AI & Data Engineering
We help teams put AI to work where it has a measurable payoff — document processing, search, forecasting and workflow automation — built on clean data pipelines and with humans kept in the loop.
Overview
Most AI projects stall for the same reason: the data isn't ready and the success metric was never defined. We start with both. If a simpler rules-based automation solves the problem, we will tell you.
When AI is the right tool, we build it to be auditable — with evaluation sets, confidence thresholds, human review queues and clear data-privacy boundaries.
What we deliver
Extracting, classifying and validating data from PDFs, forms and scanned documents.
Retrieval-augmented assistants that answer from your own policies and records, with sources.
Reliable ingestion, transformation and warehousing so reporting is trustworthy.
KPI dashboards and forecasting models your leadership team actually uses.
How an engagement runs
Use-case scoping
We size the opportunity, define the metric and check data readiness.
Proof of value
A two to four week prototype measured against real examples.
Productionize
Pipelines, evaluation, monitoring and review workflows built for scale.
Improve
Ongoing tuning based on feedback and measured accuracy.
Frequently asked
Is our data used to train public models?
No. We use enterprise API terms or self-hosted models with no training on your data, and we document exactly where data flows.
How do you measure accuracy?
We build an evaluation set from your real documents or questions and report precision and recall before and after each change.
What if AI isn't the right fit?
Then we say so. Many problems are solved faster and cheaper with straightforward automation, and we'll recommend that instead.
See it in practice.
An extraction and review pipeline that turns permits, inspection reports and invoices into validated structured data.
