AI automation consulting · Europe
AI automation consulting that takes one workflow to production
Karven is an AI automation consultant and implementation team for European companies. We map a recurring process, build the automation inside your existing tools, add human review where judgement matters, and measure the result against the way the work runs today.
- Monaco · Paris · Milan
- EN · FR · IT
- From €5,000
- GDPR-aware architecture
Production workflow
AI automation moves work, not just data
A production automation receives work from email, forms, documents or a queue; enriches it with business context; applies rules and model judgement; routes exceptions to a person; updates the system of record; and records what happened.
The output is a controlled process with visible owners and audit events, not a standalone chatbot.
What Karven automates
These examples define the input, automated work, human checkpoint and system of record before any build begins.
| Process | Inputs | Automated work | Human checkpoint | System updated |
|---|---|---|---|---|
| Customer-request triage | Email, form, account record | Classify, prioritise, enrich and draft | Ambiguous or sensitive response | Helpdesk / CRM |
| Invoice intake | PDF, scan, purchase order | Extract, validate and reconcile | Exceptions and approval | ERP / accounting |
| Contract review intake | Contract, policy, matter data | Identify clauses and deviations | Legal judgement | DMS / workflow |
| Receivables follow-up | Ledger, CRM history, email | Draft and schedule follow-ups | Disputes or concessions | Finance / CRM |
| Internal knowledge request | Question, permitted sources | Retrieve and compose a sourced answer | High-impact decision | Knowledge / helpdesk |
| Recurring reporting | Spreadsheets, systems, rules | Consolidate, reconcile and flag anomalies | Sign-off and investigation | BI / reporting |
Bring in an AI automation consultant when the process crosses systems
An AI automation consultant is most useful when work arrives in unstructured formats, moves across tools or teams, and includes exceptions that require judgement. The engagement should produce a scoped, integrated workflow with controls and a measurable baseline—not an occupational assessment or a generic automation roadmap.
- Unstructured intake
- Cross-system workflow
- Judgement-heavy exceptions
- Prototype-to-production gap
- Limited internal integration capacity
Implementation capabilities around the workflow
Karven designs the automation around the work and uses the appropriate system pattern for each stage.
What production adds that a demo does not
A demo proves that a model can produce an output. Production proves that the process can run repeatedly, safely and under ownership.
| Demo | Production system |
|---|---|
| Hand-selected inputs | Real volume, variation and edge cases |
| Manual copy and paste | Controlled integrations and credentials |
| A good-looking answer | Acceptance thresholds and exception routing |
| No named owner | Operational owner and escalation path |
| Hidden model behaviour | Version, action and source logs |
| One-time test | Monitoring, incident response and change control |
When external implementation capacity is the right fit
A simple, stable rule inside one application may not need AI or an outside team. These triggers indicate a broader production problem.
Work arrives unstructured
Email, PDF or free text must become a controlled case
Handling time
The process crosses systems
The workflow reads and updates more than one tool or team
Cycle time
Exceptions require judgement
Rules handle normal work; people receive uncertain cases
Exception rate
A prototype already exists
The missing work is security, integration, monitoring and ownership
Completion rate
Internal capacity is constrained
The process owner exists; integration time does not
Time to production
The business case needs evidence
A baseline and bounded test precede a larger investment
Measured delta
One process first. Evidence before scale.
The engagement model turns operational questions into explicit build and investment decisions.
| Phase | What Karven does | Client input | Decision produced |
|---|---|---|---|
| Discovery | Map volume, handoffs, exceptions, systems, data and risk | Process owner and sample cases | Is this worth automating? |
| System design | Define rules, model tasks, integrations, review and metrics | Technical and access constraints | What exactly should be built? |
| Build and test | Run real examples and measure failure modes | Representative inputs and reviewer feedback | Does it work on real work? |
| Production path | Add deployment, security, logging, monitoring and ownership | IT, security and operations sign-off | Can it run safely? |
| Scale | Extend only after the first process has evidence | Adjacent workflow data | What should come next? |
Five-day Quick Win Sprint
The Quick Win Sprint starts at €5,000. In five days, Karven maps one process, builds a working prototype on representative company inputs, tests it with the people who perform the work and produces a recommendation for production.
| Visible deliverable | Why it matters |
|---|---|
| Mapped process and baseline | Defines the work and the comparison point |
| Working prototype | Tests the core mechanism on real material |
| Samples processed end to end | Exposes variation and handoffs |
| Known failure and exception cases | Prevents a demo-only decision |
| Measurement plan | Makes the next investment falsifiable |
| Production recommendation and scope | Shows whether to scale, redesign or stop |
Pricing
How much does AI automation consulting cost?
