Skip to content

Blog · Published · Updated

Jev 1.13 for business classification and routing

Short answer

Jev 1.13 returns typed decisions with probabilities, so it is worth testing for routing cases. The confidence threshold decides how many cases go through automatically and how many go to an operator. In the example, after raising the threshold, 500 of 1,000 decisions pass automatically with 99% accuracy. At Syntalith an automation costs from €3,000 net, and with 1,000 cases a month at 3 minutes each it pays back in 3.4 months.

How we workDetails ↓

  1. 01Call30 minutes with an engineer, free
  2. 02PrototypeA working system on your process and data, free
  3. 03ImplementationTarget and fixed price in the contract; 50% at signing
  4. 04Acceptance50% once the agreed target is met
model on your server or in the cloud in the EU
quoted after the review
same work in application with EU cloud model
from €3,000
task, data and equipment analysis
€0

We belong to the OpenAI and Anthropic partner programmes and learn through their official courses. What this means ↓

Author: Syntalith · 3 min read

Example: the morning request inbox
Agent · ready

Statistics

Classification and AI: statistics 2026

3.4 mo.

payback of a €3,000 automation at 1,000 cases a month: €1,745 of work today, €875 with checking and the model. Scenario.

Figures checked: 22 October 2026

Separate the questions that lead to different actions

A complaints queue holds cases about late delivery, a wrong invoice and damaged goods, and sometimes one message concerns all three. A simple keyword rule starts to fail, and asking a large model about every case is expensive. Jev 1.13 on OpenRouter returns typed decisions with probabilities. The scenarios below are a proposal for a pilot.

For a complaint message you ask separate questions: which department handles it, what is missing and whether escalation is needed. Each answer means something different to the application. The department sends the case to a queue, and a missing document triggers a request to the customer. You allow for ambiguous situations. If the task forces one category when there are several problems, you set a priority or split the case, because without that rule even people will not agree on the right answer.

Example: how much work remains after raising the threshold
Decisions above the threshold
500 of 1,000, coverage 50%
Accuracy of accepted decisions
(500 − 5) ÷ 500 = 99%
Cases below the threshold
(1,000 − 500) × 3 min = 25 h
Against a threshold with 400 rejected, the queue grows by (500 − 400) × 3 min
5 h

A higher threshold means fewer errors and more work for the operator

Hypothetical numbers. We measure the time needed to correct wrongly accepted decisions separately before choosing the threshold.

A pilot of a decision model
  • Separate questions for the department, the gaps and escalation
  • A rule for cases with several problems
  • A threshold chosen on cases not used for tuning
  • An operator queue for decisions below the threshold

What classification and routing cost and when they pay back

Scenario: 1,000 cases a month
Today
1,000 cases × 3 min = 50 h × €34.90 = €1,745
With the system, a person checks the output in 50% of the time
€873
Model, 1 call per case, Claude Sonnet 5.5
€2
Cost of one case
€1.75 today, €0.87 with the system
Saving
€870 a month

Payback of €3,000

3.4 months

The volume, the time and the checking share are the scenario's assumptions. €34.90 an hour is the EU average hourly labour cost for 2025 according to Eurostat. Model: 1,000 input tokens and 50 output tokens per call at Anthropic's rates, converted at the ECB rate of 1.1205 USD per euro on 22 October 2026.

The count covers the work around a case: searching, reading, writing and passing it on. What stays is the time to check the output, 50% of today's work in the scenario. Before we quote, we measure both times on 20 to 100 cases your team has already handled, in a €0 test that takes 5 working days.

We leave 50% of today's work in the count because in the example half of the cases stay below the threshold and go to an operator. A decision model is cheap per case, so the payback depends mainly on the threshold's coverage.

When a rule or a large model is enough

If the categories follow from fixed form fields, a rule is enough. If cases are rare and very varied, a large model with instructions can be simpler to maintain than a small model with a threshold. A small decision model makes sense for a large, repetitive queue, where the cost and response time of a large model grow with the number of cases. Hugging Face explains how to choose the dataset and the evaluation measures, and you choose the threshold on cases that were not used for tuning. Running small models locally is covered in the article on models inspired by Jev.

