Document extraction
Turn variable documents into constrained records with confidence, validation, and a review state.
Connect / AI Engineering
AI engineering inside a real operating workflow.
A San Diego AI engineering team building LLM agents, RAG pipelines, voice AI, and custom model integrations: extraction, qualification, routing, reporting, and internal assistance connected to the systems and data the work already depends on.
Production proof / Magnolia Medical Supply
A fax enters. A reviewable draft order leaves.
An inbound fax is stored, extracted into a constrained schema, confidence-scored, checked against compliance rules, and queued for human review. Unreadable clinical fields become null instead of a model guess.
Read the system case- Input
- RingCentral fax and stored PDF
- Extraction
- Forced tool use into a Zod schema
- Control
- Confidence scores and deterministic compliance checks
- Decision
- FAX_REVIEW status with human approval
Where it fits
Variable inputs. Controlled decisions.
These patterns are useful when the input varies enough to require interpretation, but the output can still be constrained, evaluated, and assigned. For production deployment, that operating path often includes Railway.
Qualification & routing
Classify requests, attach the evidence used, and pass the result to the correct queue or owner.
Internal assistance
Retrieve, summarize, and draft against business-specific sources while keeping the final action visible to an operator.
Reporting & enrichment
Combine records from connected systems, surface missing context, and produce structured outputs for downstream use.
Wrong fit
Use deterministic rules when deterministic rules solve the problem.
AI adds operational risk when the input and decision are already explicit. It is also the wrong fit when no one owns review, there is no representative evaluation set, or the workflow cannot tolerate an uncertain output.
Production method
Define, evaluate, connect, observe.
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Define the decision
Specify the input, permitted output, owner, failure state, and what success can be measured against.
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Build the evaluation set
Use representative examples and known edge cases to compare approaches before wiring the workflow.
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Connect the operating path
Integrate the selected model and safeguards with the records, queues, and systems already doing the work.
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Observe and review
Log outcomes, inspect corrections, and update prompts, rules, or routing from production evidence.
Questions
AI engineering decisions, answered.
Model choice follows the workflow, its evaluation criteria, and the risk of an incorrect output.
Have a repetitive decision or handoff