Answering the same questions all day
AI does
Answers on the website, WhatsApp or e-mail from your own documents, in several languages, and hands complex cases to a person with the context attached.
You keepRefunds, exceptions and VIP customers
AI systems · Racool Studio
Let AI do the repetitive work. Your team keeps the decisions.
We design, build and run AI systems that take hours of manual work off your people — with measured accuracy, human approval where it matters and a clear plan for after launch.
What AI can take off your team’s plate
Concrete jobs, not buzzwords. For each one the AI does the heavy lifting and a person stays in charge of the outcome.
Qualifying leads and keeping the CRM current
AI does
Reads every inbound request, scores it against your ideal customer, drafts the first reply and logs everything in the CRM.
You keepThe call and the deal
Typing data from invoices, receipts and forms
AI does
Extracts fields from PDFs and photos, checks them against orders and flags anything that does not match.
You keepApproving payments
Compiling the weekly report
AI does
Pulls numbers from your systems every week, writes the summary and highlights what changed and why.
You keepWhat to do about it
Writing descriptions, posts and translations
AI does
Drafts product descriptions, SEO pages and social posts in your brand voice and translates them — thousands at a time.
You keepFinal edit and brand tone
Attendance, timesheets and shift rules
AI does
Verifies check-ins (WiFi, GPS, approved device), applies lateness and overtime rules and exports payroll-ready data.
You keepDisputes and policy
Quality checks and paper logs
AI does
Turns photos and checklists into structured records, spots defects or missing steps and alerts the supervisor.
You keepStopping the line
Searching for answers in scattered files
AI does
A private assistant over your documents, contracts and procedures that answers with the source attached.
You keepThe decision
Real systems we have shipped
Every row is a product we designed and built — open it for the problem, the solution and the result.
If your team… writes descriptions, tags or translations for thousands of files Vision + LLM content pipeline with batch delivery Rastock AI → If your team… tracks attendance on paper and argues about timesheets HR & attendance app: check-in only on factory WiFi + GPS + approved phone, lateness rules applied automatically HR Attendance → If your team… answers the same product questions by phone all day Product catalogue site with specs, FAQ and quote requests routed to sales Magna → If your team… wants customers to log data without typing Photo-to-data AI: snap a photo, the app fills in the details (meal → calories) Laren → If your team… is invisible when buyers ask ChatGPT or Perplexity AEO program: structured content, schema and AI-visibility tracking Shukurlu Design →
How we make AI work at the quality your business needs
AI demos are easy. AI that is right on Tuesday afternoon with real customers is engineering. This is how we get there.
- 01
Start from your real data
We collect a test set of your actual cases — emails, invoices, questions — before writing code. The system is judged on your work, not on a demo.
- 02
Measured accuracy before launch
Every version is scored against that test set. You see the accuracy, the failure cases and the cost per task before anything goes live.
- 03
A person approves what matters
Payments, customer promises and anything irreversible go through a human approval step. The AI prepares, a person decides.
- 04
Answers with sources, guardrails on top
Assistants answer from your documents and show where the answer came from. Out-of-scope or risky requests are refused or escalated.
- 05
Your data stays yours
We use business tiers of AI providers that do not train on your data, keep data in your cloud where possible and log who accessed what.
- 06
Costs modelled up front
You get the cost per task and a monthly estimate before we build, plus hard limits so a busy day never becomes a surprise bill.
How the system is run once it is live
An AI system is not a website you publish and forget. Models change, your business changes, data drifts. This is the operating plan we agree before launch.
Launch
Go live gradually
We start with a share of the real traffic or one department, compare against the manual process, then widen.
- Side-by-side with the old process
- Rollback in one click
- Team trained on day one
First 30 days
Hypercare
We watch every flagged case, tune prompts and rules daily and meet with your team weekly.
- Daily review of failures
- Weekly check-in
- Fixes shipped within days
Every month
Monitor and improve
A dashboard tracks accuracy, volume, cost and response time. We review it with you and ship improvements.
- Accuracy, cost and speed dashboard
- Monthly report with next steps
- New automations prioritised by hours saved
Every quarter
Model and security review
AI providers release new models and retire old ones. We re-test on your data, switch when it is better or cheaper and review access and security.
- Re-test on your test set
- Provider and model updates
- Access and security review
You own it
Code, prompts, test sets and data belong to you, with documentation and a handover — so your own team or another vendor can run it if you ever choose to.
Questions about AI projects
01 Will AI replace my employees?
In our projects AI removes repetitive tasks, not roles. People spend their time on customers, exceptions and decisions instead of copying data between systems.
02 How do we know it is accurate enough?
We measure it on a test set built from your real cases before launch and keep measuring after launch. If it is not good enough for a task, that task keeps a human in the loop.
03 Is our data safe?
We use business tiers of AI providers that do not train on customer data, keep data in your own cloud where possible, encrypt it and limit access by role.
04 What does it cost to run every month?
It depends on volume. Before building we calculate the cost per task (often cents) and a monthly estimate, and we set spending limits.
05 Which AI models do you use?
Whichever fits the task and budget — OpenAI, Anthropic, Google, Azure or open-source models in your own cloud. We stay provider-independent so you can switch.
06 Where do we start?
With an AI audit: we map one workflow, measure the hours it costs and show what can be automated, at what accuracy and cost — before you commit to a build.
Find the first job AI should take over.
In an AI audit we map one workflow, price the hours it costs and show what can be automated — with accuracy and running cost — before you commit.