01 · Assess
Find where AI moves the P&L.
We map the workflows that drive revenue and cost, size the opportunities, set a baseline, and sequence what to build first. One company, or several at once through a private equity sponsor.
What this includes VVG TakeDaily application briefAI-assisted assessment
AI engineering and operations
We engineer and operate agentic AI systems that grow revenue and margin.
We build AI agents into the work that moves money: quoting and estimating, collections, claims and document-heavy review. Then we keep them running, measured against a baseline you agree to.
Start with your problem
What are you trying to fix?
Describe it in your own words. We match it to what we do by meaning, not keywords, and show you the relevant parts of this site.
What we do
Assess. Build. Operate.
Most AI pilots never reach the P&L. We start where the money is, build the system into the work itself, and stay accountable for keeping it running.
02 · Build
Put agentic systems into the workflows that make money.
Quoting and estimating, receivables, document-heavy review, decision records, and legacy systems nobody can explain. Agents do the defined work; your people approve what matters.
What this includes03 · Operate
Keep it running and improving.
Monitoring, fixes, evaluation against the baseline, and migration. We run it, or hand it to your team with the procedures to run it.
What this includesWhere it pays off
Typical first workflows, by industry. Each starts with one workflow, a measured baseline and a human approval step.
- Healthcare
- Claims and denial rework, prior-authorization packets, billing follow-up.
- Financial services
- Loan and account-opening file review, compliance evidence, client reporting.
- Construction
- Estimating and bid turnaround, pay applications, change orders, RFIs and submittals.
- Manufacturing
- Quoting from drawings and specs, quality records, supplier and order follow-up.
- Private equity
- An assessment across portfolio companies, then the first build where the return is clearest.
Human-led. Agent-powered.
Specialist AI agents research, build, test and challenge the work in parallel. People set the goal, make the calls and approve what goes live. Access controls, audit trails and human approval are designed in from the start, and a named lead is accountable for the result.
01
Ideation
Research teams explore the problem, compare approaches, and surface opportunities.
People define the business goal and choose the direction.
02
Development
Specialist teams build in parallel while testing and adversarial teams challenge the implementation.
People guide architecture, review evidence, and resolve tradeoffs.
03
Deployment
Cloud teams prepare deployments, verify releases, and monitor health and performance.
People approve readiness, release scope, and operating responsibility.
Measured against a baseline.
Before we build, we agree what success means and measure where you are today: cycle time, accuracy on representative work, defects and rework, and acceptance results. Then we measure the system against the same scope and criteria.
Meet the research, engineering, testing and cloud teams behind the work.
Meet the engineering studioLed by Dr. Jerry A. SmithNavy nuclear engineering discipline, a PhD in Computer Science, and accountability for the result
Start with a defined scopeOne workflow, agreed inputs, clear acceptance
We operate what we shipMonitoring, fixes, and a clean handover when you want one
Proof
Systems we built and ran.
Two systems delivered for a large private equity firm, then working prototypes, with the stage of each made clear.
Delivered system
Permission-aware institutional decision memory
Decisions that draw on what the firm already knows: what it decided before, why, and what happened next. Every answer is sourced and limited to what the person asking may see.
For a large private equity firm: 90 days from an empty index to a production system on the firm's real records, used live by its leadership.
Read the engineering storyDelivered pilot
Meeting intelligence connected to the record
Turn a conversation into a usable record of decisions, actions, questions, and figures to verify.
For a large private equity firm: ran 41 consecutive days unattended with zero incidents.
Read the engineering storyWorking prototype
From an RFQ to a reviewable quote
Extract requirements, identify gaps, and draft a response using approved business context.
Read the engineering storyDomain prototype
Source documents to structured reports
Assemble cited report sections, tables, and supporting images from a collection of source documents.
Read the engineering storyTry it yourself
See the systems at work.
Interactive examples with sample data: turn a request into a quote, find out why an invoice is overdue, or follow how decisions become memory.
Intelligent RFQ
RequestRequirementsQuote draft
Interactive simulation · Sample data
Faster quotes: from incoming request to a reviewable draft.
Choose an industry and follow a request through extraction, analysis, and a draft quote.
Try the example
About 1 minute · No signup
Receivables intelligence
InvoiceDisputeNext action
Interactive simulation · Sample data
Collect cash sooner: find the story behind an overdue invoice.
Trace a balance to its source, explore a dispute, and see a suggested collections priority.
Try the example
Explore in 2 minutes · No signup
VVG Cortex
EvidenceDecisionLearning
Interactive architecture · Illustrated pathways
One memory your leadership can question and trust.
See how a question moves from your documents, through the record of past decisions, to an answer your leadership can act on, and how the outcome is remembered for next time. Every answer is sourced and permission-checked.
Try the example
Explore in 2 minutes · No signup
For delivered work, scope, and implementation details, read the case studies.
Cortex in 60 seconds
Your AI can read your documents. Can it remember your decisions?
Most AI tools search documents. Cortex also records what your team decided, why, and what happened next. The next decision starts from what you already learned, and a plan that repeats an old mistake gets flagged.
Explore CortexHow an engagement runs
A working system, and someone to run it.
01
Define the workflow
Agree on the owner, inputs, output, and acceptance criteria. Establish a baseline on representative cases.
02
Build and evaluate
Implement the workflow and integrations. Review working increments together and test useful results, errors, and exceptions.
03
Deploy and operate
Roll out with monitoring and operating procedures. We keep it running and improving, or hand it to your team with everything needed to run it.
Start with one workflow.
Tell me what your team does today, where the work gets stuck, and what a useful result would look like. We will use a short call to identify a sensible next step.