Startups and scale-ups with growing OPEX
You want to grow without linear growth in operational costs. AI agents allow throughput increase without proportional hiring.
AI & Automation
Automation and AI Agents
I design and implement AI systems that reduce OPEX in production — not pilots in a drawer. I connect language models, company data and workflows into agents that execute specific tasks with measurable business impact.
✓Identified processes with the highest automation ROI
✓Working AI agent in production in 4–8 weeks
✓Measurable OPEX reduction or throughput increase
In short
AI Agents are not prompt demos. They are systems that connect language models, company data, tools and workflows to execute specific operational tasks with quality control and measurable business impact. Automation makes sense where there is a repeatable process, a unit cost and a clear definition of correct output.
When AI automation makes sense
AI Agents deliver value in specific scenarios — not everywhere and not at any cost:
- ⚠You have repetitive tasks that consume people's time but do not require creative thinking
- ⚠Support or customer service is a bottleneck despite a growing team
- ⚠Research, data enrichment or document analysis takes too long
- ⚠Internal reporting is manual and error-prone
- ⚠The operational cost of a process is measurable and you want to reduce it
- ⚠You want to scale throughput without proportional headcount growth
Who this is for
Companies with repetitive analytical processes
Research, lead enrichment, document analysis, classification — anything repetitive and text or data-based.
Operations and customer success departments
Automation of first-line support, ticket routing, responses to common questions and escalation logic.
Founders and CTOs seeking AI ROI
You want to start with a specific use case with a measurable impact — not by experimenting across the whole organisation.
What it covers
- →Process audit — identifying processes with the highest automation ROI potential
- →AI agent design — system architecture: model, data, tools, orchestration
- →RAG (Retrieval Augmented Generation) — connecting LLM with your knowledge base and data
- →Workflow automation — integration of the agent with existing tools (Slack, CRM, ticketing)
- →Quality control — response correctness assessment, human-in-the-loop, fallback logic
- →Monitoring and metrics — latency, accuracy, cost per call, error rate
- →Security and data governance — company data does not reach models without your consent
How we implement AI Agents
- 01
Process audit and ROI map
We map processes and estimate automation potential: unit cost, volume, repeatability, quality required.
- 02
PoC on one process
2–4 week proof of concept on the highest-ROI process. Clear KPIs: time, cost, quality — not subjective impression.
- 03
Production deployment
Agent in production with monitoring, alerts and human-in-the-loop for edge cases. Not a demo — an operational system.
- 04
Measurement and iteration
After 2–4 weeks in production: verify ROI, optimise, decide on scaling to additional processes.
- 05
Scaling
Rollout to additional processes based on the proven model. Each new agent benefits from lessons of the previous one.
What you receive
- ✓Working AI agent in production with documentation
- ✓ROI report — implementation cost vs savings/throughput increase
- ✓Monitoring dashboard — accuracy, latency, cost per call
- ✓Operator runbook — how to handle the agent, edge cases, escalations
- ✓Security and data governance documentation
- ✓Scaling plan for additional processes
What we measure
Operational cost of the process before and after automationThroughput — how many tasks the agent handles per hourAccuracy — percentage of correct responses/actionsLatency — agent response timeCost per call — API cost per taskHuman escalation rate — what percentage of cases require human intervention
Frequently asked questions
What are AI Agents?
AI Agents are systems that connect a language model (LLM) with company data, external tools and decision logic to autonomously execute tasks: research, analysis, generation, classification, routing, communication.
How is an AI Agent different from ChatGPT?
ChatGPT is a generic interface to a model. An AI Agent is a specialised system integrated with your data and workflow — it operates in a specific context, has access to your systems and produces output directly within your process.
How do you measure AI automation ROI?
ROI = (process cost before × volume – process cost after × volume) – implementation cost. Process cost includes: human time, tools, errors and delays. In practice: payback period of 2–6 months with a well-chosen process.
Which processes are suitable for automation?
Best candidates: research and data enrichment, first-line support, ticket routing and classification, report and summary generation, document analysis, FAQ responses, new user onboarding.
How do you control AI response quality?
Several mechanisms: evaluation prompts (AI assesses its own output), human-in-the-loop for edge cases, structured output (response format instead of free text), accuracy monitoring on a production sample.
Is company data secure?
Yes — the architecture is based on the model in your infrastructure or an API with a data processing agreement. Company data does not reach model training. Every deployment has data governance documentation.
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