Avenryx Systemsguides
AI assistants that pay for themselves in the first quarter
The AI projects that succeed are boring: one task, measured before and after, with a human path for low confidence. The ones that fail start with the technology.
Outcomes
- Pick a task with a measurable hourly cost today
- Ground answers in your own content, and cite sources
- Set a confidence threshold with human escalation
- Track accuracy against a fixed evaluation set
- Model running cost per transaction before rollout
Problems this solves
Assistants built without a metric
Pick one task with a measurable handling time, then measure before and after.
Answers that cannot be verified
Retrieval over your own documents with citations lets users check the source in one click.
Cost that outgrows the saving
Model inference is an operating cost; route simple cases cheaply and cap spend.
How delivery runs
- 01
Pick the task
High volume, repetitive, and currently done by reading your own documents or records.
- 02
Build the test set
Real questions with correct answers, agreed before development.
- 03
Prototype and measure
Accuracy against the test set and estimated cost per interaction.
- 04
Deploy with review
Human review wherever an error carries real cost, with feedback captured.
Realistic timeline
Weeks 1–2
Task, data survey and test set.
Weeks 3–6
Measured prototype.
Weeks 7–10
Integration, review workflow, monitoring.
What moves the price
Document preparation
Cleaning, structuring and permissioning the source material is usually the larger half.
Volume
Inference cost scales with usage and must be modelled before commitment.
Evaluation infrastructure
Ongoing evaluation and drift monitoring for anything that stays in production.
Where projects go wrong
Confident wrong answers
Citations, refusal behaviour and a review step where the cost of error is real.
Sensitive content leaving your systems
Provider terms, retention and redaction agreed before any data is sent.
FAQ
Questions buyers ask
What is a realistic payback?+
For document-heavy processes, often under twelve months. We baseline the hours first so the claim is testable.
Where does AI add risk?+
Anywhere a wrong answer is expensive and unverifiable. Those flows need review steps or should not be automated.
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