The method is the product.
Every engagement follows the same shape, because the shape is what prevents the failures the research describes: pilots that never reach daily use, graphs that stall at entity resolution, and data that decays as soon as it is loaded.
Checked before the next begins.
In order, every time.
Count first
Before any processing, we inventory what you have: how many files, of what type, how many pages, how many are duplicates by content. We confirm the numbers with you. A surprising share of “we have 500 documents” turns out to be 380 unique ones and 120 copies, and the estimate, the price, and the plan change accordingly.
Sample before scale
We process a representative sample and show you the result: extraction quality, the records produced, the gaps. If your documents come in six formats, we say so and adjust before the full run rather than discover it at the end.
Define the structure with you
The ontology (sites, assets, contracts, whatever your domain needs) is agreed in writing before extraction starts. Field lists are explicit. Every field is either filled from the document or left blank; nothing is guessed to make a table look complete.
Dry run before any write
No record is written to your systems until you have seen exactly what will be written: how many, to which parents, which fields, and which rows could not be matched and why.
Verify from the target
After a write, we query the target system and report its counts, not ours.
Report the failures
Every run produces a report of what could not be processed, what was flagged for review, and what was skipped. It is in the summary, not in an appendix.
Leave it runnable
Everything is a repeatable, documented pipeline. Nothing is a one-off.
Four things we will decline, in writing.
Saying no early is cheaper than saying sorry late. These are the lines we hold on every engagement.
We do not train models on your data
Or anyone else’s. We use commercially available models under contract terms that exclude training on your inputs, and we choose models per task on accuracy and cost, not brand.
We do not scrape behind logins
Or bypass rate limits or bot protection, or collect personal data without a lawful basis. We will decline the work.
We do not sell dashboards of unverified data
If we cannot count it, we will not chart it.
We do not deliver a demo and leave
Every engagement ends with something running in your environment, documented, with your team able to operate it.
Start small. Each step useful on its own.
Fixed prices where the scope is known. Real numbers before the scope grows.
Assessment
Start here1 to 2 weeks · Fixed price
We inventory your sources, sample them, and return a written report: what is there, what can be extracted with what confidence, which questions it could answer, what it would take. Useful on its own even if nothing follows.
Pilot
3 to 6 weeks · Fixed price
A bounded slice: a few hundred documents, one or two API sources, one target system. You get the extraction quality report, a working assistant over the sample, a dry run against a sandbox of your target system, and a proposal for the full build with real numbers.
Build
Delivered in stages · Quoted after the pilot
The full scope, each stage accepted before the next starts. Priced on volume (documents, pages, records, sources) and complexity.
Operate
Monthly · Includes a fixed allowance of refinements
New sources processed as they arrive, assistants kept current, connectors maintained through upstream changes, monitoring, and a monthly summary of what changed.
Advisory
As needed · For teams building it themselves
Ontology design, pipeline review, evaluation design, and an honest opinion on what will and will not work.
Count first. Then decide.
Book an assessment. In one to two weeks you will know what you have, what it can answer, and what it would take. Fixed price, and yours to keep whatever you decide next.