AI integration for business that runs on your own data — not one more chatbot
We put AI into your processes: routine automation, chatbots and assistants, custom tools. Not a wrapper over someone else’s API — search over your own data, answers backed by sources, and cost tracked down to the cent. Starts with an audit in 1–2 weeks.
What is AI integration for business
AI integration for business is putting ready-made AI models into your working processes. Routine automation, chatbots and assistants, custom tools built for the job. We don’t train models from scratch — we connect OpenAI, Anthropic and Gemini to your systems so they run on your data and account for every dollar spent.
A wrapper over someone else's chat
AI integration for business
Most “AI solutions” on the market are a thin wrapper over someone else’s chat. We do it differently: search over your own data, several models working together, transparent token economics, and a real deployment.
That’s what we consider the difference between a demo and a tool people pay for.
Three tracks of AI integration, plus cost control
Three tracks — automation, chatbots and custom tools: we take one or combine them for your case, depending on where the process loses the most hours. Token-cost control runs across all three.
Business process automation with AI
We connect your tools (CRM, email, spreadsheets, documents) into one flow through Make, n8n and APIs. The AI reads incoming data, sorts it, drafts replies and passes it on with no human in the loop: automatic lead handling, document parsing, automated reports, first-pass triage of inbound requests.Chatbots and AI assistants
Customer-facing bots on your site, in Telegram or WhatsApp, plus internal assistants that answer from your knowledge base through RAG. Every answer comes with a link to your document or policy — not made up.Custom AI tools
A tool for a specific job that off-the-shelf products don’t have. We build on our own stack (Python, Node.js, React): dashboards with AI data analysis, content-generation pipelines, and the extraction and structuring of data from large piles of files.Model orchestration and cost control
We run several models in parallel and route expensive and cheap tasks to different ones. We show the price of each operation up front and keep a running token count. On the Themis case this made processing 40–50× cheaper.How an implementation runs: 5 steps
An implementation runs in 5 steps — from diagnostic to scale. You see the first result in numbers right after the pilot, not six months later.
Diagnostic
A 30–45 minute call: where it hurts, which processes, which numbers. No commitment on your side.Audit
We work out where the process loses hours, find the spots for AI, and estimate the ROI on the priority tasks.Plan
We put together a 3–6 month implementation plan: priorities by impact and difficulty, with no surprises in the reports.Pilot
We build the first working solution in production and measure the result in hours and money, before and after.Scale
We extend to other processes in releases, keep support and monitoring running, and report the metrics every month.Exact timelines depend on the scope and the state of your systems. The audit takes 1–2 weeks, the first pilot 3–5 weeks. We give you a precise estimate on the free 30-minute diagnostic.
Who needs AI integration
AI integration pays off most where a team works through documents, correspondence or knowledge bases every day. The more repetitive the operations, the faster the payback.
Law and accounting firms
Parsing cases, contracts and source documents with links back to sources. That’s exactly where our Themis case grew from.Agencies and service companies
An internal knowledge base, a single data hub, competitor monitoring and automated reporting — as in the geo-intel case.E-commerce and support
An assistant for products, orders and policies, automatic handling of inbound requests, and first-pass lead qualification.Fintech and regulated niches
Document processing and screening that account for compliance and the need for verifiable sources.Any team buried in documents
Anywhere a team drowns in PDFs, Excel and messenger exports and burns hours on it every day.How much AI integration costs
Starts with an AI Audit at $800. After that, three packages: Audit, Pilot and Partnership. The Audit is a low-risk way in, and its cost is credited toward the work that follows.
For those weighing up where AI will pay off. The cost is credited toward the work that follows.
- Analysis of processes and where time leaks
- A list of automation candidates with expected impact
- ROI calculation by priority
- A 3–6 month implementation plan
- Takes 1–2 weeks
For those who want a first working result, not a pitch deck.
