AI & Intelligent Systems
AI without the hype.
AI is a much wider field than chatbots. We find the tool that actually fits your problem - and costs the least to keep running - then build it into a process your business can rely on. And we'll tell you up front if you shouldn't build one at all.
For Decision-Makers
The right tool. Not the flashiest one.
Most "AI consultants" - and plenty of AI vendors and startups - are selling the same tool: a thin wrapper around a frontier model API, promised as the answer to every problem. But AI is a wide field, and most business problems don't need the flashiest tool. They need the right one.
We start from your problem, not our favorite technology, and pick the approach that solves it well and costs the least to maintain. The result is an intelligent system: a machine built to one job, on your data, that your team can rely on. That's true whether you're an early-stage founder or running an established operation.
What "AI" actually spans
The field is bigger than chatbots. We work across all of it.
- Small language models - trained to one task, on your data
- Classical machine learning - prediction, classification, estimation
- Bayesian statistics - answers that come with confidence attached
- Optimization & deterministic logic - when the best tool isn't a model at all
Picking between these well is the job. That's what you're hiring.
Honest Work
Machine learning isn't linear. We won't pretend it is.
Software has a spec: build it and it works. A learning system is different - nobody can honestly promise an accuracy number before working with your data. So we don't. We build, measure against a bar we set together, and decide with real numbers on the table.
Build
A working system on your real data - not a slide deck about one.
Measure
Against a bar we agreed on before modeling started. Including where it fails.
Decide
Production, keep iterating, or stop. All three are honest outcomes.
Anyone promising certainty before working with your data is selling something. The feasibility gut-check is free - it happens in the intro call.
How We Build
Serious tools, chosen deliberately
For the technical reader - and for the owner who wants to know what they'd be paying for. Every tool below earns its place by being the cheapest thing that solves the problem well.
Domain-specific small language models
A small model trained to your task usually beats a big model prompted at it. It's cheaper to run, faster to respond, and your data stays yours. We reach for a frontier API when it genuinely wins - not because it's the default.
Classical machine learning
Gradient boosting and regression solve most business prediction problems. They're transparent, cheap to retrain, and battle-tested. We don't reach for a neural net when something simpler wins.
Bayesian methods
When a decision has real money behind it, you need to know how confident the model is - not just its best guess. Bayesian statistics and Bayesian neural networks give you calibrated uncertainty you can act on.
Composed systems
Real intelligence in a business process is rarely one big model. It's usually several small models and some deterministic logic, each doing one job well - easier to test, cheaper to fix, and it fails loudly instead of confidently.
Data Privacy
Your data stays your data
Here's what it really means to build on a frontier model: every request sends your business data - and your customers' data - to someone else's servers, under their terms and their retention policies. Data is their business. That's not a scandal, but it is a decision, and most vendors make it for you without saying so.
Our systems are designed to keep your data your data. Models built to your task can run where your data already lives, and what they learn belongs to you. When a frontier API genuinely is the right tool, we use it with eyes open: the minimum data it needs, and your customers' privacy designed in from the start.
What that means in practice
- Purpose-built models can run on your infrastructure, so sensitive data never leaves it
- You own the model and everything it learned from your data
- No customer data used to train someone else's product
- Frontier APIs only where they clearly win, with the minimum data required
- Where your data goes is documented, so you can answer when your customers ask
How It Works
From question to reliable system
No paid discovery theater. The first conversation is free, and every step after it has a decision point you control.
Intro Call
Where everything at Pollex starts
A free 30-minute call. Bring the process you think a machine could do. You'll get an honest first read on whether it should - and sometimes the answer is a rule and a spreadsheet.
What We'll Cover
- The process you want a machine to take over
- What data you have, and what it would take to get more
- Which class of tool fits - and which don't
- What "good enough" would need to mean for your business
- Whether a pilot is worth your money
Investment
Free
Duration
30 minutes
Outcome
A first-pass answer on whether a machine should do this job, what data it would need, and what a pilot would look like. If the honest answer is "don't build this," you'll hear it here - for free.
Pilot Build
Prove it on your real data
Machine learning isn't linear like software - nobody can promise an accuracy number before the work starts, and you should be suspicious of anyone who does. So we build a working system on your real data, measure it against a bar we agree on up front, and then make a clear-eyed decision together.
How It Works
- Agree on the evaluation bar before any modeling starts
- Build on your real data, not a polished demo set
- Measure against the bar - including where it fails
- Weekly check-ins with results you can read
- Decision point: production, iterate, or stop
Stopping is a valid outcome. It's much cheaper here than after launch.
Investment
Scoped per project
Duration
4-8 weeks typical
Outcome
A working system with honest numbers attached, and a decision you can defend: put it into production, keep iterating, or stop.
Production + Support
Make it part of the business
A model that works in a pilot isn't done - it has to live inside your real workflow, and it has to keep working as your business changes. We integrate the system, put monitoring around it, and keep it honest over time.
What's Included
- Integration into your existing tools and workflow
- Monitoring and alerting on model performance
- Retraining cadence as your data changes
- Failure handling designed for the misses, not the demo
- Plain-English reporting your whole team can read
Investment
Retainer-based
Duration
Ongoing
Outcome
An intelligent system your team actually uses, with monitoring that catches drift before your customers do.
The Honest Part
What other consultants skip
Intelligent systems carry costs that don't show up in the demo. You should know them before you build - here's what we tell every client up front.
It needs your data
A model is only as good as the examples it learns from. If the data doesn't exist yet, the first project is collecting it - and we'll tell you that before you spend a dollar on modeling.
It drifts
Your business changes, and a model trained on last year slowly gets worse. Production systems need monitoring and a retraining cadence. That's a real maintenance cost, and we price it honestly.
It will be wrong sometimes
Every model has a failure rate. The question is whether you know what it is, and whether the process around the model catches the misses. We design for the failure case, not the demo.
Sometimes a rule beats a model
If a spreadsheet and three if-statements solve it, that's what you should build. It's cheaper, it's auditable, and it never drifts. We'll say so in the intro call.
Rented intelligence has a landlord
Building on someone else's API means their pricing, their outages, and their data policies. Sometimes that trade is worth it - but it should be a decision, not a default.
Where This Works
Problems that fit
If a decision gets made over and over, with data behind it, a machine can probably help. These are the shapes we see most.
Forecasting & Demand
"How much stock do we actually need in March - and how sure are we about that number?"
Document Processing
"Pull the numbers that matter out of 900 vendor invoices, in whatever format they arrive."
Quality Control & Anomalies
"Flag the units - or the transactions - that don't look like the others, before they become a problem."
Pricing & Estimation
"What should this job quote at, given the last 500 jobs and what they actually cost?"
Triage & Routing
"Which of the 400 support tickets that arrived overnight need a human first?"
Decisions Under Uncertainty
"Not just a prediction - how confident should you be before betting real money on it?"
Don't see yours? The shape matters more than the industry. If you're not sure it fits, that's exactly what the intro call is for.
Wondering if a machine should do it?
Start with a free intro call. Worst case, we save you from building the wrong thing.