Ship AI that moves the metric, not the demo.
LLM and ML integration, chatbots, predictive analytics, and funnel automation, built into production, not stuck in a proof of concept.
INTRO
What we take on, what we do not promise.
We take on applied AI that has to earn its place in a real system. That usually means LLM integrations and assistants wired to your data, retrieval over your documents, predictive models like churn and lifetime value, and funnel automation that reacts to what those models say.
We do not promise magic. There are no black boxes you cannot audit, no vague accuracy claims, and no models we cannot explain to the person who has to answer for them. If a simpler tool solves the problem, we say so.
APPROACHES
How we approach the work.
Open any area to see what we do.
LLM and assistants
Assistants and copilots wired to your data, tools, and access rules. Retrieval over your documents, not a public chatbot.
Predictive analytics
Churn, LTV, propensity, and lead scoring models trained on your history, wired into your CRM and paid media.
Funnel automation
Lifecycle flows and next-best-action rules that react to model output. Fewer manual campaigns, more compounding revenue.
Data pipelines
The plumbing under all of it. Clean ingestion, warehousing, and event tracking so models see reality, not fragments.
PROCESS
A short, honest sequence.
- 01
Discovery
We map the metric, the constraint, and the decision the system needs to make.
- 02
Data audit
We check what data you actually have, how clean it is, and whether it can support the goal.
- 03
Model and build
We pick the smallest thing that works, evaluate it against a task-specific set, and iterate.
- 04
Ship and measure
We deploy into your stack, wire logging and fallbacks, and track live performance.
SIGNATURE
A visual of the idea.
DELIVERABLES
What you get at the end.
- A deployed model or endpoint running in your stack.
- An evaluation and benchmark report tied to your task, not a leaderboard.
- A live dashboard for quality, cost, and usage.
- Documentation for how the system works and how to change it.
- A rollout plan with fallback paths and monitoring in place.
BENCHMARK
A public reference point.
RETRIEVAL AUGMENTED GENERATION
Grounding a model in your own data materially reduces hallucinated answers versus a base model with no retrieval.
Source: Lewis et al., Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Meta AI, 2020). Industry benchmark, not a Aramio client result.
QUESTIONS
Answers, before you ask.
- No. By default we use provider modes that do not retain or train on your data, and we route through vendors that offer that in writing. Where you require it, we run open models inside your cloud so nothing leaves your perimeter.
Next step
Book an intro call.
Fifteen minutes. We ask the sharp questions, tell you if we are a fit, and either scope a sprint or point you elsewhere.