Aesthetic Logic Technologies Limited

10/05/2026

https://lnkd.in/p/dsWHBGKr

09/28/2026

Unpopular opinion: the lowest quote you get for custom software isn't automatically the best deal.
Two vendors quoting the same project can both be honest — the gap usually comes from unstated assumptions, not one of them overcharging. But sometimes a low number hides something:
→ Less senior engineers (more mistakes, more rework, slower decisions) → Maintenance billed separately later instead of built into the number now → A smaller scope than what you actually asked for
Before comparing on price alone, ask what's excluded. A $15,000 quote and a $150,000 quote can both be the right price — for genuinely different scopes.

Unpopular opinion: the lowest quote you get for custom software isn't automatically the best deal. Two vendors quoting the same project can both be honest — the gap usually comes from unstated assumptions, not one of them overcharging. But sometimes a low number hides something: → Less senior en...

09/23/2026

𝐖𝐞'𝐫𝐞 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐚𝐧 𝐀𝐈 𝐭𝐡𝐚𝐭 𝐫𝐮𝐧𝐬 𝐲𝐨𝐮𝐫 𝐝𝐚𝐢𝐥𝐲 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐠𝐫𝐢𝐧𝐝 𝐟𝐨𝐫 𝐲𝐨𝐮.
If you run a small business, you already know this pain:
A spreadsheet for leads.
A separate app for invoices.
A calendar for meetings.
Email for everything in between.
Nothing talks to anything else — and the thing that falls through the cracks is always the same.
The follow-up that never got sent.
We're building an AI Daily Business Assistant that puts all of it in one place — and layers an AI on top that drafts your next move instead of making you dig for it.
Not a chatbot that just answers questions.
An assistant that proposes the actual email, reminder, or follow-up — you approve, it sends. It never acts silently.
The full build is running end-to-end right now on demo data. Next step: connecting it to a real inbox, real calendar, real AI model.
𝐈𝐟 𝐭𝐡𝐢𝐬 𝐢𝐬 𝐚 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 𝐲𝐨𝐮'𝐫𝐞 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐝𝐞𝐚𝐥𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 — 𝐰𝐡𝐚𝐭 𝐰𝐨𝐮𝐥𝐝 𝐲𝐨𝐮 𝐰𝐚𝐧𝐭 𝐢𝐭 𝐭𝐨 𝐡𝐚𝐧𝐝𝐥𝐞 𝐟𝐢𝐫𝐬𝐭?
Full behind-the-scenes:

This link will take you to a page that’s not on LinkedIn

09/23/2026

Building custom software or a SaaS platform from scratch? Here's what we actually deliver, not the generic version:
— Architecture built for scale from day one, not retrofitted later — API-first, so it integrates cleanly with whatever you already run — Senior engineers only, fixed-scope milestones, no scope-creep surprises
If you're mapping out a build and want a second opinion on scope before committing budget, happy to talk it through — no pitch deck required.

This link will take you to a page that’s not on LinkedIn

09/20/2026

𝐇𝐨𝐰 𝐭𝐨 𝐬𝐜𝐨𝐩𝐞 𝐚𝐧 𝐌𝐕𝐏 𝐬𝐨 𝐢𝐭 𝐝𝐨𝐞𝐬𝐧'𝐭 𝐛𝐞𝐜𝐨𝐦𝐞 𝐚 9-𝐦𝐨𝐧𝐭𝐡 𝐩𝐫𝐨𝐣𝐞𝐜𝐭.

The fastest way to turn a 6-week MVP into a 9-month project: let every stakeholder add "just one more feature" before the first version ships.

The fix isn't saying no to every idea.

It's separating "needed to test the core hypothesis" from "needed eventually."

An MVP should be scoped around the one question you're actually trying to answer with real users.

Not a smaller version of the full product.

If your MVP list has more than one primary user flow, it's not an MVP yet.

𝐇𝐨𝐰 𝐦𝐚𝐧𝐲 𝐮𝐬𝐞𝐫 𝐟𝐥𝐨𝐰𝐬 𝐢𝐬 𝐲𝐨𝐮𝐫 "𝐌𝐕𝐏" 𝐜𝐮𝐫𝐫𝐞𝐧𝐭𝐥𝐲 𝐭𝐫𝐲𝐢𝐧𝐠 𝐭𝐨 𝐬𝐡𝐢𝐩?

09/18/2026

https://lnkd.in/p/gtiD_muU
A working demo and a working production AI system look almost identical to anyone who isn't deep in the technical details. That's exactly why AI vendor evaluation is so easy to get wrong.
Questions that actually separate the two:
→ "Which of these portfolio projects are running in production today vs. delivered and abandoned?" A vendor who talks post-launch monitoring and retraining has done this before. One who only talks about the initial build hasn't.
→ "What's the success metric, and was it defined before or after you picked a model?" If a vendor jumps straight to model selection, that's a real gap.
→ "How do you enforce permissions and produce an audit trail for actions the AI takes?" Matters most for agents connected to real internal tools, not just a chatbot demo.
→ "Can you walk me through the architecture of one production system — not just the result?"
Our own answer to that last one: our enterprise AI Copilot for a manufacturing client connects a RAG knowledge layer, an agent reasoning engine, and a permission-checked tool-execution layer — each with a specific, explainable role, not "we added AI to it."
Watch for one more signal: a vendor pushing a single large fixed price before ever seeing your data is asking you to absorb discovery risk they should be sharing.

