Practical AI for Companies That Build.
Strategy through production for PE portfolio companies and operators who need AI to move the P&L this quarter — not the next pilot committee meeting. Vendor-neutral. Fixed-scope. Measured in margin, revenue, and cycle time.
The opportunity is large. The execution gap is larger.
The math on AI works at the company level. It breaks at the project level — POCs that don't survive production, costs that don't scale linearly, governance that doesn't exist until something goes wrong. We close that gap.
Six outcomes. One engagement model.
Each service is named for the business outcome it produces, not the technology behind it. Fixed scope, fixed fee, board-defensible measurement.
Margin Expansion
Automate the repetitive knowledge work absorbing senior talent. Compound operating leverage quarter over quarter without growing headcount linearly with volume.
Cost Reduction
Replace expensive per-seat SaaS and outsourced functions with AI systems you own. Predictable spend; no compounding licensing tax as you scale.
Revenue Lift
AI-driven top-line: enriched leads, personalized outbound at scale, opportunity scoring, faster deal cycles. Pipeline outcomes, not better dashboards.
Risk Reduction
Governance, evaluations, and guardrails before AI hits production. Risk-tier framework per use case; evaluation harness; audit logging that holds up under scrutiny.
Talent Leverage
Internal copilots that make senior employees 2–3x more productive. Codebase, knowledge-base, customer-history, financial-model copilots — wherever your top performers spend time.
Speed-to-Market
Agentic workflows that compress engineering and operational cycles. Multi-step automation replacing multi-team meetings. Features ship in days, not quarters.
From diagnostic to production, on your value-creation timeline.
Diagnose
Operating-model review and AI opportunity map. Where does AI move the P&L, and where is it theater? 1 week.
Strategy
Board-ready plan: build vs. buy vs. integrate, sequencing, ROI model, governance posture. 90/180/365-day roadmap. 2 weeks.
Build
Engineering execution from POC to production. We close the gap that kills 80% of AI projects. 4–12 weeks per use case.
Operate
Evals, guardrails, cost control, continuous optimization. AI that keeps working as your usage scales. Ongoing.
Wall Street rigor.
Hands-on engineering.
Vector Engineering combines a Wall Street finance background, multi-portfolio operating experience, and hands-on full-stack engineering. We've written the code, run the P&L, sat through the sponsor reviews — and we know which AI initiatives compound and which ones become next year's line-item embarrassment.
Engagements are vendor-neutral by default. We have no incentive to push you toward one cloud, one model provider, or one SaaS — and we'll tell you when the right answer is to do less, or buy the off-the-shelf option, or wait six months for the landscape to settle.
NDA at intake · SOC 2 storage · Per-engagement key isolation · Zero retention post-engagement
Common questions.
Where should our company start with AI?+
Start where the unit economics already point. The highest-leverage first deployments are usually internal: a copilot for the role with the highest fully-loaded cost, an automation for the workflow with the most repetition, a replacement for the SaaS line item growing fastest. We start with a one-week diagnostic that ranks your candidate use cases by margin impact and execution risk, then build the first one.
Why do 80% of AI projects fail to reach production?+
Most projects die in three places: a POC that impresses in a demo but can't survive real input distribution, an evaluation gap that lets quality regressions ship undetected, and a production-cost surprise that makes the system uneconomic at scale. We design for those three failure modes from day one — eval harness in CI, cost-per-transaction tracked alongside accuracy, deployment plan written before the POC runs.
Build vs. buy: how do you decide?+
Build when you have the data, the volume, and a workflow that's strategic to own. Buy when the function is generic and the vendor's improvement velocity beats yours. Integrate when the answer is both. We deliver a build/buy/integrate matrix for your specific stack rather than a default recommendation — the right call differs by company and changes over time.
How do you measure AI ROI?+
Pre-engagement we baseline the current cost or revenue of the target workflow: cost-per-ticket, sales-cycle days, contract review hours, etc. Post-deployment we measure the same metric at the same cadence. Reports are designed to be defensible to a PE sponsor, a board, or an audit committee — no hand-waved 'productivity gains.'
Do you work with PE-backed companies?+
Yes — most of our engagements are with PE portfolio companies in the 12-36 month value-creation window. We're vendor-neutral, equity-considered when the fit is right, and used to working with sponsors directly on the value-creation plan.