A workflow worth automating
Map the work, the people involved, the decisions being made, and the evidence already available. Start where AI can remove friction without weakening accountability.
Process discovery · feasibility · value · risk
AI workflow automation
Turn complex operational work into a controlled AI workflow.
We design AI-assisted workflows around your people, data, business rules, and approval points—reducing manual effort while keeping decisions traceable and under your team’s control.
- Workflow design
- Applied AI engineering
- Human-in-the-loop controls
Review workflow
Human controlledInput
Operational evidence
Documents · conversations · events · business context
AI taskRunning
Analyse & structure
Apply approved instructions, tools, rules, and output schema
Decision checkpoint
Result ready for human review
Review
Approve
Escalate
Inputs, outputs, and review state retained as agreed
Illustrative workflow. Controls and retention are designed for each use case.
Useful AI is part of a process—not a prompt looking for a problem.
We begin with the operational work: where time is lost, which judgements matter, what evidence is available, and what must remain with a person. The technology follows from that understanding.
What we deliver
From manual friction to an operable AI system.
The result is more than a model call. It is a designed path from source evidence to a useful outcome, with the integrations, safeguards, and ownership needed to run it responsibly.
A solution shaped around your operation
Combine models, prompts, retrieval, rules, integrations, and interfaces into a workflow that fits how your team actually works—not a generic chat window.
LLMs · tools · structured outputs · integrations
Controls built into the path
Define what the system may access, what it may produce, when it must stop, and where a person reviews or approves the result.
Access · validation · human review · audit trail
A workflow your team can operate
Test representative scenarios, observe quality and cost, document the boundaries, and establish a practical route for improvement after launch.
Evaluation · monitoring · handover · iteration
Where it becomes practical
Built for real operational work.
The pattern is consistent: turn unstructured evidence into a clear, reviewable output at a scale or speed that manual work cannot comfortably sustain.
Compliance
Marketing content review
Assess large volumes of app-listing content against defined requirements, structure the evidence, and route uncertain cases for review.
Scale analysis · exception handling
Service quality
Conversation evaluation
Turn customer-service chats and call transcripts into consistent quality signals, supported observations, and actionable coaching themes.
Quality criteria · structured insight
Recruitment
Interview analysis
Organise transcripts into comparable, human-reviewed evidence and prepare next-step communication without automating the hiring decision.
Human judgement · reviewable drafts
Recommendation
Technical depth, grounded in the business problem.
“Michał led the project technically, designing and implementing the AI-based evaluation approach and translating business and compliance requirements into a scalable solution.”
A practical engagement
A deliberate path from opportunity to operation.
Understand the work
Follow the current process from source material to decision, including exceptions, hand-offs, and control points.
Prove the useful core
Build a focused slice with representative data to test quality, speed, cost, and operational fit early.
Engineer the workflow
Connect the right models and tools, add validation and review steps, and make the experience usable for the team.
Release and learn
Launch with clear ownership and observable signals, then improve against real outcomes rather than assumptions.
Model providers
Model choice follows the workflow.
We select the provider around the task, data requirements, governance, performance, and cost.
OpenAI API
A broad range of models for reasoning, multimodal analysis, and agentic workflows.
Explore models (opens in a new tab)Claude API
Claude models for capable, thoughtful automation across complex business tasks.
Explore models (opens in a new tab)Gemini API
Multimodal models for reasoning, high-throughput tasks, live experiences, and generative media.
Explore models (opens in a new tab)Azure OpenAI
Managed access to OpenAI models with enterprise identity, network, and governance controls.
Explore models (opens in a new tab)OpenRouter
Access to a wide choice of leading models through a single integration.
Explore models (opens in a new tab)Opper AI
A model platform designed to help teams build and operate reliable AI features.
Explore models (opens in a new tab)Related insights
See the approach in practice.
AI automation · 19 August 2026
AI Interview Analysis for Recruitment in 2026
How transcripts become structured, human-reviewed insights while people remain accountable for hiring decisions.
Read the article
AI automation · 1 September 2026
First MCP Server, First ADK Agent
A guided example of an agent using approved tools and live evidence within clear, read-only boundaries.
Read the guided tourStart with the workflow
Where is manual work limiting your team today?
Bring the process, the pain point, and the constraints. We’ll identify whether AI is useful, where people should stay in control, and the smallest credible next step.