BGK Consulting — AI Workflow Engineering

Turn document-heavy, manual workflows into measurable, human-supervised software.

I help operationally complex companies connect unstructured information — emails, PDFs, specs, spreadsheets — to the systems they already run on. Senior engineering leadership from discovery through production, not a generic AI platform bolted onto your process.

What this is — and isn't

Most AI advice starts with a chatbot. I start with the workflow: where information gets stuck, where a decision needs a person, and where a system can reliably take over the repetitive parts.

Unstructured to usable Extract and validate structured data from documents, emails, and forms your team already handles by hand.

Human-supervised, not silent Consequential decisions — pricing, compliance, purchasing, safety — stay with a person. The system flags uncertainty and routes for review.

Built to be operated Evaluation tooling, monitoring, and recovery paths are part of the system from day one, not deferred cleanup.

How an engagement works

I favor bounded discovery and pilot engagements with explicit success — and stop — criteria. A strong engagement usually has:

A real operational bottleneck, not a hypothetical one

An executive sponsor and a day-to-day process owner

Representative historical data and enough volume to justify the work

A measurable baseline and a specific target — faster turnaround, fewer incomplete packets, fewer corrections

Clear boundaries around security, integrations, and decision authority

A recent example

On a recent engagement, I helped a small cross-functional team replace a bounded, brittle part of an established workflow platform with an AI-first system — from prototype, to user testing, to production in about four months.

~2 min → ~50 sec

Key review step, per case

60% → 100%

Required fields attempted by extraction

~94%

AI-populated fields accepted without edits

The 94% figure reflects field acceptance without edits, not independently verified accuracy, and "full coverage" means every required field was attempted — not that every prediction was correct. The result came from AI extraction paired with a better review experience, and included the evaluation tooling, dashboards, queue monitoring, and data-auditing systems built alongside it. Company, customer, and industry details are kept confidential.

Working style

Engineering-led and evidence-driven. Direct about uncertainty and technical risk. Comfortable with legacy systems and ambiguous requirements. Conservative about letting AI make consequential decisions on its own. If the evidence doesn't support moving to production, the right call is to stop or narrow the scope — not to keep going.

Tell me about the workflow

A few sentences is plenty to start.

Prefer to talk first? Call 816-590-4351.