LLM Integration
Claude, GPT, and open-source LLM integrations for intelligent chatbots, copilots, and content tools.
LLMS · ML · NLP · AUTOMATION
AI Process Automation
We automate the repetitive work with AI: processing documents and requests, LLM chatbots and copilots, demand forecasting, computer vision. We start from the use cases that pay back fastest — not from the technology.
About this service
What it is and how we do it
AI automation is not about "adding a neural network" — it is about removing repetitive manual work from your processes. We start by finding where your staff lose time: retyping data from invoices and delivery notes, answering the same customer questions, sorting requests by hand, checking photos and documents. From those tasks we pick the ones where AI pays back measurably, and we do the arithmetic before development starts.
Then we build on the right model — Claude or GPT via API, or an open LLM when data cannot leave your walls — and embed it in the systems you already run: CRM, ERP, email, messengers. Not breaking at scale is its own task, so in production we monitor answer quality, watch for model drift, and keep a human in the loop wherever a mistake is expensive. If it matters for your industry that data never leaves the company, see our separate service: local AI deployment.
What We Offer
Our Capabilities
Process Automation
Eliminate repetitive work with RPA, intelligent document processing, and workflow automation.
Predictive Analytics
ML models that forecast demand, detect anomalies, and surface insights from your data.
Computer Vision
Object detection, OCR, quality control, and visual inspection systems.
NLP Solutions
Text classification, sentiment analysis, entity extraction, and summarisation.
AI Strategy Consulting
AI readiness assessment, use-case prioritisation, and roadmap for measurable ROI.
Good fit
When to talk to us
- Staff move data from documents, emails or requests into a system by hand, and it eats hours every day.
- Support answers the same questions over and over, and the ticket volume grows faster than the team.
- You have years of accumulated data that is never used to forecast demand, churn or workload.
- You need visual checks — object recognition, quality control, reading plates or documents from a photo.
How We Work
Our Process
- 01
Discovery
Identify high-impact automation and AI opportunities in your existing workflows.
- 02
Data Audit
Assess data quality, availability, and governance requirements.
- 03
Prototyping
Rapid proof-of-concept to validate feasibility and business value early.
- 04
Development
Model training, fine-tuning, integration, and API exposure.
- 05
Deployment
Production deployment with monitoring, drift detection, and retraining pipelines.
- 06
Measurement
ROI tracking, accuracy monitoring, and continuous model improvement.
Tech Stack
Tools & Technologies
PythonOpenAILangChainTensorFlowPyTorchHugging FaceFastAPIn8n
Our work
Projects in this area
SecureKazAI
An on-premise AI platform for large organisations. A secure enterprise solution that unlocks AI without sending confidential data outside the company's infrastructure.
Vue.js · Django · AIFAQ
Questions about this service
Where do we start if we do not know where AI would help?
With a short review of your processes — usually one or two meetings. We look at where staff time goes and which tasks repeat, score each by payback and difficulty, and propose two or three candidates to start with. You can then run one of them as a pilot without reorganising the whole company at once.
Will our data end up training somebody else's model?
That depends on the option you choose, and we settle it up front. Using commercial models via API, we work under enterprise terms where your data is not used for training. If that is not enough for your security or regulatory requirements, we deploy an open model on your own servers — then the data physically never leaves your infrastructure.
What if the AI gets it wrong?
We assume it will, and design the process around that. On anything consequential the AI does not act alone — it prepares a decision for a human to approve; model confidence is measured, and borderline cases are routed to manual handling. After launch we track accuracy and tune the system on real data.
What does AI automation cost, and how fast does it pay back?
A pilot covering one process typically lands between $5,000 and $12,000 and takes 4 to 8 weeks. We calculate payback before we start, from the number of manual hours the use case removes — and if the numbers do not add up, we say so and suggest a different process instead.
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