AI development services for teams shipping to real users
Centricone Technologies builds AI that survives contact with production: retrieval systems over your own documents, models scored against a test set you own, and the guardrails, logging, and cost controls that decide whether a pilot ever becomes a product.
A demo is easy and a deployment is not. We build against an evaluation set from the first week, so the question is never whether the model felt right on a Tuesday.
support coverage across US and Canada time zones
of code reviewed, tested, and documented before release
Most AI projects stall between the demo and the deadline
The pilot never crosses into production
A notebook that worked on ten examples meets real inputs, real permissions, and a real bill, and the project quietly loses its sponsor.
Nobody can tell whether it got better
Prompts and models change weekly with no scored test set, so quality is argued from screenshots instead of measured.
The costs and the risks arrive late
Token spend, latency, hallucinations, and data-handling questions surface after launch, when they are most expensive to answer.
for teams putting AI in front of customers
Centricone covers the whole path: data and retrieval, model selection and evaluation, the application around it, and the monitoring and cost controls that keep it running.
Stack
What we build AI systems with
Models and APIs
- Claude
- OpenAI
- Gemini
- Llama
- Mistral
- Whisper
Retrieval and data
- pgvector
- Pinecone
- Elasticsearch
- LangChain
- LlamaIndex
- dbt
Serving and ops
- Python
- FastAPI
- TypeScript
- AWS Bedrock
- Azure OpenAI
- Vertex AI
How an AI engagement runs
Scope against a use case, not a technology
One workflow, the people doing it today, and the measure that would tell you it worked. If a rules engine wins, we say so.
Build the evaluation set first
Real examples with expected outputs, agreed with your experts. Everything after this is scored against it.
Ship a thin path to production
One user group, real data, real permissions, logging on — small enough to correct, real enough to learn from.
Harden, then hand over
Guardrails, cost controls, runbooks, and the retraining or re-evaluation schedule, documented for whoever owns it next.
Why teams choose Centricone for AI work
Evaluation-first: quality is measured, not demonstrated
Your data stays inside a boundary you approve
Engineers who ship applications, not just notebooks
US and Canada time-zone overlap, with senior contacts
What teams get out of it
A system you can defend
Scored quality, traceable answers, and a cost curve you can forecast — the three questions that decide whether AI work gets a second budget.
Answers with sources
Retrieval grounded in your own records
Measured quality
A test set that runs on every change
Predictable spend
Routing and caching tuned to the workload
A safe failure mode
Refusal and review paths where it matters
Frequently asked questions
Still weighing it up? Book a free consultation and we’ll scope it with you.