Data engineering & retrieval
DeliveredPipelines from spreadsheets and legacy tools into clean databases, with search that answers from your own documents.
You receiveDocumented data model and search index with citations
Layer 2 · Optimize
Models & Inference
We compare retrieval, model adaptation and conventional software against your defined task, then make the winner fast, affordable and testable.
DeliveredBacked by delivered work.Pilot programFixed-scope, fixed-price first engagement.
What we offer
Domain-specific AI engineering
Pilot programEvaluate retrieval, fine-tuning and conventional code against one clearly defined business task.
Pilot deliverableEvaluated task-specific system and deployment pipeline
Inference optimization & QA
Pilot programBenchmark model workloads, find bottlenecks, and trade off speed, cost and output quality with evidence.
Pilot deliverableReproducible benchmarks and a tested optimization plan
Evidence
All case studiesDelivered work in this practice
Manufacturing & global trade
Shine Hair International: end-to-end business digitalization
One platform for a US hair company and its overseas production network, replacing spreadsheets and messaging threads.
In deliveryRead case study →
Healthcare
Health1Pro: on-demand healthcare coordination
Patient registration, around-the-clock doctor access, advocacy and payments for a Southern Arizona provider.
LiveRead case study →
HR & finance
Payroll platform with IRS reporting
Multi-employee payroll with W-4 withholding and Form 941, 940 and W-2 output.
LiveRead case study →
FAQ
Common questions
Do we need to fine-tune a model?
Often not. VACL compares retrieval, fine-tuning and conventional software against your defined task and recommends whichever performs best for the cost.
How do you measure model quality?
With evaluation sets built from your real examples, tracked pass rates and regression tests that run before any model change goes live.
The rest of the stack