HHuie AI
Huie AI

The data science autopilot for companies that need answers, not another chatbox.

You describe the business problem in plain language. The AI analyst works out the right approach and routes the work through a deterministic engine that computes every number so no result is ever a guess from a Large Language Model (LLM).

The engine, layer by layer

Four layers. One clear path from question to answer.

Open the engine
Huie AI Engine Exploded view · 04 layers
01 / Perception

Business context enters the engine.

The analyst reads the dataset, schema, searches for data-quality issues, and the decision behind the request before it picks an analytical route. The same dataset can power five different deliverables. The engine only knows which to build once it understands what the client is actually deciding. 

What gets captured
  • Files, columns, and quality checks
  • Business objective and target audience
  • Decision being made downstream
  • Interpretability of models, i.e. no black boxes
  • Existing definitions, KPIs, and naming conventions
What this enables
  • Routing logic chooses the right analytical path
  • Data issues get flagged before computation
  • Deliverable format matches the consumer
  • Definitions stay consistent across reports
Nothing is computed until the engine knows what the answer is for.
02 / Cognitive Routing

The architecture adapts to the problem.

A churn question, a revenue forecast, a fairness audit, or a record-comparison workflow should not run through the same path. The analyst assembles a custom topology of modules. Lightweight routes for clean and simple questions, deeper reasoning paths for ambiguous ones.

What gets captured
  • Problem type (forecast, classification, comparison, EDA)
  • Data complexity and volume
  • Prior decisions the user has made on similar work
  • Prior metrics or suggestions
What this enables
  • Lightweight routing for simpler problems
  • Deeper reasoning paths for more complex issues
  • Specific modules are used based on the problem class
  • Cost and delivery time is based on the problem itself
The engine adapts its depth to the problem. Light routes for simpler questions, deeper reasoning for ambiguous ones.
03 / Tool Execution

The agent guides. The engine computes.

The LLM doesn't invent numbers. Deterministic Python modules calculate metrics, train models, run statistical analysis, generate tables, and render charts. The analyst's job is choosing which tool, the tool's job is being correct.

What gets captured
  • Module and tool selection rationale (why this model, this test)
  • Inputs, parameters, and random seeds
  • Intermediate dataframes and transformations
  • Run-time, errors, and convergence diagnostics
What this enables
  • Every number is computed by tested code, not generated by the model
  • The same inputs produce the same result
  • Model assumptions are documented, not hidden
  • Consistent performance over several iterations
Numbers come from controlled code paths, not from a large language model.
04 / Memory

The system learns how the user decides.

Feedback becomes a signal about what was useful, what got pushed back on, and what the client actually acts on. The analyst builds a profile of how a specific organization thinks. The terminology, preferred metrics, default time windows, the shape of a ‘good’ deliverable.

What gets captured
  • Per-deliverable preference and written feedback
  • Edits made to recommended actions
  • Accepted vs. rejected suggestions
  • Follow-up outcomes after decisions ship
  • Reusable definitions, segments, and templates
What this enables
  • Future projects open with the right defaults
  • Repeat work skips redundant clarifying questions
  • The analyst opens each project already knowing how your team works.
  • Patterns of effective work compound over time
Memory is reviewable and editable while autonomous self-modification is a choice. 
Click a layer to focus · scroll to separate them
FAQ

Questions, answered.

There is no statistics background or coding needed. You describe the business problem in plain language. No SQL, Python, or model-building is necessary. Huie AI works out the right approach and the engine handles the rest. Don't know anything about Random Forest, XG-Boost, or other machine learning models? Don't worry, if any machine learning or statistical models are used, Huie AI will explain how they work and why they were used for your specific problem. 
The difference is what you're left holding. A general model can write code to run an analysis, but it has no way of knowing whether the approach it picked was the right one for your problem, and neither do you until a decision goes wrong. You'd be trusting a first attempt with no track record. Huie AI is the engine around the model: it routes each problem to a proven approach, runs it through tested analysis instead of a one-off guess, and gets sharper every time you use it because it learns how your business works. You're not paying for a model, you're paying for an engine that already knows how to do the job reliably, so you're not betting your decisions on hope.
Summary statistics, exploratory data analysis, the relevant models or forecasts, a 10-20 page written deliverable with findings, and clear-actionable next steps. Built for a decision-maker to act on, not raw output to interpret yourself. Huie AI also has persistent memory to remember your business needs and the suggested next steps it made in the past. This way it won't blindly keep suggesting the same thing if it is not working. It will adjust course with the latest data and inputs.
Customer churn, demand forecasting, segmentation, revenue projections, record comparison, and more. The engine picks the approach from your specific question, so the same data can power different analyses depending on what you're deciding. Check out the solutions page for more info.
Bring your own business data or any data you've collected if you don't feel comfortable sharing private data. A common saying in data science is "The more data the better". While this is mostly true, try not to overlook the quality of your data. Huie AI will also check your dataset's structure and quality before any analysis runs. A spreadsheet alone is enough to begin with.
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