Let AI handle repetitive work. Keep the important decisions with people.
Tell us about a process that takes your team hours every week. During a free call, we’ll explore how to improve that process. Within 2 business days, you’ll receive a written recommendation: what the process costs you per year, what to automate first and from what price.
Client review
View the original training review on LinkedInWe 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.
Scope of work
- Personal AI agent
Kuba's own AI operator, installed on his computer with a clear boundary and a trace behind every decision.
- 1:1 training
Working with Claude on his own processes, built to ship in production.
Public course review on LinkedIn - AI across the business
An ongoing rollout of the resulting tools and working practices across his team.
Selected client work
From a personal AI agent and training to systems used by entire teams.
Syntalith is an OpenAI Select Partner in the OpenAI Partner Network.
We help your team respond to customers faster and find information in company documents. We choose and set up the right AI tools, then teach your team how to use them.
- 01
Natu.CareAI collaborationVisit the company - 02
Signature EstatesAI agent for real-estate acquisitionView the case - 03
jakotako.designClaude training and an agent loop for sales-prospect sourcingView the case - 04
foap.comCreator email operations and reply reviewView the case study - 05odbierze.aiodbierze.aiOur own product: website, infrastructure, dashboard and testingVisit the website
We choose the process, set the target, and only then start the build.
We set the pilot target before the build. A pilot on agreed data usually takes 3–6 weeks. Before it starts, the contract states the target, the fixed price, and how we will check the result. After the pilot, you get the results and decide whether to proceed with the full implementation.
30 min · €0
Scan: choose the process
We discuss one recurring task: inbox handling, invoices, quotes or sales contacts. We learn how it works today and where the difficulties are. The engineering call takes 30 minutes. Within 2 business days, you receive a written summary: what the process costs you per year, what to automate first, from what price, and what not to build.
After reviewing data and requirements
Scope and fixed quote
We review sample data, integrations and exceptions. Once we have enough information, we prepare a fixed-price proposal. For a larger scope, we propose an audit from €600. We credit its fee toward the build.
fixed price
A contract with a clear target
Before work starts, the contract defines the scope, data, fixed price and one pilot target: handling time, cases completed without manual work or data accuracy. We agree on measurement, acceptance conditions and the fixes included in the price. You know how we will assess whether the system works.
build time depends on the format · pilot usually 3–6 weeks
Build and pilot
The system handles an agreed part of the process using your data, usually for 3–6 weeks. It gets the access needed for the task, records its actions and routes exceptions to a named person. We check the result against the contract target. If it falls short, we complete the agreed fixes within the pilot price. Further changes are quoted separately.
Yours to keep
Pilot results and system handover
You get concrete results: what the system achieved, what the full implementation will cost and what work remains with your team. You then decide whether to continue. Code, model instructions, test data and documentation belong to you. You can keep developing the system with us or hand it to another provider. Maintenance scope and price are agreed before the contract.
Deployed
The result guarantee applies to automation, app, and agent implementations. It requires access to the agreed data, enough cases for a reliable assessment, and the scope agreed before the pilot to remain unchanged. Training is priced and accepted under separate terms.
Leave the call knowing what to automate and the starting price.
During the 30-minute call, we discuss the problem and how your team works. Within 2 business days, we send a written summary: the process cost calculated on your numbers, what to automate first and from what price, and one concrete next step.
Process scan takeaway
example layout
What it costs today
Copying PDF invoices into the ERP takes 20 hours a month. At a labour cost of €15 an hour, that is €3,600 a year.
What to automate first
Reading invoices and preparing postings for the accountant to approve. Automation from €1,750 net.
Next step
We review sample invoices and the ERP integration, then send a fixed-price proposal.
If a simple script or a later start is the better route, the recommendation appears in the summary.
You receive a fixed price and timeline in a free proposal after we review the data and integrations. For a larger scope, we propose an audit from €600 net, credited toward the build.
What can we build for your business?
Automation follows defined steps. An agent chooses actions for a task. An application gives people a place to work. We can combine these in one system.
See what each option does and how it could work in practice.
AI automationsConnect your tools and carry out repeatable steps, from reading a document to recording data in a system.
Useful when work follows a defined sequence. AI reads emails, files and forms; agreed rules determine what happens next.
Example use case
A customer submits a form and attachments. The system extracts the details, creates a CRM case, assigns it to the right person and reminds them of the deadline.
AI agentsReceive a task, use tools and choose the next step based on what they find.
