An AI automation agency that ships data, not demos.
Most AI automation agencies sell a workshop and a prototype. Recordpipe sells a running pipeline: AI-powered RPA that collects public records at scale, and an analysis layer that classifies, resolves, ranks, and summarizes each delivery down to what your team needs. Designed in scoping, operated by us, delivered into your stack.
Agentic collection
Automation that adapts to how each source publishes and keeps running when formats change.
AI classification and normalization
Free-text fields classified, names and entities resolved, duplicates removed before delivery.
Ranking against your definition
Every delivery scored against your ideal account, property, or case profile.
Summaries and flagged changes
Change feeds and summaries instead of full dumps, for teams that act on deltas.
AI automation services with an outcome, not a toolkit
Most AI automation services start with a workshop and end with a deck. Recordpipe starts with a dataset you need and ends with that dataset arriving on schedule. The AI is a layer inside a managed pipeline, not the product. It classifies, matches, ranks, and summarizes public records that our automation has already collected, so what reaches your team is the shortlist, not the haystack.
The scope is fixed: publicly available government records — registries, courts, recorders, assessors, licensing boards, permit offices. The automation collects them. The AI layer turns raw rows into something a person can act on. Delivery puts the result into your file drop, warehouse, webhook, or hosted API.
An automation agency that sells tooling leaves you to operate it. We operate it. That is the whole difference, and it shows up in the contract: a fixed price for a defined output, with the pipelines and the models maintained by us. Scoping is $500, credited, with a real sample and a fixed quote within 5 business days.
AI RPA: what an AI powered RPA pipeline actually does with public records
AI RPA gets described as bots that think. In practice, AI powered RPA is a division of labor. The RPA handles what is deterministic: opening the source, submitting the query, capturing the record, repeating on cadence. The AI handles what is fuzzy: is this filing the same company as that registration, which of these permits matter for your use case, what does this docket entry mean in plain language.
In a Recordpipe pipeline, that split is explicit. Collection runs on the automation that keeps over one million public records processing nightly. Then the analysis layer runs against your rules: classification into the categories your team uses, entity resolution across sources with a confidence score, deduplication of records that describe the same event, ranking by the signals you care about, and summarization of long documents into the fields you asked for.
Every AI decision is logged with its inputs and its confidence. Low-confidence outcomes go to an exception queue rather than into your data. You see the model's work; you do not have to trust it blind.
AI agents for data extraction, run by an automation partner that stays
AI agents for data extraction are the most-searched and least-defined thing in this category. Here is what they mean in a Recordpipe engagement. An extraction agent is given a document type — a recorded deed, a court filing, a license record, a permit application — and a target schema. It reads the document, populates the fields, cites where each value came from, and flags what it could not resolve. It runs inside the pipeline, on every document, on cadence.
The value is not the agent. It is the operation around it: a validation step that checks the extracted fields against the expected shape, a human on the escalation path, a repair-and-backfill process when a source changes its format, and a change notice to you when the fields shift. An automation consultancy hands you the agent. An automation partner keeps it working.
Output is structured rows with a document reference and per-field provenance, delivered wherever your systems ingest. Details on the runtime are on the RPA at scale page.
Agentic automation you can audit, from an intelligent automation company
Agentic automation sounds like handing the keys to a model. In public-records work that is the wrong design. Agents are useful for bounded steps — resolve this entity, classify this filing, summarize this document — and dangerous when asked to run an open-ended process with no checkpoint. Recordpipe uses them the bounded way, with a deterministic pipeline deciding what runs, when, and what counts as done.
That makes the output auditable. Each delivered record carries its capture date, its source category, and, where the AI layer touched it, the decision it made and the confidence it had. Your compliance team can reproduce why a record was included, merged, or ranked where it was. Raw public data, not consumer reports; we are not a CRA, and eligibility use cases get compliance review at intake.
As an intelligent automation company, the offer is narrow on purpose: managed pipelines over public records, with an AI layer that makes them usable, priced as fixed contracts from $5,000 to $3 million. If that is the partner you want, the intake form is the first step and the $500 scoping is the second.
What an engagement delivers
| Deliverable | Description |
|---|---|
| collected_records | Raw public records captured by the automation on cadence, in a stable schema with capture dates. |
| classification | Each record tagged into the categories your team uses, with the label and its confidence stored alongside. |
| entity_resolution | Records describing the same business, filer, or property linked across sources, with match confidence. |
| deduplication | Filings that describe the same event collapsed to one row, with every source reference retained. |
| ranking | Records ordered by the signals agreed in scoping, so reviewers start at the top of a shortlist. |
| summaries | Long documents reduced to the fields and plain-language summary you specified, with citations to the source text. |
| extraction_provenance | Per-field note of where each value came from in the source document. |
| exception_queue | Low-confidence outcomes routed to human review instead of delivered as fact. |
| decision_log | Inputs, outputs, and confidence for every AI step, retained for audit. |
| delivery_and_manifest | Output by file, warehouse load, webhook, or hosted API, with a per-run manifest and checksum. |
In the field.
Docket triage for a litigation research team
A litigation research team can receive new court filings in its practice areas nightly, with each filing classified by cause of action, parties resolved to the entities the firm tracks, and a plain-language summary of the pleading. Analysts open a ranked shortlist rather than reading every docket entry.
Entity resolution for a compliance data team
A compliance team can hand over a customer book and receive it linked across state registries, UCC records, and licensing boards — variant names, affiliates, and agents resolved to one entity with a confidence score. Eligibility use cases get compliance review at intake; the output is raw public data, not a consumer report.
Permit ranking for a building-products sales team
A manufacturer's sales team can get permit records collected across its territories and ranked by the signals it sells against: project type, scope described in the application, and stage. The AI layer reads the free-text descriptions so reps do not, and the top rows land in the CRM by webhook.
Deed extraction for a title and escrow operation
A title operation can commission extraction agents for recorded documents in the counties it serves: grantor, grantee, legal description, and consideration pulled into structured fields with per-field provenance. Ambiguous instruments go to an examiner queue; clean ones post straight to the production system.
AI RPA, agentic automation, intelligent automation: what actually ships
The AI is applied to a real data problem with a fixed price and acceptance criteria, not sold as a capability. Contracts from $5,000 to $3 million; scoping is $500, credited, with a sample in 5 business days.