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Proposal libraries: assemble the right versions

Short answer

A proposal library collects approved service descriptions and references with their owners and rules of use, and a finished proposal keeps the versions it was built from. First check the content library in your current proposal tool, and build an application when authors still copy passages between files. At Syntalith the application costs from €3,000 net. At 100 proposals a month of 35 minutes each, it pays back in 2.1 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
net per application, fixed price before contract
from €3,000
from signing the contract to acceptance
4–10 weeks
free prototype on your documents
€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

Proposals and AI: statistics 2026

2.1 mo.

payback of a €3,000 application at 100 proposals a month: €2,036 of work today, €612 with checking and the model. Scenario.

Figures checked: 22 October 2026

An old paragraph changes the scope of the service

A salesperson prepares a proposal for an installation service. The old document says the client prepares the installation site. The current description in the library says the contractor prepares it, within a separately defined scope. Copying the old paragraph would change what the company presents to a new client.

In the library, the author opens the current description with its version and the contact for the service owner, and agrees the scope for this case with them. The reviewer sees which description was used and which adjustment was approved. After approval, the proposal keeps the chosen passages, and a later change to the shared description serves future proposals and does not change a document already sent to the client.

A reference needs agreement on who may see it and in what form. Material available internally does not have to be approved for sending, and removing the client's name does not settle whether the rest of the content may be used. When that agreement is missing, the author asks the owner before attaching the reference.

A proposal from the library
  1. 01Choicethe current service description with its version and owner
  2. 02Adjustmentthe scope agreed for this case
  3. 03Referenceonly material approved for this use
  4. 04Reviewthe reviewer sees the sources of the passages
  5. 05Exportthe client version with no internal notes
A library ready for acceptance
  • Every passage has a version and an owner
  • A reference has its permitted use recorded
  • A sent proposal does not change after a description is updated
  • The export keeps the links between the proposal and its sources

What it costs and when it pays back

Scenario: 100 proposals a month
Today
100 proposals × 35 min = 58.3 h (€2,036)
With the system, a person checks the output in 30% of the time
€611
Model, 1 call per case, Claude Sonnet 5.5
€1
Cost of one case
€20.36 today, €6.12 with the system
Saving
€1,424 a month

Payback of €3,000

2.1 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: 4,000 input tokens and 600 output tokens per call at Anthropic's rates, 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 handing over. What remains is the time to check the result, 30% of today's work in the scenario. Before the quote, we measure both times on 20 to 100 cases the team has already handled, in a free test that takes 5 working days.

According to a study in Science (2023), ChatGPT cut the time 453 professionals spent on writing tasks by 40%. In proposals that gain only counts when the draft uses current passages, so we also measure the reviewer's corrections.

Check the library in your current tool

PandaDoc describes a library in which a team reuses blocks, pages and whole documents in templates. The new solution described is in early access, and the features depend on the plan (PandaDoc content library). If you use a similar product, check with the administrator how an author picks an approved passage and what is recorded with the proposal. Tidying the library and naming content owners may be enough. An integration makes sense when descriptions sit in one place, references in another, and proposals are assembled in a third. Searching internal guides is covered in the article on a knowledge base for professional services.

Tidy up, integrate or build
  • The tool has a library, no owners

    tidy the content and name owners

    no build

  • Descriptions, references and proposals in different places

    an integration with versions and rules of use

    from €3,000

  • Authors copy, the reviewer reconstructs the origin

    a proposal library built to order

    from €3,000

Where to start

The first scope covers proposals for one type of service, from choosing content to exporting the approved version. For the call, bring a passage that recently had to be corrected because it came from an old proposal, and the name of the person who approves the right version. We build this kind of rollout as part of AI applications. For the free analysis, a description of one case from recent weeks and of the tools it passed 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: assembling proposals

We run the same count for your team in the free analysis.

€2,094

a month for preparing 120 quotes from customer enquiries by hand

Payback: 1.7 mo.

30 minutes per quote from an enquiry 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 4,000 + 1,500 tokens per draft quote, at the ECB rate of 1.1205 USD per euro on 22 October 2026. The extra margin is a scenario: we assume that 1 in 40 enquiries quoted the same day ends in an order that a slow reply loses today, with a margin of €344. In the review we count it on your sales process.

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 ↗

When an off-the-shelf tool does not fit, we build your own.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 →

Custom AI apps

When an off-the-shelf tool does not fit, we build your own.

We build an application with a language model for the work of your team: searching the company's knowledge, case panel or portal. We check the validity of your documents before launch, and the code stays with you.

See AI applications →
net per application, fixed price before contract
from €3,000
from signing the contract to acceptance
4–10 weeks
free prototype on your documents
€0

Questions

  • What is a proposal and reference library?

    A set of approved service descriptions and references with versions, owners and rules of use, from which an author assembles a proposal. The finished proposal keeps the versions of the passages it was built from.

  • Do you need AI for a proposal library?

    An ordinary application is enough for assembling versioned content. AI can help find a passage or draft a fit between a description and a request.

  • How much does such an application cost?

    At Syntalith from €3,000 net. At 100 proposals a month of 35 minutes each, the payback is 2.1 months. We agree integrations and maintenance before the quote, and the model costs about €1 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.