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renvane
Enterprise AI for manufacturing, logistics & engineering
Consult / Build / Educate

Adopt AI only
where it drives revenue.

Renvane finds the few areas where AI systems can significantly pay off in your business, builds the tools to capture it, and trains your team to own them.

Start a conversation See what we've built
Why Renvane

We make AI adoption practical, measurable, and built to last.

We build systems that take the mundane, error-prone busywork off your team's plate, so they spend more time on what they do best. Every build is tailored to your specific needs and the way your team already works, and backed by real-time support as you adopt it. Here is how we work:

01 · Consult

Find the leverage

We map where AI is worth the investment in your business and build a clear, sequenced plan, starting with the workflow that returns the most, fastest.

02 · Build

Ship the tools

We design and ship custom tools that are simple and effective. We automate as much of the task as we can and simplify the rest, so your team gets the result with almost nothing new to learn.

03 · Educate

Hand over ownership

We train your people to own what we build, so the capability stays in-house and keeps compounding long after the engagement ends.

Solutions

Tools we've shipped for real, repeated work.

Every engagement ends in something that runs. Below are a few of the tools Renvane has built, each aimed at a specific, repeated task that was quietly draining a team's time.

Government construction · Texas

HSP Vendor Finder

Every state bid requires a Hub Subcontracting Plan proving good-faith use of HUB-certified subcontractors, found by hand, one trade code at a time, across the Texas CMBL database. Hours of searching before a single bid could go out.

What we built

An automation that searches CMBL by trade code, verifies each vendor's description actually matches the work, confirms certifications are active and not expiring, prioritizes by location and category, and outputs bid-ready vendor sheets organized by code, flagging the vendors that cover more than one. The team gets a bid-ready plan in minutes and spends its time winning the bid, not hunting for vendors.

~8 hrs → under 1 hr per bid · delivered and in use
See how it's built →
Afri Investment PLC · Industrial pumps, East Africa

B2B Outreach Engine

An industrial-equipment company wanted to reach qualified buyers across 13 East- and Central-African markets, with no scalable way to run high-quality, personalized outreach at volume.

What we built

A multi-agent pipeline with a vetted prospect database and an AI writer that personalizes every message around the prospect's own projects, never fabricating, behind a human-in-the-loop review gate, so each email and message is approved before it ever sends. The team stays focused on building and delivering, while the system researches the market and grows the pipeline in the background with minimal effort.

~110 hrs saved est. manual research & writing across 340+ prospects · human-approved sends
See how it's built →
KareApp · Food distribution, VA / DC / MD

Invoice Builder

A food distributor delivering to ~50 stores bills every store twice a month from a hand-kept delivery spreadsheet, where store and product names are typed inconsistently, invoice-number columns are corrupted, and prices go missing. Turning that log into Zoho invoices meant re-keying every line by hand, with ~$1.2M of yearly billing riding on a manual copy step where one wrong line becomes a disputed bill.

What we built

An AI-powered tool that reads the raw delivery spreadsheet and rebuilds it into a Zoho-ready import in minutes, one invoice per store per billing period. A Claude-driven matching engine intelligently resolves every messy store and product name against the master catalog, auto-corrects the safe data problems, and reasons about anything ambiguous, surfacing its best guess with a confidence score behind a human-review gate, so a wrong invoice is never exported silently. The model learns from every confirmed decision, so recurring data quirks stop needing review and the system gets sharper with each cycle.

hours → minutes per billing cycle · ~100 hrs/yr saved · validated on live billing data
See how it's built →

Let's find the workflow worth automating first.

Tell us where the work slows down. We'll tell you whether AI can fix it, and what it would take.

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