The cNode Co-Pilot — how natural language becomes forecasts and scenarios

Getting started: the myth of “access to all data”

Many organisations sit on valuable data, yet access remains complex. To answer “How would a price adjustment affect Q4 cashflow?” you typically need SQL, model knowledge and manual data joins.

The cNode Co-Pilot solves this by translating plain English into concrete, traceable model queries — returning forecasts or simulations in seconds. The result: AI support without black-box risk.

What the cNode Co-Pilot can do

Real-world example

Input (natural language):
“Simulate a 10% OPEX increase in Q3 and show the forecast impact on working capital.”

The Co-Pilot automatically identifies:

• Intent: simulation

• Target: working capital

• Parameters: OPEX +10%, period Q3

Output:

• Scenario visualisation

• Explainable model output

• Audit log for traceability

An interactive decision process emerges — open to all users, regardless of technical background.

Technical architecture: NLP → SQL / NLP → model mapping

The first implementation uses the OpenAI API (GPT-4o) with cNode-specific prompt logic for our data models.

Co-Pilot architecture

• NLP layer: intent detection & entity recognition

• Mapping layer: transform to SQL query or model call

• Output layer: forecast, simulation, SHAP-based explanation

Example
Input: “Forecast cashflow impact if churn increases by 5% in Q2.”
Process: generated SQL → model predict → delta analysis
Output: forecast with scenario impact and explainable drivers

Model flexibility: beyond OpenAI

For sectors with strict data requirements (finance, healthcare, public sector), cNode supports:

• Local LLMs (e.g., Mistral, Llama 3, Falcon)

• On-prem or private-cloud deployment

• Decoupled NLP and forecasting modules

Benefit: data sovereignty. Organisations retain full control of data access, model responses, logs and audit trails.

Governance & security

Every Co-Pilot request is:

• Versioned (MLflow)

• Audit-logged (Haloframe)

• Role-scoped (e.g., Finance can only trigger Finance clusters)

Important safeguards

• External models have no direct access to raw data

• Results are explainable (SHAP/LIME), verifiable (query + prediction + delta) and ready for compliance documentation

UX perspective

Embedded in the cNode web app, the Co-Pilot supports:

• Forecast generation

• Scenarios & simulations

• Model comparisons (“Which model currently performs best for cluster X?”)

• Automated reporting as decision notes

The outcome: an intuitive gateway to complex model logic — without technical barriers.

Conclusion: why this matters

The cNode Co-Pilot isn’t a “smart chat box”. It’s a governance-ready decision layer that connects natural language with forecasting intelligence and auditing.

It shows how LLMs can be integrated responsibly, modularly and explainably into enterprise decisions — from start-ups to large corporates.

👉 Question for readers: Which forecasts or scenarios would you like to generate from your data using natural language?