Turn scattered data into one clean, matched dataset
Starts at 2,000 EUR. Initial match + ongoing sync.
Why matching projects stall
You have product data in your ERP, competitor prices from a scraping feed, and supplier catalogs in a shared drive. The records describe the same things but nothing links them. Names don’t match, IDs don’t align, formats differ. A developer writes a script that handles the obvious cases, but the ambiguous 20% sits in a spreadsheet forever. We build matching pipelines that combine scraping, AI, and deterministic rules to link records across sources — then handle the ongoing flow of new, updated, and deleted items so the match stays current.
Any source, scraped or not
We scrape websites, APIs, and platforms you point us to. We also ingest your internal data — databases, spreadsheets, ERPs, PIMs. Everything enters the same matching pipeline.
AI + deterministic matching
Rule-based matching for clean identifiers. AI-assisted matching for messy names, partial addresses, and inconsistent attributes. The right technique for each field.
Precision-recall you choose
Some clients need zero false positives. Others need maximum coverage and can tolerate review. We calibrate the pipeline to the tradeoff that fits your operations.
Stays current over time
Initial bulk matching is the biggest job, but data keeps moving. New records, updates, deletions. The pipeline runs on schedule and handles the ongoing delta.
What most teams try first
VLOOKUP or exact-match scripts
Quick to write, handles the easy cases. Matches on a shared ID or exact name.
Falls apart on dirty data. One misspelling, one missing field, one format difference, and the record drops out. You end up with a matched set that covers 60% and a growing pile of exceptions.
Master data management platforms
Enterprise-grade matching with configurable rules. Built for exactly this problem.
Heavy setup, significant licensing cost, and ongoing configuration. Makes sense at enterprise scale but overshoots most matching projects in both cost and complexity.
Manual matching in spreadsheets
A person can resolve ambiguous matches that algorithms miss.
Works for hundreds of records, not thousands. Doesn’t scale, can’t repeat on a schedule, and the person who built the spreadsheet becomes a single point of failure.
Why a custom matching pipeline
Matching is a spectrum, not a toggle. The right approach depends on how clean your data is, how many sources you have, what the cost of a false match is, and whether you need this once or forever. We build the pipeline that fits your actual situation — not the one that assumes your data is perfect.
Built for these situations
Tell us what you’re matching
Which sources, how many records, and what does a good match look like? We’ll scope the pipeline.
Get a QuoteFrom disconnected sources to linked, deduplicated data
Map the sources
We inventory your data: internal systems, files, and the web sources we’ll scrape. We assess schema overlap, data quality, and where the hard matching problems will be.
Build the matching model
Deterministic rules for clean identifiers. Fuzzy matching and AI for names, descriptions, and partial data. We define match confidence thresholds and how exceptions are handled — automated resolution, flagged for review, or discarded.
Run the initial match
The bulk match across all sources. This is typically the largest job. You review a sample of results to validate accuracy before we finalize.
Handle exceptions
Not every record matches cleanly. We build the level of exception handling that fits your requirements — from automated best-guess resolution to structured human review queues.
Keep it running
New records, updates, and deletions flow in on schedule. The pipeline matches incrementally, so you’re not reprocessing the full dataset every time.
Why choose Stratalis for data matching
Scraping and matching in one team
Most matching projects need web data. We scrape the sources and build the matching pipeline. One team, one delivery, no integration gap between the data collector and the data linker.
Economical by design
We scope matching to what you actually need. If 90% accuracy is enough, we don’t build for 99%. If your data is clean enough for rules, we don’t add AI. You pay for the precision your operations require.
Built for ongoing operations
The initial match matters, but data changes daily. We build pipelines that handle new, updated, and deleted records over time — not one-off scripts that need manual re-runs.
Engineers, not a platform
No per-record pricing, no rigid matching models. We build the exact pipeline your data needs, using the techniques that fit each source and field.
FAQ
That’s common and expected. We assess data quality upfront and set realistic expectations. The pipeline handles what can be matched automatically and routes the rest through exception handling that fits your budget — automated best-guess, human review, or simply flagging the gap.
AI-assisted matching handles multilingual data, abbreviations, and naming inconsistencies. We also build normalization steps into the pipeline — standardizing formats, expanding abbreviations, transliterating — before the matching layer runs.
Yes. We ingest data from any source you can export or give us access to — databases, APIs, spreadsheets, file drops. The pipeline treats scraped and non-scraped sources identically.