ongoing monitoring and SLA support

We watch coverage drift, fill rates and source changes daily. A named engineer owns your account, and layout breaks are fixed by us — not queued for you.

Formats & destinations

JSON, JSONL, CSV, Parquet or XLSX, delivered to Amazon S3, Google Cloud Storage, Azure Blob, SFTP, Snowflake, BigQuery, Databricks or a REST/GraphQL endpoint. Webhooks fire on completion, and every batch ships with a manifest containing row counts, schema version and QA results so your pipeline can fail loudly instead of silently ingesting a bad file.

Compliance & data ethics

We collect published list pricing and catalogue data visible without an account. We do not create trade accounts, use client credentials, submit quote requests to extract pricing, or access gated trade pricing. Published compliance marks are captured as published with compliance_verified constant false.

Service commitments

What we commit to, in writing

These are contractual, not marketing copy. They appear in the engagement document.

Service level commitments written into every managed engagement
Commitment	What we hold ourselves to
Pilot turnaround	A real sample from your own sources within 24 hours of scoping, at no cost.
Go-live	Production collection running within 5–10 business days of sign-off.
Delivery punctuality	99.5% on-schedule delivery, measured monthly and reported to you.
Breakage response	Source layout changes triaged same business day; critical sources inside 4 hours.
Data quality	Schema validation on every run plus sampled human QA before any delivery leaves us.
Escalation	A named engineer and an account owner, not a shared ticket queue.
Change requests	Field additions and source changes handled inside the retainer, not re-quoted.
Exit	Your historical data exported in full on request. No lock-in, no export fee.

Why teams pick Actowiz for this work

Engineers, not a dashboard. You get people who fix breakages, not a self-serve tool you maintain yourself.
We tell you what we can't do. Scope limits and coverage gaps are stated before you sign, not discovered in month three.
QA is part of the service. Schema validation and sampled human review run before delivery, every run.
Compliance is documented. Sources, method and lawful basis written down so your legal team can review them.
Fixed monthly cost. No per-request metering, no surprise overage on a month when a competitor adds SKUs.
Six years, 40+ countries. Long-running production pipelines across retail, travel, mobility and finance.

Definitions

Terms used on this page

Plain definitions of the terms used on this page, so procurement and legal reviewers are working from the same vocabulary as your data team.

Price break ladder: The full set of quantity tiers and their unit prices. A single price is arbitrary in this category because price is a function of quantity.
Pack multiple: The increment in which a part must be purchased. Combined with minimum order quantity it determines real cost, which is why a lower unit price is often more expensive.
Lead time basis: Whether a quoted lead time reflects dispatch from stock or a factory lead. Conflating the two is the most common error in industrial sourcing data.

FAQ

Industrial and MRO data: frequently asked questions

What procurement, sourcing and engineering teams ask during evaluation.

Because price is a function of quantity in this category. The same part can be 6.40 at one unit and 4.12 at 250. A dataset holding one number has picked a quantity arbitrarily and made every comparison invalid.

We deliver the full tier ladder plus MOQ and pack multiple, because a distributor quoting 3.95 with an MOQ of 100 is more expensive than one quoting 4.85 with an MOQ of 10 if you need twelve.

About 94%, with confidence scores on every link. The difficulty is suffixes — a trailing character can denote a real specification difference or just packaging.

We bias against merging, because a wrong merge produces a price comparison between two different parts and looks entirely correct. Low-confidence links arrive flagged rather than silently applied.

No. We collect published list pricing only. We do not create trade accounts, use client credentials, or submit quote requests to extract pricing.

List pricing is still useful if you buy on contract: it establishes the reference your discount is measured against, and tracking list movement tells you when to reopen a negotiation. Where a distributor publishes nothing without login, we exclude them and say so rather than substituting a different source.

Yes, as equivalence relationships rather than as the same part. A distributor's own-brand bearing may be functionally comparable to a branded one but it is not the same part.

Keeping them as a relationship lets you include or exclude equivalents deliberately. For procurement that distinction is often the whole analysis, and merging them would remove the choice.

Yes, where published — lifecycle status, not-recommended-for-new-design flags, last-buy dates and replacement part references, with change detection.

This is one of the most under-collected fields in the category and one of the most valuable, because obsolescence discovered at order time is a redesign; obsolescence discovered six months early is a purchasing decision.

Many do, which is unusual compared with consumer retail. We capture stock_qty_shown as displayed.

It remains a display figure rather than an audited inventory count, and we do not treat it as one. Combined with lead time and its basis, it is still substantially more actionable than the binary availability flag consumer retail provides.

Where publicly downloadable, yes, with revision detection. Datasheets also feed cross-referencing, since some distributors publish the manufacturer part number only inside the document.

Document volume drives storage cost, so we scope whether full capture or reference-only is needed. Most clients start with references and archive selectively.

No. Compliance marks are captured as published with compliance_verified permanently false. Verification requires documentation review and testing, not extraction.

What we do provide is a dated record of what each distributor published, including changes — which is what a compliance review starts from rather than concludes with.

We quote individually. Drivers are distributor count, part set size, whether cross-referencing is required, refresh frequency and whether datasheet capture is included.

A defined part list across several distributors at weekly refresh sits at the lighter end. Broad catalogue coverage with cross-referencing and datasheet capture sits higher. One scoping call, a free pilot on your own part list within 24 hours, then a fixed monthly quote. Request a quote.

See real price break data for your own part list

Send us manufacturer part numbers and distributors. We return cross-referenced records with full price ladders, MOQ and lead times within 24 hours.

Get a free industrial data sample Book a 20-min scoping call

Free pilot, no card, no obligation. Send known equivalents and we'll tune cross-referencing against them.

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Industrial & MRO Data Scraping

Key facts at a glance

Key takeaways

What is industrial and MRO data, and why is one price useless?

Why a single price means nothing here

How we structure it

What we cannot see

Six categories of industrial and MRO data

Part identity & cross-reference

Quantity price breaks

Order constraints

Lead times

Documentation & specification

Lifecycle & compliance

What the ecommerce data scraping service includes

✓ Included in every engagement

× Not included — stated upfront

Industrial and MRO fields you receive

Distributors and categories we cover

Countries and markets where this service is in highest demand

North America

United Kingdom & Ireland

Western Europe

Nordics

Middle East

Asia Pacific

South Asia

LATAM

Africa

Which teams buy industrial and MRO data

Head of Procurement

Strategic Sourcing Manager

Design / Component Engineer

Pricing Manager

Category Manager

Maintenance / Reliability Lead

How industrial data gets used in practice

True cost comparison at your quantity

Obsolescence risk monitoring

Distributor consolidation modelling

Competitor tier pricing for distributors

Two engagements, anonymised

Distributor comparison used a single price per part

Component obsolescence was discovered at order time

The 24-hour sample — run on your sources, not ours

Three ways to engage us

Managed service (most common)

API access

One-time or project extraction

Pricing

Should you build industrial data collection in-house or hire it as a service?

Part number cross-referencing is the whole job

Why it is hard

How we handle it

What is behind a login stays behind it

What we collect

What we decline

What we do instead

How an industrial data engagement goes live in 5 to 10 business days

Scope the sources and fields

Pilot sample, free

Production build and QA harness

Scheduled delivery into your stack

Ongoing monitoring and SLA support

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