Workflow automation
Replace repetitive handoffs and manual coordination with explicit, observable processes.
02 / Service
Automation that works when the happy path ends.
Turn painful workflows into reliable systems by combining deterministic software, APIs, browsers, and AI only where AI earns its place.
What this is for
Automate the ugly parts without turning the business into an AI demo.
The hardest workflows are rarely one clean API call. They cross inboxes, documents, internal tools, browser-only systems, human decisions, unreliable vendors, and exception paths that become the real process.
Reliable automation treats that whole environment as an engineering system. AI can classify, extract, interpret, or assist where ambiguity is real. Deterministic code should own the parts that require repeatability, accounting, safety, and clear failure behavior.
Browser and API automation
Integrate services that offer incomplete APIs—or no practical API at all—without hiding fragility.
LLM integrations
Use model capabilities inside a defined system rather than making a prompt the entire architecture.
Agent systems
Design tool use, state, approval, cancellation, and recovery around real operating constraints.
Evaluations and guardrails
Measure useful behavior, define unacceptable outcomes, and test changes before trusting them.
Human-in-the-loop design
Keep people in control where judgment, authorization, or costly ambiguity demands it.
Reliability model
AI is a component. The workflow is the product.
- 01Map the real workflow, including exceptions, approval boundaries, and hidden manual recovery.
- 02Separate deterministic operations from tasks where model judgment creates actual value.
- 03Make state, retries, idempotency, cancellation, and side effects explicit.
- 04Add evaluations, observability, and auditability proportional to the consequence of failure.
- 05Design a useful human fallback instead of assuming autonomy is always the goal.
Good project shapes
Start where manual effort and operational risk meet.
Good starting points include a workflow teardown, browser/API integration, document or message pipeline, internal AI tool, agent prototype with production guardrails, or rescue of an automation that works only under supervision.
What Pliska does not pretend to offer
- A chatbot added for appearance
- Autonomy without recovery paths
- Model claims without evaluations
- AI where ordinary code is safer
Why software and security matter here
Automation expands both capability and blast radius.
An automated system can act faster, touch more data, and repeat mistakes at scale. The engineering has to account for credentials, authorization, prompt and input boundaries, third-party behavior, non-idempotent actions, and the difference between a model suggestion and an approved operation.
Pliska approaches AI automation as software and security engineering—not as a detached experiment.
Explore security engineeringHave a workflow that should not be manual?
Show us where it breaks down.
The rough process, the exception cases, and the desired outcome are enough to start a useful conversation.
Start a technical conversation