Most AI pilots work. Very few survive production.
The demo is rarely the hard part. What follows is: evaluating whether the system is right often enough to rely on, controlling cost per request at real volume, handling the cases it gets wrong, and explaining a decision when someone asks. Aminexus builds AI systems with those questions answered at design time, because retrofitting them costs far more than designing for them.
What we build
Four areas, chosen because they are where projects fail.
AI strategy and use-case selection
Which problems justify AI, which are better solved by a query and a rule, and which are not ready because the data is not. Sequenced by value and feasibility, with the cases we advise against written down too.
RAG and agent systems
Retrieval pipelines, chunking and embedding strategy, tool use and orchestration, and the human checkpoints that belong in any workflow with consequences. Built against your data, not a demo corpus.
Evaluation and observability
An evaluation set reflecting your real distribution, regression testing on every prompt and model change, and tracing so a bad answer can be tracked to its cause. Without it you are hoping, not operating.
Deployment, scaling and unit cost
Model serving, routing between models by task difficulty, caching, and cost per request measured from week one. The same discipline we apply to cloud spend, applied to inference.
Governance
The question that stops deployments is rarely technical.
It is usually: where did the data come from, who can see the inputs, what happens when the model is wrong, and can we show our reasoning if challenged. For organizations operating across jurisdictions those answers differ by market, and the strictest one tends to set the design. We build data lineage, access boundaries, human-in-the-loop checkpoints and an audit trail into the system rather than documenting around it afterwards.
How an engagement runs
Prove it is worth building before building it.
The first phase often concludes that a use case should not proceed. That is a successful outcome, not a failed one.
1. Frame
Define the decision the system supports, what "right" means, and the threshold worth clearing. Output: success criteria agreed before any build.
2. Prove
A narrow prototype against real data, measured on the agreed criteria. Output: evidence, and a recommendation that may be to stop.
3. Harden
Evaluation harness, guardrails, failure handling, observability and unit cost. Output: a system operable by people who did not build it.
4. Run
Deploy, monitor quality drift as closely as uptime, review economics. Output: an operating rhythm rather than a launch event.
Bring us the use case you are unsure about.
Those conversations are more useful than the ones already decided. If it should not be built, we will say so in the first session.
Book a scoping call