Avenryx SystemsTeams
Hire AI developers who have shipped to production
Plenty of engineers can call a model API. Far fewer have owned an AI feature through an evaluation regression, a cost spike and a compliance review. Those are the people we introduce.
Outcomes
- AI features that pass a data-protection review
- Measured accuracy instead of anecdotes
- Running costs modelled before rollout
- Knowledge transferred to your own engineers
Teams
Where it is used
Feature ownership
One engineer accountable for an AI capability end to end.
Evaluation and hardening
Turning an internal prototype into something you can sell.
Advisory
A senior voice in build-versus-buy decisions on AI tooling.
What we deliver
- RAG and vector search
- Structured extraction
- Evaluation harnesses
- Cost and latency engineering
- Data-protection review
Who this is for
- Companies with a specific AI use case and a measurable operational saving in mind.
- Product teams adding assistants, extraction or classification to an existing system.
- Teams whose prototype works but is not production-safe.
When this is the wrong choice
- Open-ended research without a business metric attached.
- Projects with no usable data and no route to obtaining it.
Problems this solves
Prototypes that never ship
Production AI needs evaluation, fallbacks, cost control and a human review path. Engineers who have shipped those build them in from the start.
Unmeasured accuracy
A test set and regression evaluation are built before the feature, so 'better' is a number rather than an impression.
Runaway inference cost
Token and inference spend is modelled against real volume and monitored, with caps and cheaper routing for simple cases.
Architecture and integration
- Model access behind an internal interface so providers can be swapped.
- Retrieval over your own documents with source citations for verification.
- Human review step wherever an error carries real cost.
- Evaluation and drift monitoring running continuously, not once.
How delivery runs
- 01
Use case and metric
One task, one measurable target, one baseline to beat.
- 02
Data survey
What exists, how clean it is, and what may legally be used.
- 03
Evaluated prototype
Measured on a real test set before anything is integrated.
- 04
Production integration
Review workflow, monitoring, cost limits and rollback.
Realistic timeline
Weeks 1–2
Use case, data survey, test set.
Weeks 3–6
Measured prototype.
Weeks 7–12
Production integration and monitoring.
What moves the price
Data preparation
Usually the larger half of the work when documents are inconsistent.
Accuracy target
Removing human review requires far more evaluation infrastructure.
Volume
Inference cost is an ongoing operating line that scales with usage.
Where projects go wrong
The business case does not survive measurement
We report that plainly and stop, rather than shipping something that costs more than it saves.
Sensitive data exposure
Data handling, retention and provider terms are agreed before any content leaves your systems.
FAQ
Questions buyers ask
Which models do you work with?+
Whichever fits the task, cost and data-residency constraints. We are not tied to one provider.
Can they work alongside our data team?+
Yes, that is the most common arrangement.
You already have the idea. Let's define what comes next.
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