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Who we build for

Teams that depend on data arriving correctly.

The work looks different across sectors, but the underlying need doesn't: structured records, on a known schedule, that someone else stays responsible for.

Are we a fit

We'd rather say no early than disappoint you late.

  • You need the same data again next week
  • A schema your warehouse can rely on
  • Sources that fight back
  • One-off list, never repeated
  • A tool you'd run yourself
  • Data behind someone's login
Talk it through
By team

What each kind of team is actually up against.

Pricing & e-commerce teams

Competitor prices, stock levels, and promotions across a defined set of retailers, matched to internal SKUs.

The pressure

Decisions get made every morning. Data that arrives late or partially is worse than no data, because someone prices against it anyway.

Daily pre-market delivery · SKU-keyed · Parquet or warehouse

AI & ML teams

Large text or structured corpora, deduplicated, filtered, and carrying provenance on every record.

The pressure

Dirty training data is expensive to discover late. Near-duplicates and template boilerplate degrade runs quietly.

Batch delivery · JSON Lines · provenance retained

Real estate & proptech

Listings from multiple portals, normalized to one schema with cross-portal duplicates resolved.

The pressure

Every portal models property differently. Reconciling them is the actual work, and it never ends.

Daily or hourly · address-keyed dedupe · unified schema

Recruitment & HR tech

Job postings, employer attributes, and hiring signals tracked over time rather than as a snapshot.

The pressure

Postings appear and vanish. Miss a window and the history has a hole you can't backfill.

High cadence · change tracking · historical retention

Market intelligence

Category coverage, brand presence, and assortment tracked consistently across a competitive set.

The pressure

Comparisons only hold if collection is consistent. A methodology change mid-quarter invalidates the trend.

Weekly or monthly · stable methodology · versioned schema

Finance & alternative data

Web-derived signals collected on a strict schedule with defensible provenance.

The pressure

Timing and auditability matter as much as accuracy. A number you can't trace back is a number you can't use.

Fixed windows · full provenance · audit trail

Fit

Whether this is worth a conversation.

We'd rather you self-select out now than discover the mismatch three weeks into scoping.

We're a good fit if

  • You need the same dataset repeatedly, not a one-off export
  • The data feeds something that matters: pricing, a product, a model
  • Nobody on your team wants to own scraper maintenance
  • You care about knowing when coverage drops, not just receiving files
  • You'd rather agree a schema up front than clean data afterwards

We're probably not if

  • You want a self-serve scraping tool to operate yourself
  • You need data from behind a login that isn't yours
  • You need it tomorrow with no scoping conversation
  • The lowest possible price matters more than reliability
  • You're collecting personal data without a lawful basis
Consistent across every project

The same validation, whatever the sector.

Sector changes the schema and the cadence. It doesn't change what runs before a batch is allowed to ship.

Type conformance
Every field matches its declared type
Range and sanity
Values fall inside agreed bounds
Null-rate thresholds
Coverage flagged when it drops
Duplicate resolution
Resolved against your chosen key
Schema drift
Source structure changes caught early
Volume variance
Unexpected record counts halt delivery
How we work

What working with us is actually like.

We'd rather show you how we operate than fill this space with quotes. Every point below is something you can hold us to from the first conversation.

You talk to the people building it

No account layer between you and the engineers. The person who scoped your schema is the person who fixes it when a source changes.

We tell you when something won't work

If a source is unreliable, a field isn't consistently available, or a request isn't something we'll take on, you hear it during scoping, not after an invoice.

Small on purpose

A four-person team means every project gets senior attention. We take on work we can do properly rather than filling a pipeline.

The maintenance is the commitment

Anyone can deliver a dataset once. We stay responsible for it: monitoring sources, catching drift, and repairing extraction before it reaches you.

Recognise your situation in any of this?

Tell us the sources and the fields. You'll get an honest read on feasibility and a sample from your own sources before committing to anything.

No retainer required to find out whether your sources are feasible.