Price reviewed 11 August 2026
Quick Win Sprint · five days · from €5,000
Karven's five-day Quick Win Sprint starts at €5,000. A production build is scoped as a fixed-price project after the workflow has been tested. Cost depends on the number of integrations, input variation, data quality, security environment, review requirements, expected volume and ongoing monitoring.
- • Number of integrations
- • Input variation and edge cases
- • Data quality and access
- • Security and deployment environment
- • Human-review requirements
- • Volume, latency and monitoring
General AI use is ahead of operational automation
In 2025, 18.90% of EU SMEs used at least one measured AI technology, while 4.78% used AI for workflow automation or decision support. The comparison uses Eurostat aggregates and does not reveal production maturity or ROI, but it shows why implementation capacity remains distinct from tool adoption.
Source: Eurostat, isoc_eb_ai (2025 data) ↗A European implementation team
The service is designed for operating teams buying a production workflow, not for creators trying to start an automation business.
- Delivery across Monaco, France, Italy and broader Europe
- Implementation in English, French and Italian
- Euro-denominated entry offer
- EU or controlled-environment deployment options
- Privacy, governance and human review considered during design
Systems are designed to support GDPR and AI Act readiness. Applicable obligations depend on the purpose, data, organisational role and risk classification of the system actually deployed.
AI automation consulting: buyer questions
How much does an AI automation consultant cost?+
Karven's five-day Quick Win Sprint starts at €5,000. Production work is then scoped as a fixed-price build. The cost depends on integrations, input variation, data quality, security, review requirements, expected volume, monitoring and the operational support model.
What process should a company automate first?+
Start with a recurring process that has visible volume, handling time, errors or queue pressure; real examples; a named owner; and an output a person can judge. Work that combines documents, repeated decisions and cross-system handoffs is often a stronger first candidate than a broad innovation idea.
How long does an AI automation project take?+
A bounded prototype can be tested in five days through the Quick Win Sprint. A production workflow typically takes six to twelve weeks, depending on integrations, data readiness, security review, governance, volume and the number of exception paths that must be handled.
Can AI automation work with an existing ERP or CRM?+
Yes, if the required interfaces, identities and permissions are available. The production design should document what the workflow reads and writes, how credentials are controlled, how failed updates are retried, and who owns each integration after launch.
What happens when the AI is uncertain?+
Low-confidence, ambiguous or sensitive cases are held for a human checkpoint. The system should expose the reason for review, preserve relevant context, record the operator decision and allow the workflow to continue, pause or escalate according to an explicit rule.
Is AI automation different from RPA?+
AI automation can interpret unstructured inputs and support judgement where classic RPA follows deterministic interface steps. The two can work together. A stable rule or native platform feature may be preferable when the process does not require language, document understanding or probabilistic decisions.
Can company data remain in the EU?+
EU or controlled-environment deployment can be designed where the system, providers and client requirements support it. The actual answer depends on data flows, model and infrastructure vendors, support access, telemetry and contract terms; it should be documented rather than assumed from a product label.
How is automation ROI measured?+
ROI starts with a pre-automation baseline such as handling time, cycle time, error rate, rework, cost per case, queue size or completion rate. Reviewer effort and ongoing operating costs must also be measured so the automation is not credited for work transferred to a hidden manual queue.
Can we start with one process?+
Yes. Starting with one bounded workflow limits risk and creates evidence before scale. The first process should be tested on representative inputs, including failure cases, and end with a decision to move to production, redesign the approach or stop.
Who owns the workflow after deployment?+
The client should have a named operational owner, documented access, runbooks, change and incident procedures, and visibility into the integrations and logs. The commercial support model can vary, but the system should not depend on undocumented knowledge held only by the implementation team.
One recurring process is enough to start
Pick the process that hurts the most
Walk us through one recurring process. We will identify what can be automated, what should remain with your team and what evidence is needed before a production build.
Book a discovery call