Data as a service: subscribe to a current dataset, not a collection project
Data as a service (DaaS) means you buy the outcome — a current, structured dataset sitting in your stack — instead of building and babysitting the collection that produces it. Recordpipe is a DaaS provider for public-record data: county recorders, assessors, courts, state registries, licensing boards, and permit offices. You describe the dataset and the delivery target. We build the pipeline, run it on the cadence you set, and keep the data landing.
The difference from a one-off data purchase is the subscription. A static file is out of date the day it arrives. A managed data feed refreshes daily or weekly, carries what changed since the last run, and comes with someone accountable when a source moves. That accountability is the product.
Every record is public data with provenance: a capture date and a source category on every row. Nothing login-walled, nothing private, no breached data — the same stance across every contract, whether the dataset is a single county's recorded instruments or a national license registry.
What a data as a service company actually runs on your behalf
A data as a service company earns its retainer by absorbing the parts that decay. Behind every feed we deliver is a managed data pipeline: RPA and automation bots we operate ourselves, scheduled collection, parsing into your schema, entity deduplication, change detection, and validation against the prior run. When a source restructures its pages or changes a form, our team repairs the pipeline. Your feed keeps arriving; your engineers never see the break.
Monitoring is part of the service, not an add-on. Each run is checked for row counts that drift, fields that go empty, and refreshes that finish late. A status feed reports the last completed refresh per dataset, so your own alerting can watch it.
The AI analysis layer sits between collection and delivery. It filters records to your criteria, deduplicates entities that appear under variant names, and ranks or flags what matters — new filings, changed owners, lapsed licenses — so the feed you receive is already shaped for use. That is what managed means in managed data pipeline: everything between the public source and your table is our problem.
Managed data feed delivery: warehouse, bucket, SFTP, webhook, or API
A custom data feed is only useful where your team already works, so delivery is designed around your stack. Warehouse-native: tables loaded directly into Snowflake, BigQuery, Redshift, or Databricks on each refresh, with a change table alongside the full snapshot. Object storage: Parquet, CSV, or JSON dropped to S3, GCS, or Azure Blob with a manifest per run. SFTP for teams whose ingestion is file-based. Webhooks that push matching records the moment a run finds them. Or a data feed API — search, fetch, and a cursor-based change endpoint — when your product queries on demand.
Most engagements combine channels. A nightly warehouse load for analytics plus a webhook for the alerts that cannot wait a day is a common shape. The schema is versioned and documented: additive changes flow in quietly, breaking changes get a new version and a migration window.
The pipeline behind every delivery method is the same one — the infrastructure processing over one million public records nightly — so adding a channel later is a configuration change in the retainer, not a rebuild.
DaaS pricing: a fixed build, a flat retainer, and a $500 scoping to start
DaaS pricing at Recordpipe has no per-row meter and no usage tier to outgrow. The engagement starts with a free intake form describing the dataset, cadence, and delivery target. We reply within 2 business days with feasibility and an approach. Then a $500 scoping, credited in full against the contract, delivers a real data sample and a fixed quote within 5 business days — your team reviews actual rows in the format you will receive them, not a proposal deck.
The build is fixed-price. Contracts run from $5,000 to $3 million depending on the sources in scope, the cadence, and how much analysis sits on top. Ongoing operation — collection, maintenance, monitoring, delivery — is a flat retainer quoted alongside the build. When your volume grows or you add another delivery channel, the retainer is resized; nothing is throttled.
Data subscription terms are written once: refresh cadence, schema change policy, delivery commitments, and downstream licensing if the data will appear in your own product. What you pay is known before the pipeline is built, and it does not move when a source does.
What an engagement delivers
| Deliverable | Description |
|---|---|
| Managed pipeline | Collection from the public sources in scope, parsed into your schema and run on your cadence. Built, operated, and maintained by Recordpipe. |
| Schema and data dictionary | A versioned field list with types, allowed values, and provenance columns — agreed before the build and documented for your engineers. |
| Full snapshot per run | The complete current dataset delivered on every refresh, so a consumer can always rebuild state from the latest load. |
| Change delta | Rows added, updated, or no longer present at source since the prior run, with a change type and timestamp on each. |
| Delivery to your target | Warehouse tables, object-storage drops, SFTP, webhooks, or API — configured to your stack and tested against it before launch. |
| Analysis layer | Filtering to your criteria, entity deduplication, and ranking or flagging rules applied before delivery, tuned during scoping. |
| Run manifest | Per-run metadata: row counts, capture window, sources covered, and validation results, so your ingestion can verify each load. |
| Monitoring and alerts | Automated checks on every run and human review when a check fails. You hear from us before a stale feed reaches your users. |
| Source maintenance | Repairs when a source changes its layout or process, handled under the retainer with no ticket from your side. |
| Status endpoint | Last completed refresh per dataset, queryable by your own monitoring. |