AI systems that take cost out of a business process
We build recommendation and search systems, forecasting, data pipelines, RAG and LLM features into the process you already run. A senior ML lead owns every delivery, a working demo arrives every week, and the code is yours from day one.
- Supplier0.99
- Example Supplies Ltd
- Invoice date0.98
- 14 Aug
- Total0.97
- $4,180.00
- VAT0.71
- $696.67
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What we build
Say what hurts. See the system that fixes it.
Six problems we are asked about most, and what the working system looks like. Screens show illustrative data.
How a project goes
You do not need to know how ML works. You need to know what will change and when.
- Step 1
A first call about the process, not the technology
Which process costs you money today, who works on it and what breaks when it goes wrong. If a rule, a report or better data entry would do, we say so and the call ends there.
First call · notesIllustrative data- Process: stock planning, 4 people, weekly
- Cost today: stockouts on top lines, cash in slow lines
- Data: 3 years of sales in the ERP
- Verdict: forecasting is worth a 2-week check
- Step 2
One number agreed before any model is trained
Forecast error, documents handled without a person, revenue per session: the number the system must move, and how it will be measured. It goes into the contract.
Agreed metricIllustrative dataForecast error (weighted)
Current method: 12.1%
Target: below 6% on a 12-week holdout
Measured weekly, written into the contract
- Step 3
A working demo on your data every week
Not a status report. Something that runs, from the second week on. Each milestone has written acceptance criteria, and you sign off before we invoice.
Weekly demosIllustrative dataWeek 2
Baseline forecast on real data
Week 4
Promotions and seasons added
Week 6
Order recommendations in the ERP
Week 8
Accepted: milestone signed off
- Step 4
Production with monitoring and a rollback switch
A dashboard on the agreed number, alerts when behaviour drifts, and a rollback you can trigger without us. A system nobody can roll back is not finished.
Production · this weekIllustrative dataForecast error 5.1%No drift detectedRollback to previous version - Step 5
Handover: the code was yours all along
The repository has lived in your organisation since day one: code, models, documentation and runbooks. We stay on for support if you want, and leave cleanly if you do not.
your-org / demand-forecastingIllustrative data- README.md
- runbooks/rollback.md
- runbooks/retrain.md
- models/
- pipelines/
- dashboards/
- tests/
Owned by your organisation since the first commit
- Process: stock planning, 4 people, weekly
- Cost today: stockouts on top lines, cash in slow lines
- Data: 3 years of sales in the ERP
- Verdict: forecasting is worth a 2-week check
Solutions and prices
Typical projects, with the price range up front.
Ranges come from the same rate card as the project calculator. The final price is fixed once the scope is agreed.
Recommendation systems
Show each shopper the products they are likely to buy, and prove the gain in an A/B test.
€14k–€42k2–4 monthsRAG copilots
An assistant that answers from your own documents and shows the source of every answer.
€8k–€18k4–8 weeksAI agents
Software that takes a request, does the steps in your systems, and asks a person when unsure.
€13k–€38k6–12 weeksDemand forecasting
Forecast demand per product and turn it into order quantities, so stock follows sales.
€10k–€28k1–3 monthsDocument processing
Read invoices, contracts and forms automatically, and send only the doubtful fields to a person.
€7k–€42kScoped on discoveryFraud detection
Score every transaction in real time and cut false alarms at the same catch rate.
€7k–€42kScoped on discovery
What changes
Before and after, in numbers.
Every figure links to the case it comes from and carries its evidence label: measured on a live system, estimated, or a reference design with typical values.
- Reference design
0%81%
Documents processed with no one retyping them
Document Intelligence (OCR, Extraction, RAG)
- Reference design
4.1%1.6%
False fraud alarms, at the same catch rate
Fraud Detection Pipeline + Monitoring
- Reference design
65.3%92.4%
Support tickets sent to the right team first time
Support Ticket Triage + Auto-Routing
- Reference design
12.1%2.7%
Forecast error in stock planning
Demand Forecasting + Inventory Optimization
Compare
Three ways to get an AI system. Each is right somewhere.
| Hire an ML team | Buy a SaaS tool | Work with SwankyForge | |
|---|---|---|---|
| First working result | Months: hiring comes first | Days to weeks, if the tool fits | A demo on your data in the second week |
| Fits your process | Yes, once the team learns it | As far as the product allows | Built around your process and data |
| Who owns code and models | You | The vendor | You, from day one |
| Cost | Salaries, every month | Subscription, every month | A pilot or an engineer from €5,000; full builds from a published rate card |
| After launch | Your team runs it | The vendor runs it | Monitoring and rollback handed over; support if you want it |
| Right choice when | ML is the core of your product for years | Your problem is standard and a tool already solves it | Your problem is specific and you want to own the result |
Your data
Your data and your code stay yours.
The repository lives in your organisation from day one
You own the code, the weights, the runbooks and the history. Nothing is held back until the last invoice.
Data stays where you keep it
We work inside your cloud or private network when that is the requirement. Your data is never used to train anything for anyone else.
Access by role, removed at handover
Project accounts get the least access the work needs, and they are closed when the project ends.
Monitoring and rollback ship before handoff
A dashboard on the metric you named, drift alerts, and a rollback you can trigger without us.
Insights
Articles for people who buy and build AI.
Each one starts with a short answer and ends with its sources.
- Article
When AI cuts the hours, a fixed milestone price on published rates splits the saving instead of hiding it
Hourly billing hands every saved hour to the client, an opaque fixed price hands it to the vendor; a worked example shows a middle that both sides can check.
11 min read - Article
With an AI pair in every repository, we automate the checks and keep five decisions human
How the engineering team works with AI coding assistants: what runs automatically on every change, what a person always decides, and what we have not measured yet.
8 min read - Article
Why every case study here carries an evidence label
Numbers on this site now say where they come from — measured, estimated or illustrative — and whether anyone will vouch for them.
2 min read
Questions
What business owners ask first.
What does an AI project cost?
Two ways to start: a fixed-scope pilot from €5,000 (2–4 weeks, written acceptance criteria), or a dedicated ML engineer from €5,000 a month under our lead's review. Each solution page shows the typical range for a full build.
How soon will we see something working?
A working demo on your data every week from the second week on. Most production systems land in 4 to 16 weeks.
What do you need from us?
Read access to the data, one person who knows the process, and an hour a week for the demo. You do not need a data team.
What if it does not work?
The number the system must move is agreed before any model is trained, and each milestone has written acceptance criteria. You sign off before we invoice. If the numbers say the project is not worth continuing, we stop and say so.
Who owns the result?
You do: code, models, documentation and runbooks, in a repository in your organisation from day one.
Do we need AI at all?
Sometimes not. If a rule, a report or better data entry moves your number more cheaply, we write that down. It costs us a project and saves you months.
Is our data safe with you?
Least-privilege access, isolated environments, and no training on your data for anyone else. We work inside your cloud when that is the requirement.
Where are you based, and who do we sign with?
Our engineers are in Minsk, Belarus, and work remotely with clients in Europe and the US, overlapping with CET business hours. The proposal names the contracting party and how payment works; contracts and payments go through Georgia.
Contact
Tell us about the task.
The first call is free and does not commit you to anything. You get a written reply within one working day.