Looking Glass
For SAP support teams
Say what you need, and it opens the right SAP screen with the document number filled in. It asks before anything can change data, and makes no network requests of its own.
AI-accelerated software engineering
Engineers drive.
AI accelerates.
AppVantix builds the tools and systems that give your company a leg up in the age of AI. Not AI wrappers and not vibe coding: real software, designed and owned by trained, certified engineers, and built faster because AI speeds up the work they check. Tools like Looking Glass, our own tool for SAP support teams.
- Not an AI wrapper
- Engineer-led
- Tests gate every release
Two ways to work with us
Use our product, or have us build yours.
Custom tools and systems
For companies that need something built
We engineer the tool your company needs. AI speeds up engineers who review and own every change, and you keep the source, the tests and the runbook. Start with a short, paid discovery.
What we build
Tools your company keeps, not a chat box you rent.
The companies that pull ahead in the age of AI will not be the ones with the thinnest layer over someone else’s model. They will be the ones with their own tools, built around how they actually work, and engineered to last.
Systems, not wrappers
Real software with AI where it earns its place
Deterministic logic, integrations, data flow and a gate before anything changes. A model is one component, never the whole product.
Yours to own
Source, tests and a runbook, handed over
You are not renting access to a prompt. You own a system your own people can run, change and trust.
An edge that compounds
Built around the work only you do
Tools that make your specialists faster at the thing that sets your company apart, which a generic AI product cannot copy.
The difference
Vibe coding hopes. Engineering knows.
AI can write code in seconds. Whether that code is correct, secure and maintainable is an engineering question, and it takes an engineer to answer it. An AI wrapper skips the question altogether: a thin layer over someone else’s model, easy to copy and easy to lose.
Vibe coding
- Prompt, accept, ship
- No written specification
- Nobody can explain why it works
- Tests, if any, written by the same AI that wrote the code
- No one accountable when it breaks
AI-enhanced engineering
- The specification is agreed first
- An engineer reviews every change and understands it
- Tests and evaluation run before every release
- AI drafts, people decide
- A named engineer is accountable for what ships
Why buyers are right to ask
62%of IT and technical leaders named security and privacy as a top concern with AI in DevOps
54%cited poor-quality or insecure code as a challenge of vibe coding
33%were concerned about unclear ownership of AI-generated code
EMA survey of 206 IT and technical leaders, as reported on PR Newswire. Survey findings, not a measure of every organization. Our answer is a process: review, tests, data-flow drawings, and source you own.
Behind the wheel
Real engineers steer. AI is the co-pilot.
AI drafts code, proposes tests and searches documentation faster than anyone. It does not understand your business, carry responsibility, or know when it is wrong. Our engineers do.
The engineer decides
- Architecture and security
- What is built, and what is not
- What ships, and why
- Accountability for the result
AI assists
- First drafts and refactors
- Test and edge-case suggestions
- Documentation and search
- Nothing ships on its own
- Educated engineers
- Certified engineers
- Accountable for every release
The engineering method
Five stages. Each one ends in something you can check.
01
Specify
Write down what it must do, and what it must never do, before any code.
02
Design
An engineer chooses the architecture, the data flow and the failure modes.
03
Build with AI
AI drafts and suggests. An engineer reads, rewrites and owns every change.
04
Verify
Tests and an evaluation on data we did not tune on gate every release.
05
Hand over
Source, tests, runbook and a plain list of limits. You own it.
One change, start to finish
A draft is not a release.
The robot drafts. An engineer reviews every change at the gate. Tests promote it. Only then does it reach release, and nothing skips a stage.
- Draft: AI proposes
- Reviewed: an engineer approves
- Tested: ready to release
What we do
Build it, decide on it, or buy ours.
Build
The tools and systems that keep you ahead
Internal tools, assistants over your documents and systems, workflow automation, search and triage, and integrations with what you already run, engineered with AI and owned by you.
Advise
A clear answer on where AI fits
Use-case review, build or buy, vendor and risk review, and evaluation design, so a decision rests on evidence and not on a demo.
Products
Tools we build and ship ourselves
Starting with Looking Glass, a desktop tool for SAP support teams, now in private beta with companies. It is the kind of tool we build for others.
A worked example
Our own product, checked in public.
We have no client names to show yet, and we will not invent any. Instead, here is how our method looks on something we built and ship ourselves.
Looking Glass finds the right SAP screen from plain words and opens it with the document number filled in. It never saves or posts anything, and it makes no network requests of its own.
275automated unit tests
160end-to-end checks
92 to 95%right answer in the top three, on test sets not used for tuning, against its built-in list
0network requests of its own, measured
Honest about it: it has been tested against a stand-in for SAP. The first results on a real SAP system come from the beta.
Before you ask
Fair questions. Straight answers.
“Our people can just use ChatGPT.”
They can, and so can your competitors. Everyone gets the same assistant and the same edge. We build tools around your own systems and rules, and you own them.
“Can we trust code an AI helped write?”
Trust the process, not the tool. A named engineer reads and answers for every change, and tests must pass before anything ships. AI drafts. It never approves.
“What happens when the AI gets it wrong?”
It cannot act alone. Anything that changes data or money stops at a fixed gate and waits for a person. We test on data we did not tune on, and show you the misses.
“Won’t we be locked in?”
No. You get the source, the tests and a runbook, and the model can be swapped. Nothing breaks because a provider raised a price or changed a prompt.
“How do we know it is worth it before we commit?”
You find out first. A short, paid, fixed-scope discovery ends in a written brief, an evaluation plan and a quote. Stop there if it does not add up.
“Where does our data go?”
Nowhere without your say. We draw the data flow first, name any model provider before a single byte is sent, and never train on your data.
Working with us
Start small, stop whenever it stops making sense.
Discovery
A short, paid, fixed-scope look
One problem, studied properly. It ends with a written brief, an evaluation plan and a quote.
Build
Delivered in small steps
A scoped application with tests at each step, so you see it working early and can change course.
Support
After handover
Fixes, monitoring and changes on a monthly basis, if you want them.
We quote after discovery and state the price before work starts. We do not charge for the first conversation.
Questions
Straight answers.
Is this just an AI wrapper?
No. A wrapper is a prompt and an interface over someone else’s model. We build engineered software: your logic, your data flow, your integrations and a gate before anything changes, with a model used only where it earns its place. You own the result, and it does not stop working when a model provider changes a price or a prompt.
Is this vibe coding?
No. Vibe coding means accepting whatever an AI produces and hoping it works. We write a specification first, an engineer reads and takes responsibility for every change, and tests gate each release. AI makes the engineers faster. It does not replace their judgement.
Do you use AI to build?
Yes, and we say so. AI coding tools are part of our process, under engineers who review everything they produce. What we rely on is a written specification and tests that run before every release, not trust in any tool.
Do you train models on our data?
No. When a project needs a model provider, we use terms under which your data is not used for training, and we tell you which provider before any of your data is sent.
Do you have clients we can ask?
Not yet to name. Our worked example is our own product, and we say so. If we ever publish a client’s name, it will be with their written permission.
Which models do you use?
We choose per project, from cloud models from providers such as Anthropic to open models that run on your own infrastructure, and write the choice into the data-flow diagram.
What do you not do?
We do not sell hype, guarantee savings, or build anything that acts on money or records without a confirmation step.
Where are you?
Lawrenceburg, Tennessee. We work with businesses across the United States.
Start here
Put an engineer on it.
Tell us the problem. A short note is enough. We reply within two business days, and the first conversation is free.