Noetis Data · Business engine
Business Data Analysis Engine
Answers a business question using an uploaded dataset.
Answers: "Can an engine explain what a dataset we supply suggests?" For analysts and data teams.
Dataset execution and exact calculation are not available. The engine reads data as text and does not run analysis code.
Available today
ImplementedImplemented: runs on the shared runtime with supplied material. One bounded development check on 2026-10-12 took 128.5s with 15 model calls and ended "incomplete". That is a development signal, not an evaluation. Customer access is by approved account.
What you send
- Question
- Dataset file
- Text material up to 150,000 characters per run
What comes back
- Analysis with reproducible computations and clear limitations.
- Findings with the located quote and a support status from claim review
- Release status: released, incomplete with reasons, or failed
- Executed roles, usage and measured cost
Sample
Authored illustration. Written by Encevra to show the workflow. Not customer data and not a recorded model output.
Did the new onboarding flow improve 30 day retention?
Cohort before (Jan, 400 signups): 30 day retained 132. Cohort after (Mar, 120 signups): retained 48. March signups came mostly from a referral campaign. Analyst note: "Retention up 10 points, success."
- Patterns described from the supplied text or table excerpts
- Statements that would need computation marked as unverified
- Questions to answer with a proper analysis tool
The real response shape is documented in the API reference.
How the workflow operates
Each role is a responsibility in the workflow, served by a third party language model. More roles are not automatically better; depth and routing decide which run.
Built with
Models this engine uses: OpenAI (GPT), Anthropic (Claude). Specialists run on OpenAI models and the claim review and final brief on Anthropic models. If a provider has a temporary failure, the step can fall back to an OpenAI model, and the run records that.
Question and Schema Analyst
Map the question to available fields.
Data Quality Analyst
Check missingness, duplicates and inconsistencies.
Statistical Analyst
Select and execute appropriate analyses.
Interpretation Challenger
Check confounding, uncertainty and unsupported causal claims.
Reproducibility Reviewer
Verify transformations and reported results.
Evidence and deployment
Evidence
This engine reads the text you send in the request. Each released finding carries the exact quote and its character span in that text, located mechanically before release. Quotes that cannot be located are labelled Insufficient evidence and never shown as source verified. Interpretation is reviewed as a separate step, and gaps such as a missing clause describe what was searched instead of citing text that is not there.
Deployment
Runs on Encevra hosted infrastructure using the third party models listed above. Every model call and fallback is checked against the deployment policy first. Private cloud and air-gapped deployment are planned; discuss deployment requirements.
What it does not handle
- Causal claims the data cannot support
- Spreadsheet and dataset execution is not available: Not built.
- Deterministic calculation is not available: Not built. Arithmetic is rebuilt by the model and checked by review only.
- Uses only supplied material. No freshness guarantee beyond the documents provided.
- Required for every output during trials.
How to integrate
Send "engine": "data" with your question and material from your server. Keys stay server side.
How to evaluate it
No published result for this workflow yet. Build a small answer key from your own work and compare against a strong single model baseline. See the evaluation checklist and method.
Cost and rights: pricing and licensing. Showing outputs to your own customers needs a written agreement.
Related engines
Content reviewed 2026-10-12 by the Encevra engine team. Engine version 0.1.0 on noetis-core-1.