# PromptFormr

> PromptFormr LLC is a data engineering consultancy practicing closed-loop data engineering: structuring data pipelines as feedback loops so data quality issues are found and fixed by the loop, not by users. We build the core datasets that power an AI-native business — the better the data, the better the agents. Founded in 2025 and based in the United States. Contact hello@promptformr.ai or visit https://promptformr.ai.

## The Method: Closed-Loop Data Engineering

Most pipelines are built open-loop: write transformations, ship, and find out from users when the data is wrong. Agents built on wrong data give wrong answers. We structure pipelines as feedback loops:

1. **Define the core data model** — The entities, relationships, and invariants the business runs on, written as an executable contract: schemas, types, keys, allowed values.
2. **Define the target state** — Each pipeline gets concrete acceptance criteria (e.g. match rate above 95%, totals reconcile to the source system, data fresh within 24 hours).
3. **Build regression tests, not just pipelines** — Golden datasets and expected-output snapshots run on every change and every load. When a source or transformation changes, the diff shows what broke and where.
4. **Observe against the target state** — Monitoring scoped to the target-state criteria and data quality tests. An alert means the data is diverging from correct, not just that a job failed.
5. **Close the loop** — When a check fails, automated loops localize the failing transformation, propose a fix, and validate it against the regression suite. An engineer approves what ships.

## Example Pipelines (Anonymized)

- **Healthcare — Privacy-Preserving Record Linkage**: Encoding and matching pipelines that link patient records across hospital systems without exposing PHI. Target state: cross-site match quality verified against known record pairs. Flow: clinical records → PPRL encoding → matched patient dataset.
- **Finance Ops — Invoice & Receipt Processing**: Emailed receipts and invoices parsed into amounts, dates, vendors, and GL hints. Target state: extracted totals reconcile against statements before entries post. Flow: email inbox → extraction → validated ledger entries.
- **Marketplace — Sports Card Transaction Tracking**: Photos of transacted cards parsed for set, player, grade, and serial number, matched to grading data. Target state: every transaction lands in inventory with computed cost basis. Flow: photos → parsing & matching → inventory and P&L dataset.

## Why PromptFormr

- **Engineer-led**: 9+ years building data automation at big-data scale. AI writes the first draft of a fix; an engineer approves what ships.
- **Private & Secure**: On-premises and private-cloud deployments for regulated data, with PII masking and privacy-preserving record linkage where required.
- **Fixed scope**: Engagements are scoped to a pipeline and its target state, and plug into existing systems. No platform to buy.

## Contact

- **Email**: hello@promptformr.ai
- **Website**: https://promptformr.ai
- **LinkedIn**: https://linkedin.com/in/chris-cardwell-84a970123

## Optional

- [Sitemap](https://promptformr.ai/sitemap.xml)
- [AI Info](https://promptformr.ai/ai.txt)