A rule, a small model or a large model
  • Categories from fixed fields

    a rule in the ticketing system

    configuration

  • A large, repetitive queue

    a small decision model with a threshold

    from €3,000

  • Rare, varied cases

    a large model with instructions and review

    from €3,000

Where to start

The pilot covers one queue, for example complaints, compared with today's assignment. We choose the threshold together with the queue manager, because it is a decision about how work is shared. We build this kind of rollout as part of AI automations. For the free process review, a description of one case from recent weeks and the tools it went through is enough. You do not need to send confidential documents, and you get a written plan within 2 working days.

Worked example · Scenario

How we count the payback: case routing

We run the same count for your queue in the free process review.

€5,817

a month for answering 2,000 customer emails

Payback: 0.6 mo.

5 minutes to read an email and write a reply and checking in 15% of the old time are our assumptions. €34.90 an hour is the EU average hourly labour cost for 2025 according to Eurostat, for companies with 10 or more employees. Model cost: Claude Sonnet 5.5, about 3,000 + 600 tokens per message with a draft reply, at the ECB rate of 1.1205 USD per euro on 22 October 2026.

See the full calculation →

This is how we implement it in companies

All case studies →

Client results.

All projects →
foap.comcustomer service

foap.com customer service: 95% of replies are sent automatically.

Users from several countries write to foap.com, in several languages. The system reads the entire conversation and the terms of that person, and then sends a response in that person's language. Questions about money and exceptions go to the team with a ready draft, so they have to check and send them.

the system sends the answers itself, without human typing
95%
time from the arrival of the e-mail to the ready response (median)
30 s
e-mails that require a response receive a ready-made draft
100%
less work for every 1000 answers (estimate)
47 h

Data from foap.com production, October 20-21, 2026. Estimate: 3 minutes for handwritten response.

How foap.com customer service works →
Sample messages. Money matters always come to the person.

Other implementations

jaktako.design

studio projektowe

−87%

Time to look for new customers, every day

4 hours → 30 min

approx. 73 hours less per month (21 days of work)

according to the studio

How the agent loop works →
Signature Estates

real estate

approx. −90%

Operator time spent browsing portals, per day

2–3 hours → 20 min

approx. 45 hours less per month (21 days of work)

respect · contact the seller 5 minutes after the announcement

How the agent finds property listings →
Kuba Koziej, CEO and co-founder of MoreGrowth and board member at Natu.Care

Kuba Koziej

CEO and co-founder, MoreGrowth

„We started with my own AI agent. During the training that followed, I learned to build new applications myself. Today I am bringing this way of working with AI into my business. Working with Syntalith gave me exactly the skills and tools I needed.”

View the original training review on LinkedIn ↗

A model on your server or in the cloud in the EU, proven on your tasks.Book a free process review (30 min) →

How we build automation and agents

From a conversation to a working system in four steps.

  1. 01 · 30 min

    Free process review

    €0

    You're talking to an engineer. You receive the written plan within 2 business days.

  2. 02 · 5 days

    Free prototype

    €0

    The system works on 20-100 cases that your team has already handled. You see every result.

  3. 03 · 2–16 weeks

    Build

    50%

    Fixed price and goal written in the contract. First installment upon signing.

  4. 04 · after reaching the goal

    Reception

    50%

    You pay the second installment when the system reaches its target on your cases.

How a free prototype is created

Mon.

We set a goal

What result do you consider good?

Tue–Wed

The system is working

It goes through your affairs, one by one.

Thu

We compare

We check every difference with the team's work.

Fri

Result and price

List of errors and fixed implementation price.

You choose the matters. We anonymize them upon request, they stay in the EU and we delete them after the test.

On the market, such a test usually costs from PLN 3,000 to PLN 8,000.

Book a free process review (30 min)

We establish a prototype during a free analysis.

Data checked on October 25, 2026.

„Is my data safe?”

Yes. You choose where they go: from your company account to the model on your server.

  1. 00Employee's private account

    Customer and company data ends up in an account that the company cannot see or control. There is no data processing agreement, so it is a violation of the GDPR. When an employee leaves, the chat history goes with them.