Useful when cases vary and require gathering information, checking conditions and acting across systems. We define what the agent can do independently and what needs approval.
Example use case
A customer asks to change an order. The agent checks its status, availability and change policy, asks for missing details and prepares a solution. A refund goes to a person for approval.
Custom applicationsGive your team one place to work, with workflows, data and AI features built around your needs.
Useful when off-the-shelf tools do not fit how your business operates. Applications can serve employees, customers or partners and connect to existing systems.
Example use case
Customers submit work through a portal instead of coordinating by email and spreadsheet. The team sees documents, deadlines and owners, while AI prepares a case summary.
Personal AI agentsHelp one person with everyday email, calendar, document and task work.
An agent uses the context you share and the tools you choose. It can prepare materials and carry out agreed actions, asking for approval where you need it.
Example use case
You ask: “Prepare me for tomorrow’s meetings.” The agent gathers previous agreements, open tasks and relevant documents. Afterwards, it drafts follow-up messages and actions.
Company brainLet people ask questions about company knowledge and receive source-linked answers within their access permissions.
Connect scattered procedures, documentation and decisions. Help people find answers even when they do not know which folder or system to search.
Example use case
A salesperson asks: “Can we offer this customer those terms?” The system finds current policies and links to the documents. It also identifies what the available material cannot establish.
Your own software instead of expensive SaaSReplace selected paid tools with an application built around the functions you actually use.
Worth considering when subscriptions grow, tools do not connect well or workarounds multiply. We compare migration and maintenance costs with continuing to use existing products.
Example use case
A company manages customers, tasks and reports in three tools. We build a shared workspace with the required features and migrate agreed data. New accounts do not need licences for the replaced products.
AI trainingTeach teams to use AI in their work, check its output and handle company data.
Exercises match employees’ roles and tools. People practise giving instructions, supplying context and recognising answers that need correction.
Example use case
The team practises preparing a presentation, analysing a spreadsheet or comparing proposals. Each person works from source material to a checked result on a task similar to their own work.
Model fine-tuning and trainingAdapt a model to your terminology, assessment criteria or required response format.
We consider training when instructions and access to knowledge do not deliver the required quality. It needs good examples; improvements are checked on data held out from training.
Example use case
A company has thousands of requests correctly labelled by its team. We use these examples to fine-tune a model to identify categories and urgency, then measure errors on a separate test set.
Models deployed on your infrastructureRun AI on company servers or in a chosen cloud, with control over where data is processed.
We select a model and infrastructure for your confidentiality, workload and quality requirements. Access, monitoring and updates are part of the plan; a private environment needs maintenance.
Example use case
Employees analyse contracts and reports in an internal application. A model in the agreed environment processes the documents, with access linked to team accounts.
Selected systems doing daily work.
Each description explains the task the system handles, when a person decides, and how the work is recorded.
Conversation, campaign and reply draft in one workspace.
The foap.com interface brings together creator correspondence, campaign context and reply review. Cases requiring a decision go to a person. The case shows current local views with fictional data.
See the deployment
foap.com · local application in development · fictional data
- Real-estate investment
Four listing portals turned into a short decision queue.
See the deployment - VC fund, investment analysisDelivered
A company pipeline scored before the analyst sits down.
See the deployment - Personal operator
A personal AI operator, tuned and hosted on the owner's own machine.
See the deployment - jakotako.design · one-to-one training
Claude training that ended with an agent loop for sales prospects and competitors.
See the deployment
Implementation scope and starting budget.
Choose the work you want to improve. These are starting prices: after a conversation, we agree on the scope, integrations and fixed price for your project.
AI implementation
We start with a conversation about the process. Scope and fixed price follow a review of the data, integrations and requirements.
AI Automations
Repetitive office work runs itself: the system reads, classifies, files and replies, and people only approve.
an invoice from email lands straight in accounting · a request gets a case record and a draft reply · the weekly report assembles itself
2–6 weeks
AI Agents
A system that runs a process from event to outcome and calls a human only on exceptions.
portal monitoring and a decision queue · inbox triage under an agreed policy · research and dossiers before a decision
6–16 weeks
AI Apps
Your own application with a language model inside, built for your process when a ready-made tool does not fit.
search across company documents · data extraction from PDFs and scans · an internal panel with a copilot
4–10 weeks
Personal AI agent
A personal operator for owners and executives: inbox triage, briefings, research, and decision preparation on infrastructure you control. Actions with business consequences go to you for approval.
