Avenryx SystemsService
AI development that ends in production, not a pilot
Most AI projects fail between the demo and the day it has to be right. We build the unglamorous parts — evaluation sets, fallbacks, human review and cost control — so the feature survives contact with your operation.
Outcomes
- A measurable task removed from a human queue
- Answers grounded in your own documents and data
- Quality tracked against a real evaluation set
- Predictable running cost per transaction
Service
Where it is used
Document understanding
Invoices, contracts and forms read, checked and posted.
Operational assistant
Staff asking your systems a question in plain language.
Classification and routing
Inbound requests triaged before a human sees them.
What we deliver
- Retrieval-augmented generation over your content
- Structured extraction with validation
- Model selection and cost engineering
- Evaluation harnesses and regression tracking
- Human-in-the-loop review flows
- Data-protection review before any data leaves your estate
Who this is for
- Companies with a document, classification or drafting workload measured in hours per day
- Teams with enough of their own data to make answers specific rather than generic
- Operators who want measurable handling-time reduction, not a demonstration
When this is the wrong choice
- Tasks needing a guaranteed correct answer every time with no human review
- Problems a deterministic rule or a database query solves more cheaply and more reliably
Problems this solves
Reading documents by hand
Invoices, tenders, claims and contracts consumed page by page. Extraction with confidence scoring routes only the uncertain cases to a person.
Knowledge locked in files
Answers exist in a decade of documents nobody can search. Retrieval over your own content gives cited answers instead of guesses.
Triage backlogs
Inbound requests sorted manually before anyone can act. Classification with review thresholds clears the obvious cases.
Architecture and integration
- Retrieval grounded in your own documents with citations, so answers can be checked against the source.
- Confidence thresholds that route low-certainty cases to a human queue instead of guessing.
- Model-agnostic interface so a provider can be swapped as prices and capabilities change.
- Full audit of prompt, retrieved context, output and reviewer decision — essential for regulated work.
How delivery runs
- 01
Task selection
We pick one task with a countable baseline — minutes per document, percentage routed correctly — so the result can be judged.
- 02
Evaluation set first
A labelled set of your real cases is built before development, giving an accuracy figure rather than an impression.
- 03
Pilot with human review
The model proposes, a person approves. Thresholds relax only where measured accuracy supports it.
- 04
Production and monitoring
Cost per task, accuracy drift and escalation rate tracked after launch, with a fallback path when the provider is down.
Realistic timeline
Weeks 1–2
Task selection, baseline, evaluation set.
Weeks 3–8
Build, evaluate, iterate against measured accuracy.
Weeks 8–12
Supervised pilot, then production with monitoring.
What moves the price
Data readiness
Clean, structured source documents cut the work sharply. Mixed scans and inconsistent formats add a preparation phase.
Accuracy target
Moving from 85% to 97% typically costs more than the first 85% did.
Human-in-the-loop tooling
The review interface is usually a third of the build and the reason the system is trusted.
Where projects go wrong
Demo-driven scope
Impressive demos on curated examples collapse on real inputs. We evaluate on messy production data from the start.
No baseline
Without a measured 'before', no one can say whether the system helped.
Unbounded running cost
Per-token costs need caps, caching and a model tier policy or the bill surprises you in month three.
Indicative budget
Running cost matters as much as build cost; we model both.
FAQ
Questions buyers ask
Will our data train someone else's model?+
Not in the configurations we deploy. We use providers and settings that exclude your data from training, and we document the data path.
What if the model gets it wrong?+
Every production flow has a confidence threshold and a human path. We design for the wrong answer before we ship the right one.
You already have the idea. Let's define what comes next.
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