Training programs
Generated training programs and multi-week plans, built from the athlete’s own history.
Adroited LLCAI and operational automationCape Coral, FL
AI that makes operational decisions — not chatbots
Most “AI features” are a chat box bolted onto an existing product. We build AI into the decisions that actually bottleneck a business: what to charge, who to assign, whether a schedule is executable, what a document says, and what your data is telling you.
- Discipline
- Operational AI and
decision engines - Not this
- A chat box bolted
onto the sidebar - Proof point
- A production pipeline
we built and run - Location
- Cape Coral, FL
Working remotely
01
A production AI pipeline, end to end
Our clearest proof point is one we run ourselvesOur clearest proof point is a system we built and run ourselves: an AI content pipeline that takes a single campaign keyword and ships a finished, published video.
Keyword in, published video out — resumable at every stepSchematic
- LLM-written script and title variants
- Synthesized voiceover across three text-to-speech providers
- AI-generated and stock imagery, selected and composed automatically
- Automated video assembly
- Direct publishing to YouTube, TikTok and Instagram
It is built as a resumable background pipeline. Every step is checkpointed and logged, so a failure at step six resumes at step six instead of restarting from step one. Voice and video vendors sit behind provider abstractions and can be swapped without touching the pipeline. It is multi-tenant from the ground up.
Technologies: OpenAI, DALL·E, ElevenLabs, Google TTS, OpenAI TTS, Pexels, FFmpeg, and OAuth publishing to YouTube, TikTok and Instagram.
02
Decision engines we have shipped
Live in production systems, not demosPricing
Engines that account for distance, vehicle class, staffing requirements and repositioning cost
Schedule feasibility
Answering whether a multi-stop itinerary can actually be executed, before anyone commits to it
Intelligent assignment
Matching jobs to people by availability, certification, location and constraint
Document extraction
Turning captured paperwork and field images into structured records
Insight engines
Deterministic rules combined with LLM interpretation, so the output is explainable rather than a black box
03
AI inside the product, scoped to your data
Bounded by the tenant, alwaysItineraries
Generated itineraries and schedules produced in-app, not researched by hand.
Bounded assistants
Assistants that answer from the client’s records, not the open internet.
Narrative reporting
Automated analysis and written reporting over operational data.
04
Cost control and correctness
Why AI survives its first invoice, or does notTwo things separate AI that survives contact with a real business from AI that gets switched off after the first invoice.
Metered billing
We build per-tenant AI credits, plan allowances and usage tracking into the product. AI spend that cannot be predicted or attributed is a liability, not a feature.
Regression testing
AI output drifts. We ship automated benchmark harnesses that re-test analytics and AI output against known-good results, so a model or prompt change cannot quietly degrade the product.
Async by design
AI work runs on background and queue workers and is polled by the interface. Nothing blocks a user request waiting on a model.
05
Questions we get asked
FAQ01 Is this just a chatbot?
Rarely. Most of the value we see is in classification, extraction and drafting — reading an inbound document and pulling the fields out of it, routing a request to the right queue, drafting a first response for a human to approve. A chat interface is one delivery mechanism among several, and often not the best one.
02 What stops it making things up?
Scope and verification. We constrain what the model is asked to do, feed it your actual records rather than letting it recall from training, and put a human approval step in front of anything that leaves the building or moves money. Where an answer must be exact, the model retrieves it rather than composes it.
03 Does our data get used to train someone else’s model?
Not under the API terms we build against, and we configure for that explicitly. We will tell you which provider processes what, what is retained and for how long, and where a workload is sensitive enough that it should run on infrastructure you control instead.
04 What does it cost to run?
Per-request pricing on the underlying model plus the hosting for the application around it. The cost is genuinely usage-driven, so we instrument it from day one and can show you spend per workflow. Where a task is high-volume and simple, we will often use a smaller model or plain code instead — the point is the outcome, not the technology.
Have a process that should not still be manual?
Tell us where the time goes. We will tell you honestly whether AI is the right tool for it.