The scan chooses one process to assess, and within 2 business days you receive a recommendation, including when a simpler route is the better fit.
Book a free process scan (30 min)See example scopes and budgets below. Training and audit prices exclude VAT.
Delivery and technical review
Four people who put their names to the work.
More than 22 years of software engineering experience combined. Artem and Serhii own the architecture and delivery. On complex projects, Przemek and Piotr independently review the architecture and stand behind their assessment.
Meet the full team
Artem Lisovtsov
Co-founder · Applied AI/ML Architect
Has built production systems for more than 6 years. His AI work spans chatbots, model training, fine-tuning, and private deployments through RAG and workflow orchestration to agents that use governed sources and tools. He has worked with AWS Bedrock, OpenSearch, and pgvector.
Serhii Ivanchatenko
Co-founder · Voice AI & Agent Systems Engineer
More than 7 years of commercial .NET experience. At Syntalith, he builds voice AI and agent systems: telephony, integrations, evaluation sets, scenario and edge-case testing, and execution-trace analysis.
Przemysław Markiewicz
GenAI Tech Lead · external auditor
More than 5 years in IT, including more than 2 years of hands-on AI and LLM work. He designs RAG and multi-agent systems, runs models on-prem, and leads fine-tuning and evaluation.
Piotr Łach
Backend & AI Engineer · external auditor
Four years of production Python backend experience. He built APIs, RAG platforms, asynchronous data pipelines, and automated local vLLM deployments on Kubernetes.
Training
Your team uses AI. Agree how to use it together.
We train technical and nontechnical teams on their own process or codebase. Sessions are led by engineers who run AI systems in production. We have delivered five one-to-one sessions.
Who teaches
- Led by engineers who run AI systems in production
- Data rules, verification, approval, and an audit trail
- A documented method and reusable working materials
Participant testimonial
I came in using coding agents ad hoc and left orchestrating them: loops, an agent kanban, a full development cycle that ships. What stayed with me is running context and cost on purpose, and building my own harnesses instead of waiting for a tool to ship one. It changed how I work day to day.
Senior Software Engineer, Immutable
Choose a training format
For builders · 1:1 or company
AI agent development course for developers
Build with agents under defined controls and keep the resulting method and tools.
quoted after a call
paid course · netFor non-technical teams
AI training for companies and teams
Your team uses AI in daily work to produce source-based documents and analysis under clear data-handling rules.
from €600/day
net · quoted per scope
FAQ
How much does an AI deployment cost?
The task determines the budget: a personal operator on your own machine from €600, handling one Gmail inbox from €1,750, and preparing quotes from customer enquiries from €3,000, excluding VAT. These are examples of similar systems, rather than fixed packages. Integrations, data and requirements affect the final quote. Running costs are stated separately before signing.What does the free scan commit us to?
A 30-minute conversation with an engineer and a written summary within 2 business days. It ends with a recommendation, including when a simpler route is the better fit.What if something simpler than an agent is enough?
Sometimes the scan recommends an integration or a simple script; sometimes it recommends waiting. We record that recommendation as part of the work, at no cost.What if the AI makes a mistake?
The system gets boundaries and a full action trail: anything it isn't sure about goes to a human, and production impact requires approval. In the pilot we write down a measurable target and test it on your data before you commit to the full rollout.How long does a deployment take?
Automations 2–6 weeks, apps 4–10, agents 6–16. We set the scope and the timeline in the contract before we start.Where is our data processed?
Before work starts, we sign a data processing agreement (DPA) and agree the environment, data scope, and access. For deployments that require it, we design the flow so data never leaves the EU and model providers operate under zero data retention. The system receives only the permissions needed for the specific process, and sensitive operations require human approval.
Start with the process costing your team the most time.
Tell us about the work you want to improve. Within 2 business days after the call, you receive in writing what that work costs you per year, what to automate first and from what price.
- A 30-minute call with the engineer who would lead the work.
- A review of the processes that cost you the most time and money.
- A written summary: what the process costs you per year, what to automate first and from what price.
Times are shown in your own time zone. We work with clients across time zones.
The scan chooses one process to assess, and within 2 business days you receive a recommendation, including when a simpler route is the better fit.
On the call, we show working implementations and how we measure their results. Browse the deployments before the call
Describe your process in writingClaude Partner Network member. Denotes membership in Anthropic's partner program for Claude. Not an endorsement of Syntalith's services by Anthropic.