- 1–2 automations or 1 assistant in production
- Documentation and access
- A guide for the team
- Before/after measurements
- Takes 3–5 weeks
For those scaling AI across several processes who need ongoing support.
- A monthly task backlog
- Regular releases
- Support and monitoring
- A metrics report every month
- A dedicated development team
The client pays for the AI models (OpenAI, Anthropic, Gemini) based on actual usage — we show these costs openly during the audit. A free 30-minute call gives you an estimate tailored to your processes and a plan for the first 90 days.
What we’ve already built
Two systems that live in production and serve real teams every day. Not pilots, not demos — full products built on the client’s data.
Themis — a private AI lawyer
A private AI system for a lawyer: it pulls a case from the registry, parses the evidence, and drafts documents with links back to sources.
Build an AI assistant for casework that doesn’t make things up, leans on sources, and doesn’t cost tens of dollars per run.
Evidence recognition 40–50× cheaper (from ~$0.30 to ~$0.006), case assembly cut from ~a day to ~30 min, 3 LLMs cross-checking each other.
geo-intel — a single data hub
We pulled leads, analytics, competitors and broken links from a network of sites and partners into one dashboard instead of eight browser tabs.
Bring scattered data from 8 sources (GA4, GSC, Telegram leads, Sheets, competitors) into one interface with guest access for partners.
121 leads and €81,075 of visible pipeline in real time, monitoring of 8 competitors with before/after, 138 broken links found, weekly briefings in Telegram.
Why pick Chyzh Agency for AI integration
Chyzh Agency has built production software since 2011, holds official Google Partner status, and treats AI as an engineering problem, not as hype.
Products in production, not wrappers
With Themis and geo-intel we’ve already built systems that live in production and account for every dollar. Search over your data, not an off-the-shelf chat.
Full cycle and a real stack
14 years of development, a stack of Python, Node.js, React, Laravel, Flutter and DevOps. You’re not coordinating three contractors — it’s all under one roof.
Transparent token economics
We show the cost of each operation up front and keep track of spend. On Themis this made processing 40–50× cheaper with no loss of quality.
The difference between a demo and a tool
“We don’t “drop ChatGPT onto a site.” We make the model run on a specific company’s data, lean on its sources, and account for every dollar spent. A finished product in production, not a pitch deck — that’s what people pay for.
Yevhenii ChyzhFounder of Chyzh Agency · AI Integration
Questions and answers
How is this different from just dropping in ChatGPT?
We don’t bolt on an off-the-shelf chat. We build a system on your data: search over your own documents and databases (RAG), answers that link back to sources, and cost control on every operation. The result is a working product in production, not a demo.
Do we have to train our own AI model?
No. We connect ready-made APIs from OpenAI, Anthropic and Gemini to your systems. Building your own model from scratch is expensive and rarely needed for business tasks. When it helps, we tune the models to your data without training from scratch.
Who pays for the AI models?
The client pays the API cost based on actual usage. We show these costs openly during the audit and build them into the ROI calculation. On the Themis case we made evidence processing 40–50× cheaper precisely by routing tasks to cheaper models.
Where do we start?
With an AI Audit. In 1–2 weeks we map your processes, calculate the ROI on the priority tasks, and give you a 3–6 month plan. The cost of the audit is credited toward the work that follows.
What don't you do?
We don’t train models from scratch (we integrate ready-made ones), we don’t hand-label large datasets, and we don’t take on managing GPU infrastructure. And we don’t promise specific AI metrics without a pilot — we measure first.
How long does an implementation take?
The audit takes 1–2 weeks. The first pilot with a working solution in production takes 3–5 weeks. After that, development continues in releases under a partnership. Exact timelines depend on the scope and the state of your systems.
Let’s work out what AI does for your team specifically
A free 30-minute call — we’ll find the processes where AI saves hours this quarter and give you a plan for the first 90 days. No commitment.
Or email us directly: hello@chyzh.agency