02Service · AI Agents & Pipelines
AI isn’t just for chatting. It’s for operating.
We build AI Agents and AI Pipelines that plug new-generation models directly into your business processes — thinking, processing data and adapting on their own in repetitive work.
- Agents with tools
- AI can read, write and call APIs within the scope you allow
- Multi-model
- The right model per task to balance quality and cost
- Under control
- Logs, approval thresholds and human checkpoints
Show your screen The software running for real
Capabilities
What the system does for you
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01
Lead scoring & outreach agent
Reads a prospect’s website and social channels, scores fit and drafts personalised outreach.
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02
Data processing pipelines
Extract, classify and summarise data from emails, documents and orders at scale.
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03
Internal assistant on your data
Q&A over your documents, processes and products — with cited sources.
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04
Built into existing tools
AI inside your Auto Tools, CRM, marketplaces and mobile apps.
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05
Model cost optimisation
Easy tasks go to light models, hard ones to strong models; results are cached.
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06
Continuous quality evaluation
Output test suites catch quality drops early.
Implementation process
Four steps from manual to self-running
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Step 1
Pick what to automate
Rank tasks by time spent and how repetitive they are.
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Step 2
Rapid prototype
A prototype runs on your real data within 1–2 weeks.
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Step 3
Go live
Connect systems, set approval thresholds, monitoring and alerts.
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Step 4
Measure & improve
Track hours saved, accuracy and cost per task.
In-depth analysis
Why this approach works better — and when you should choose it.
From chatbots to AI that operates
Most businesses still use AI as a chat window: ask, get an answer, copy it into another tool. The real value appears when AI is wired directly into your processes — allowed to read data, call tools, write results and hand work to the next step without a person in the middle.
AI Agents and AI Pipelines
An AI Pipeline is a fixed processing chain, ideal for repetitive batch work: classifying orders, extracting data from documents, generating content from templates. An AI Agent is more flexible: it receives a goal, plans its own steps, uses the tools it is allowed to and adapts to new situations — for example reading a company’s profile, scoring its fit and drafting a personalised outreach message.
We choose the right architecture for each problem, and often combine both.
Control, measurement and cost optimisation
- Human approval thresholds for important decisions.
- Full logs to trace every agent action.
- Model routing: light models for simple tasks, strong models for hard ones.
- Output test suites that catch quality drops early.
Results are measured in concrete numbers: manual hours saved, accuracy and cost per task.
Best suited for
Who gets the most value?
- Businesses with repetitive data workflows
- Sales and support teams personalising at scale
- POD and content teams automating creative work
- Product owners adding AI features to their apps
FAQ
Frequently asked questions
Is our business data used to train AI?
We use providers’ enterprise API channels, where data is not used for training by default. Sensitive data can run on self-hosted models.
Can AI replace staff?
AI replaces the repetitive part of the job, freeing your team for decisions and relationships. Important decisions always keep a human approval threshold.
What does running AI cost per month?
It depends on task volume and models. During the pilot we measure the real cost per task so you can budget accurately before scaling.
Pairs well with