09/12/2026

Most "AI lead scoring" pitches fall apart at one point: sales reps don't trust a black-box number they can't explain.
We ran into this building a lead scoring engine for a B2B sales team. Here's what actually worked:
→ Cut the noise first. Years of CRM history, but most of it wasn't predictive. We isolated the handful of signals that actually correlated with closed-won deals before touching a model.
→ Integration > architecture. A scoring model that needs reps to open a separate dashboard doesn't get used. We wrote scores directly into existing CRM fields — no new tool, no adoption curve.
→ Explainability closed the trust gap. Every score ships with its top 2-3 driving factors. Reps can see why, and override it when their read differs.
Result: faster time-to-first-contact on high-intent leads, and a model sales actually uses instead of ignoring.
If your team's CRM history is only being used for reporting, there's probably a scoring model hiding in it.

09/04/2026

https://lnkd.in/p/gCvzeihJ

𝟑 𝐬𝐢𝐠𝐧𝐬 𝐲𝐨𝐮𝐫 𝐁𝐈 𝐝𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬 𝐚𝐫𝐞𝐧'𝐭 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐛𝐞𝐢𝐧𝐠 𝐮𝐬𝐞𝐝. A dashboard nobody opens isn't a small problem. It's the same budget as one used daily — with none of the value...

09/02/2026

𝐇𝐨𝐰 𝐰𝐞 𝐛𝐮𝐢𝐥𝐭 𝐚 30-𝐜𝐥𝐚𝐬𝐬 𝐢𝐦𝐚𝐠𝐞 𝐜𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐞𝐫 𝐭𝐡𝐚𝐭 𝐡𝐢𝐭 93%+ 𝐭𝐞𝐬𝐭 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐟𝐨𝐫 𝐚 𝐜𝐥𝐢𝐞𝐧𝐭.
The brief: classify 30 distinct species across nearly 3,000 images - visually similar animals included, like telling a goat from a deer or a domesticated cow from a wild look-alike.
Instead of betting on one model, we benchmarked nine architectures against each other.
The winner wasn't a single model - it was an ensemble.
EfficientNetV2B0 + MobileNet + DenseNet121, combined.
95%+ validation accuracy.
93%+ test accuracy.
But the real work wasn't the model. It was everything around it: a six-step data-cleaning pipeline, 5-fold cross-validation, and a real path to production - Flask API, Docker, cloud-ready packaging.
A model in a notebook isn't finished. A model your team can actually deploy and monitor is.
𝐖𝐡𝐚𝐭'𝐬 𝐚𝐧 𝐢𝐦𝐚𝐠𝐞 𝐨𝐫 𝐝𝐚𝐭𝐚 𝐜𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 𝐲𝐨𝐮'𝐫𝐞 𝐬𝐢𝐭𝐭𝐢𝐧𝐠 𝐨𝐧 𝐭𝐡𝐚𝐭 𝐡𝐚𝐬𝐧'𝐭 𝐛𝐞𝐞𝐧 𝐭𝐨𝐮𝐜𝐡𝐞𝐝 𝐲𝐞𝐭?
Full breakdown: https://lnkd.in/gnVFM25r

08/31/2026

𝐎𝐩𝐢𝐧𝐢𝐨𝐧: "𝐀𝐈-𝐩𝐨𝐰𝐞𝐫𝐞𝐝" 𝐢𝐬 𝐝𝐨𝐢𝐧𝐠 𝐚 𝐥𝐨𝐭 𝐨𝐟 𝐰𝐨𝐫𝐤 𝐢𝐧 𝐭𝐡𝐚𝐭 𝐬𝐞𝐧𝐭𝐞𝐧𝐜𝐞.

Unpopular take: most "AI-powered" features I see pitched this year are a database query with better marketing.

That's not a criticism.

A well-placed database query solves real problems.

But calling it AI to sound current — when the actual value is just "we finally organized your data" — sets the wrong expectation.

It makes the real AI work look interchangeable with it.

The kind that needs a model.
Real training data.
A tolerance for being wrong sometimes.

Call the boring solution boring.

It still ships faster and costs less.

𝐖𝐡𝐚𝐭'𝐬 𝐭𝐡𝐞 𝐦𝐨𝐬𝐭 "𝐀𝐈-𝐩𝐨𝐰𝐞𝐫𝐞𝐝" 𝐩𝐢𝐭𝐜𝐡 𝐲𝐨𝐮'𝐯𝐞 𝐡𝐞𝐚𝐫𝐝 𝐭𝐡𝐚𝐭 𝐰𝐚𝐬 𝐫𝐞𝐚𝐥𝐥𝐲 𝐣𝐮𝐬𝐭 𝐚 𝐬𝐩𝐫𝐞𝐚𝐝𝐬𝐡𝐞𝐞𝐭 𝐢𝐧 𝐚 𝐭𝐫𝐞𝐧𝐜𝐡 𝐜𝐨𝐚𝐭?