SovrGPT Decision Engine

Decisions via API, in milliseconds.

The Decision Engine reads a text or system state and rates it against your rules: which team, how urgent, whether a human should look. Your software gets probabilities and a recommendation back and decides for itself what runs automatically.

Request access Technical documentation

An example

Incoming message

“Since this morning I cannot log in. My September invoice was also charged twice – please check this urgently.”

Evaluation

Team → support 65 % · unclear 31 % · billing 4 %
Urgency → level 3 of 4 (urgent)
Human review? → yes, 99 %
Recommendation: review – no automatic reply

Sample measurement without double check: four questions about one text in 397 milliseconds, without a single line of generated text.

Three formats

Choice

One of up to 26 options – team, category, next action. With a probability for every option.

Yes/no

Does a statement hold? Review needed, complaint, escalation – as a probability, not a gut feeling.

Ordered score

One level of a scale: urgency, relevance, quality. With an expected value between the levels.

Why it is fast

A chat answer is written word by word. For a decision the model reads the text and options once, and we read off how likely each option is. Several questions about the same text run in parallel. Choice questions are also checked with reordered options, in parallel as well.

Our measurement (warm, ticket text, four options, without double check):

v1 (Germany): 65 ms per question · four questions at once 123 ms
v2 (EU): 198 ms per question · four questions at once 397 ms

An image can be the state too. So far we have only tested this with a small test image; we have not yet measured how long photos or scans take.

A chat answer to the same text takes 5 to 30 seconds. These are measurements, not a guarantee.

One API access, clear rules

1Your application
2sovrGPT API – identity, tenant, scopes, limits
3Data centres in Germany or the EU
4Rating back, your workflow decides

Every answer states which region and model version did the computing. The core path calls no model API of a US provider.

Next, we are training and calibrating specialised decision models based on open base models, with documented tests for each task.

Transparency

Even a cleanly structured answer can be wrong. That is why every answer states the model version and calibration status, and uncertain cases go to a human for review. Binding approvals stay in your application.

We do not store the text, questions or answers. Billing is by tokens. The processing location is in every answer.

Operating models

Decision API

Shared inference pool with API access, quotas, docs and playground. Ready to use.

Dedicated Decision

Dedicated operation with contractually defined region and performance. On request.

Custom Decision

Data preparation, training, evaluation and maintenance of a model for your task. On request.

For developers

OpenAI-style authentication, one endpoint, JSON in, JSON out.

curl https://sovrgpt.com/api/v1/decisions \
  -H "Authorization: Bearer $SOVR_KEY" -H "content-type: application/json" \
  -d '{ "preset": "ticket-routing-v1", "state": "…" }'

Go to the API documentation

Prices on request. The probabilities are not calibrated yet. We keep measuring and will let you know when limits or model versions change.