Avenryx SystemsPricing
What AI development costs to build and to run
AI is the rare category where running cost can exceed build cost. We model both before you commit, and we design the pipeline to control per-transaction spend.
Outcomes
- Build and run cost modelled separately
- A payback calculation against real hours
- Guardrails and review costed in
- An indicative estimate in minutes
Pricing
Where it is used
Single automation
One document type or one assistant — €20,000 to €45,000.
Production AI feature
Evaluation, review flow, integration — €45,000 to €120,000.
AI across operations
Multiple pipelines with monitoring — €120,000 upwards.
What we deliver
- Volume of transactions per month
- Accuracy requirement and review cost
- Data preparation and access
- Model choice and residency constraints
- Monitoring and re-evaluation cadence
Who this is for
- Companies costing a specific AI use case with a measurable operational saving.
- Teams that have run a promising prototype and need production numbers.
How delivery runs
- 01
Scoping
One to two weeks on the real workflow, systems and data. Output is a written scope and an indicative band.
- 02
Phased plan
The first usable release is identified explicitly so spend produces something usable early.
- 03
Fixed first phase
Phase one is quoted as a fixed price where scope allows; later phases are re-estimated with real velocity data.
- 04
Change log
Anything added after sign-off is estimated and scheduled in writing, so the budget never moves quietly.
Realistic timeline
Weeks 1–2
Use case, data survey and success metric.
Weeks 3–6
Prototype measured against a real test set.
Weeks 7–12
Production integration, review workflow and monitoring.
Build or buy
Buy something existing when
- A general assistant or an existing vendor feature already covers the task
Build custom when
- The value depends on your own data, documents or workflow
We stop at the prototype when the measured accuracy does not support the business case — that is a successful outcome, not a failed project.
What moves the price
Data readiness
Where the source documents or records are clean and accessible, the build is short. Preparation and labelling is usually the larger half.
Accuracy target and review
Most useful systems keep a human review step. Removing that step requires evaluation infrastructure and pushes cost up sharply.
Model running costs
Token or inference spend is an ongoing operating cost that must be modelled against volume before committing.
Evaluation and monitoring
A test set, regression evaluation and drift monitoring are required for anything that stays in production.
Where projects go wrong
Budget set before scope
A number agreed before discovery is a guess. We give a band after a short scoping phase and narrow it once the workflow and integrations are documented.
Data migration priced last
Legacy data is rarely as clean as expected. We survey the actual export during discovery rather than assuming it at the end.
FAQ
Questions buyers ask
What does it cost to run?+
Usually cents per transaction, but at high volume that becomes a real line. We model it before build.
How do we know it is worth it?+
We baseline the human hours first. If the automation cannot beat that number, we do not recommend it.
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