Guide
Build versus buy for AI document analysis
If one prompt on one model does the job well on your evaluation set, use a model API directly. Use a framework when you need custom multi-step logic your team will own. A managed workflow like Noetis is worth considering when you want source-checked findings and a release gate without building them.
For ctos and engineering leads. Written by the Encevra engine team. Reviewed 2026-10-12.
Option 1: a model API
Cheapest and fastest to start. You write prompts, handle long documents and build any citation checks yourself. Often sufficient for summaries and single-question extraction.
Option 2: a framework or orchestration runtime
Gives you control over multi-step workflows and deployment. You still design the roles, the checks, the evaluation and the operations.
Option 3: a managed workflow
You send material and a question and receive reviewed findings with located quotes and a release status. You give up some control, accept per-run pricing and current latency of minutes, and still build your own interface and review process.
Comparing cost
Use your own numbers: engineer time to build and maintain checks, model spend per document, and the cost of a wrong finding reaching a user. We do not publish a savings figure because it depends entirely on those inputs.
Questions to decide with
- Does one prompt on one model pass your evaluation set?
- Who will maintain citation checks and release rules?
- Is minutes of latency acceptable for this feature?
- Where must the data be processed, and under what policy?
Frequently asked questions
- Is Noetis cheaper than calling a model directly?
- Usually not per call. You pay for the checks and orchestration around the models.
- Can I move off later?
- Results are plain JSON you store yourself, so your data stays portable.
- Can it run in my own cloud?
- Not yet. Private cloud and air-gapped deployment are planned.