AI workflow assessment
Find where AI adds value, where rules work better, and where human judgement must remain.
AI Automation Agency for Business Workflows
Add AI where it can classify, extract, draft, search, or assist—connected to your systems, permissions, and real workflow.
Start with the workflow—not a software sales pitch.
AI with a job to do
PROJECT OUTLINE PAPKA / 01
Move AI from a promising demo into a controlled business workflow.
- Current state
- AI experiments are disconnected from daily work
- First move
- AI workflow assessment
- Working result
- Extraction & classification
Ready to scope
Does this sound familiar?
Move AI from a promising demo into a controlled business workflow.
- 01AI experiments are disconnected from daily work
- 02Teams process large volumes of unstructured content
- 03Knowledge is difficult to find and reuse
- 04Automation needs judgement as well as fixed rules
What we can build
One workflow.
The right combination of tools.
We use configuration, custom development, integrations, automation, and AI where each one makes sense.
Extraction & classification
Turn documents, messages, and other unstructured input into usable, routed information.
Knowledge assistants
Help people search and use approved internal information with source context.
AI-assisted workflows
Draft, summarise, compare, or prepare work before a person reviews the result.
AI agents with controls
Let agents use bounded tools and actions with permissions, approval steps, and logs.
Evaluation & monitoring
Test useful scenarios, observe failures, and improve quality against business criteria.
A useful first conversation
Let’s find out if the project is a fit.
Good projects start with a real operational problem, a person who owns it, and a clear reason to change.
Tell us what is stuck✓
The use case has a clear input, output, and owner
✓
Useful source data is available with appropriate access
✓
A human-review or fallback path can be defined
✓
The value can be measured against today’s workflow
How the work moves
From messy reality
to a working system.
The process keeps business decisions visible and gives your team working software to respond to throughout delivery.
- 01
Map the work
We learn how information moves today, where it gets stuck, and what the new system must change.
- 02
Shape the solution
We turn the workflow into a clear scope, system design, integrations, and delivery plan.
- 03
Build in visible stages
You review working increments and real scenarios throughout implementation—not only at the end.
- 04
Launch and improve
We support rollout, resolve real-world gaps, and plan the next useful improvements.
Questions, answered
Before we start.
Still deciding what kind of solution you need? Describe the workflow and we’ll start there.
How can we integrate AI into business processes?
Start with a specific workflow and connect AI to the data, systems, permissions, decisions, and people around it. The model is one component of the solution.
Which workflow should we automate with AI first?
Choose a bounded, frequent task involving text, documents, classification, search, or drafting, with a clear quality measure and a safe review path.
What is the difference between AI automation and regular automation?
Regular automation follows explicit rules. AI is useful when inputs vary and interpretation is required. Reliable systems often combine both.
Can AI work with our CRM, ERP, email, or documents?
Yes, when the systems provide appropriate access. We design how context is retrieved, what the AI may do, and how the result returns to the workflow.
How do you prevent the AI from taking unsafe actions?
We limit tools and permissions, validate inputs and outputs, add approval for sensitive actions, record activity, and define fallback behaviour.
Can employees review output before it is used?
Yes. Human review is often the right first deployment model, especially for customer communication, financial data, and consequential decisions.
Which AI model or provider will you use?
That depends on the task, data sensitivity, quality requirements, latency, deployment constraints, and cost. We select after evaluating the use case.
How do you evaluate accuracy and business value?
We use representative examples and business-specific criteria, then track quality, exceptions, time saved, and downstream outcomes.
What determines the cost of AI workflow automation services?
The main factors are workflow scope, integrations, data preparation, model usage, evaluation, security, interface needs, and ongoing monitoring.
Start with the problem
What should work better?
Tell us what your team does today, where the process breaks, and what a useful result would look like.