Noetis Marketing · Business engine
Campaign Brief Generation Engine
Develops a campaign brief from approved product and audience evidence.
Answers: "Can an engine draft a campaign brief that stays within approved claims?" For marketing teams.
Drafts are hypotheses for a marketer to review. Unsupported product claims are flagged, not published.
Available today
ImplementedImplemented: runs on the shared runtime with supplied material. One bounded development check on 2026-10-12 took 111.5s with 13 model calls and ended "incomplete". That is a development signal, not an evaluation. Customer access is by approved account.
What you send
- Question
- Supplied text material
- Text material up to 150,000 characters per run
What comes back
- Campaign brief with substantiated claims and a testing plan.
- 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.
Develop a campaign brief for our new expense app launch.
Approved product facts: receipt scanning, card sync with 3 banks, approval rules. Draft claim from sales: "Cuts expense time by 80%" (no study cited). Audience research: 15 finance managers interviewed, 11 cited month-end chasing receipts as top pain. Budget $60k. Past campaigns: LinkedIn CPL $140, webinars CPL $60 with 4 webinars run.
- The 80% time saving claim has no supporting study and is flagged
- Audience pain point grounded in 11 of 15 interviews
- Channel hypotheses using the supplied cost per lead figures
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), NVIDIA (NVIDIA). 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.
Audience Analyst
Identify supported customer needs and segments.
Positioning Analyst
Develop defensible differentiation.
Claims Reviewer
Check product claims against available evidence.
Channel Planner
Propose channel hypotheses and resource allocation.
Measurement Designer
Define experiments and outcome metrics.
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
- Publishing campaigns
- Claims without evidence
- Uses only supplied material. No freshness guarantee beyond the documents provided.
- Required for every output during trials.
How to integrate
Send "engine": "marketing" 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.