Operational processes under control, even when AI is involved
We redesign processes where time, errors, and manual review create unnecessary cost. We design rules, exceptions, and traceability so automation works in production, not only in demo.
Operational friction map · rules/exceptions matrix · ROI hypothesis
Accelerate operations.Increase margin.
We do not build fragile automation
Rules before promises
Exceptions before ideal cases
Production before demo
Traceability before autonomy
We are not a fit if you want to explore AI without a clear process or build autonomous agents without defined operational accountability.
Operational Friction Diagnosis
You are not buying a meeting. You are buying operational clarity to decide which process to move first, with what logic, and with what expected impact.
What you get
- operational friction map
- 2-3 prioritized opportunities
- rules / exceptions matrix
- initial ROI hypothesis
Price
€450
To determine whether a real opportunity exists before entering pilot or deployment.
Built for operations-heavy teams
Killia is a strong fit for teams where outcomes depend on repeatable criteria, explicit validation, well-treated exceptions, and handoffs that cannot break.
Client profile
- teams with recurring operational load that require controlled production execution
- operations with clear ownership and explicit decision criteria
- organizations that need to scale execution without constant manual review
Where operations lose value
Most teams do not lose margin because of strategy.
They lose it in repeated reviews, cross-team delays, errors that create rework, and decisions that still depend on manual intervention.
Manual review overload
Critical processes still depend on people checking, copying, validating, and chasing information.
Slow cycle times
What should move in minutes gets delayed for hours or days across departments.
Hidden operational errors
The cost is not only the mistake. It is the rework, the delay, and the loss of control that follows.
Dependency on key people
When critical decisions live in a few heads, scale becomes fragile and inconsistent.
We design processes where AI adds value, but does not govern alone
We apply AI only where it materially improves outcomes: classification, extraction, prioritization, or decision support.
Control stays in the process architecture: rules, validations, accountable owners, and traceability.
- reduce manual effort in critical processes
- standardize decisions with explicit rules
- manage exceptions without losing control
- improve operational capacity without growing overhead
- measure impact with operational KPIs
Why we are not another AI consultancy
we start from the bottleneck, not from the model
we design exceptions, not only ideal paths
we think in production, not in demo
we prioritize control and accountability, not empty autonomy
we do not sell agents as an end state; we use them only when they improve the process
A clear path from diagnosis to deployment
Operational Friction Diagnosis
€450
We define which process should move first, with what logic, and with what expected impact.
Includes
- operational friction map
- 2-3 prioritized opportunities
- rules / exceptions matrix
- initial ROI hypothesis
Pilot Process
From €2,500
1 scoped process · 2-4 weeks · minimum viable integration · visible initial KPIs
Does not include multi-area rollout, complex integration, or multiple processes in parallel.
For teams that want to validate a real process on top of a shared operating foundation, with explicit rules, exception handling, and minimal integration.
Includes
- initial setup on a shared operating foundation
- redesign of 1 process
- automation logic
- AI where it materially improves the process
- exception handling
- initial KPI baseline
Operational Deployment
From €6,000
Designed for one or several processes that already passed initial validation or require full operational implementation.
Extends that validation on a common foundation to move one or more processes into production with less complexity, clear rules, exception handling, traceability, and operational support.
Includes
- operational extension on a shared foundation
- production deployment
- process orchestration
- operational traceability and accountable owners
- KPI framework
- go-live support
Diagnosis identifies. Pilot validates on a common foundation. Deployment extends and operates on that same foundation to reduce complexity and accelerate production rollout.
Operational frictions we solve
We organize by friction type: where review gets stuck, where decisions lack criteria, and where handoffs break.
Manual review
Processes where people repeatedly review, validate, and correct work without a consistent decision criterion.
Documents + rules
Document extraction, classification, and validation with explicit rules and accountable owners per exception.
Handoffs and escalations
Cross-team flows where context is lost, decisions are delayed, or escalations happen too late.
Reporting and consolidation
Consolidation across fragmented sources to reduce manual corrections and speed up operational closing cycles.
Operational patterns we solve
Examples of real frictions solved with rules, accountable owners, and observable operational changes.
Document validation with exception routing
Problem: slow manual reviews, non-homogeneous criteria, and repeated internal returns.
Applied logic: validation rules by document type, exception queue, and owner per threshold.
Observable operational change: fewer duplicated reviews, fewer internal returns, and fewer documents blocked by unclear criteria.
Support to back-office handoff
Problem: late escalations, lost context, and cases bouncing back between teams.
Applied logic: initial classification, escalation criteria, and accountability checkpoints.
Observable operational change: less context loss, fewer late escalations, and fewer cases returned between support and back-office.
Operational reporting and consolidation
Problem: slow closing cycles, manual consolidation from multiple sources, and repeated corrections before sharing numbers.
Applied logic: consolidation rules, pre-validations by source, inconsistency detection, and a review owner only for exceptions.
Observable operational change: fewer manual corrections, less closing time, and more consistency in shared data.
These patterns are usually a strong signal that a process is ready for diagnosis, pilot, or deployment.
Method Friction -> Logic -> Production -> Governance
A proprietary operating method to move manual-review processes into production with clear rules and owners.
What changes after deployment
The goal is not to add AI.
The goal is to remove operational drag.
less manual review and less rework
shorter cycles and faster resolution
more operational consistency and clearer ownership
Operational design principles
This is how we design for controlled, verifiable production execution.
business rules and explicit decision criteria
exception handling by design
production-ready logic with verifiable outcomes
Find the process where you are losing the most without seeing it
Identify the first process where automation has real business value.
Start with one process. Scale to several without rebuilding the foundation each time.