  2. 01ChatGPT or Claude corporate account

    The provider does not use your data to train models. A copy of the queries remains in his logs for up to 30 days.

  3. 02API with zero retention (ZDR)

    The provider processes the request and does not save anything. It requires his consent, we prepare the application together.

  4. 03Processing in the EU

    Queries are counted and stored in Europe: OpenAI in the EU region, Claude via clouds with the EU region.

  5. 04Model at your place

    The open model runs on your server or in your cloud in the EU. The data does not go outside the company.

What we do with every implementation

  • Confidentiality agreement and data processing agreement before we receive any data.
  • You choose the cases for the prototype and we anonymize them upon request.
  • The data stays in the EU and we delete it after the test.
  • Corporate accounts with an administrator on your side.

When data cannot leave the company

The open model runs on your server or in your cloud in the EU. To choose from:

White-tailed eagle
Polish model, SpeakLeash and ACK Cyfronet AGH
PLLUM
Polish model, HIVE consortium commissioned by the Ministry of Digitization
Qwen
Alibaba
GLM
Zhipu AI
DeepSeek
DeepSeek

We select a model for the task and check it on your cases. We run it, tune it and maintain it.

Source: Bielik and PLLuM, ACK Cyfronet AGH ↗ · PLLuM, Ministry of Digitization ↗

Do you have a lot of your own data? We will fine-tune the small model.

  1. 1 Your examples
  2. 2 tuning
  3. 3 test on new cases
  4. 4 model at your place

When you have thousands of similar emails, documents or reports, we teach a small open model based on your examples. It works for you, responds quickly and costs less to maintain than a large model. We measure quality on issues that the model has not seen while learning.

Przemysław and Piotr did it in large programming and consulting companies. Artem did it before and does it today at Syntalith. It's about models on your own infrastructure, tuning and quality measurement.

You choose the protection level during the interview. We sign the data processing agreement before we see any data.Schedule a free analysis →

Private AI models

A model on your server or in the cloud in the EU, proven on your tasks.

We select the model and equipment for the task, run it and measure the quality using your examples before you buy anything. The data stays in the company or in the cloud in the EU. We price implementation, hardware or hosting and support separately after analysis.

View private AI models →
model on your server or in the cloud in the EU
quoted after the review
same work in application with EU cloud model
from €3,000
task, data and equipment analysis
€0

Questions

  • What can a company use the Jev model for?

    For decisions with a finite set of answers, for example routing tickets, spotting gaps in documents and escalation. It returns typed decisions with probabilities, so you can set a confidence threshold.

  • How do you choose the confidence threshold?

    On cases that were not used for tuning. A higher threshold means fewer errors among automatic decisions and more cases for the operator, so the choice is a decision about how work is shared.

  • How much does such an automation cost?

    At Syntalith from €3,000 net. With 1,000 cases a month at 3 minutes each, the payback is 3.4 months. We agree integrations and maintenance before the quote, and the model costs €2 a month.

Let's talk

Just write, call or choose a date.

We work fast because we hate corporate cotton wool. We don't have salespeople or managers to keep an eye on managers. You talk immediately to engineers who then build your system. We respect each other's time and yours too.

We will accept 3 more companies by the end of the year.

Mon–Fri 9:00–17:00 CET

What our OpenAI and Anthropic partnerships mean for you.

OpenAI makes ChatGPT and Anthropic makes Claude. We belong to the partnership programs of both companies, so we work with their tools every day and learn from the creators themselves.

OpenAI Select PartnerClaude Partner Network
01We will select a tool for the task
We implement both ChatGPT and Claude. We check both on your documents, and you choose the one that makes fewer corrections and saves more time.
02Knowledge straight from the creators
We learn from official OpenAI and Anthropic courses and materials. Your team learns about the features and settings that work today.
03Licenses without our margin
When we roll out ChatGPT, Claude or Copilot, you buy the licences directly from OpenAI, Anthropic or Microsoft and they invoice you. We add nothing to the licence.
04Certified engineers
On the team, we have an AWS certification in machine learning and an Anthropic certification in systems architecture on Claude.

Participation in programs does not mean that OpenAI or Anthropic endorses our services.