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Your AI product, built end to end, on your own infrastructure.

We build the custom AI system your business needs, the core product or the automation, and deploy it on your cloud (AWS, GCP, or Azure) or your own on-prem hardware. Your data never leaves your environment, and we have delivered systems like this for enterprises before.

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10+ years in ML & LLM systems
20+ AI projects delivered
3 continents of clients

What we build

Production-grade AI systems, built end to end and run where your data already lives.

These are the patterns enterprises in finance, FMCG, e-commerce, SaaS, and manufacturing put into production with us. Each one is built end to end and deployed on the client's own infrastructure, whatever it is: their cloud or their on-prem hardware, shaped to their data.

01

A private LLM platform on your own infrastructure

What you get: your own ChatGPT-style platform, your data, your keys.

Your people get a private, ChatGPT-style assistant that runs on your own infrastructure, your cloud (AWS, GCP, or Azure) or your on-prem hardware, and answers from your data, never a shared vendor cloud. Your existing applications, any stack, connect to it through a standard, OpenAI-compatible API, so adoption is a config change, not a rebuild. Open-source models are chosen by comparative testing on your own data, and access control, usage logging, and monitoring come built in.

Open-source models
Your infrastructure
OpenAI-compatible API
Access control
Usage logging
02

A whole process runs itself end to end, checked at every step, decided by you

What you get: a team of AI agents that runs a full workflow across your systems, validates its own work, and stops for your approval at the decisions that matter.

A multi-step process that today moves by hand from one tool to the next now runs from start to finish across your CRM, ERP, email, and internal apps. An orchestrator agent plans the work and hands each part to a specialist agent that is good at one job, and the system keeps a plan, act, check, and correct cycle going until the goal is actually met. Before any action commits, a guardian check validates it against your own rules, so nothing off-policy goes out, and the agents still stop for your approval at the decisions that matter. Every step is logged and reversible, so you get a whole process handled reliably without giving up oversight.

03

Answers from your own documents, in plain language

What you get: a RAG knowledge assistant that cites its sources.

Your teams ask questions in plain language and get sourced answers from your own documents, procedures, and archives the same afternoon, plus natural-language access to your databases without writing SQL. Every answer cites where it came from, and when the system does not know, it says so instead of guessing.

Your documents
Retrieval
Cited answers
Says when unsure
04

The repetitive work runs itself

What you get: agents that read documents, extract data, and report.

The repetitive work that ties up your team, reading documents, pulling out structured data, compiling reports, keeping multilingual knowledge bases current, runs on its own through AI agents that coordinate across your systems. You keep the oversight and the approval points; the agents handle the volume.

Read documents
Extract data
Compile reports
Your approval
05

See cloud cost spikes in days, not at month-end

What you get: AI-driven FinOps that flags spend before it lands on the invoice.

You see a cost anomaly across AWS, Azure, and GCP within days of it starting, not when the invoice arrives, with the spike already attributed to the service that caused it. Your infrastructure gets scored for quality, and your leadership gets a written executive summary automatically. This ships as a production system that keeps running, not a dashboard that goes stale after a quarter.

Multi-cloud
Anomaly detection
Spike attribution
Alert in days

How we work

From workshop to production, one phase at a time.

A small senior team owns your project from the first day to go-live. From you we need access, one decision-maker and short sessions with the people who know the process. The rest is on us.

Phase 01

Discovery Workshop

Two structured days with your process owners.

You walk away with prioritized use cases, a capacity plan sized for your own infrastructure, and a phased roadmap you can act on, whether or not you continue with us.

Phase 02

Platform & Pilot

The foundation goes up on your own infrastructure and your first use case goes live at pilot scope, with real data and real users.

You walk away with a working system measured against the acceptance criteria you agreed to, not a slide deck.

Phase 03

Scale

More use cases on the same foundation, each one delivered as its own phase with its own result.

The decision to continue is always yours, and every phase closes with something running in production.

Fixed-price phases. No long lock-ins, no vendor trap. You keep full knowledge transfer at the end, documentation and training, no black boxes.

Selected work

Shipped, in production, measured.

Client names are withheld by agreement. These are the same engagements we walk through in proposals, each one built on the client's own infrastructure.

01

A Türkiye-based global beverage producer

Multi-Cloud Cost Intelligence Platform

Automated cost monitoring across AWS, Azure, and GCP: anomaly detection with service-level spike attribution, a cross-cloud unification layer, daily email and Microsoft Teams alerts, and Power BI reporting. In production, surfacing cost spikes within 1 to 3 days instead of at month-end.

AWSAzureGCPPythonPower BI

02

A digital mental-wellbeing product company

Real-Time Multi-Agent Therapeutic Conversation Platform

A production conversational AI for mental wellbeing, built on the client's own AWS cloud, that delivers clinically-informed, therapeutic-style support through a guided vent, reflect, reframe flow. A conductor agent routes each exchange and directs specialized agents for category detection, conversation phase, emotion, and CBT pattern recognition, with the blocking response path kept separate from background analysis so the user never waits. A two-tier safety layer screens every message and routes at-risk users to professional crisis resources in under 850 milliseconds, while the response itself streams word by word with a first token in roughly 1.9 seconds. Model selection delivered 100% reliable structured output for the detection agents and prompt caching brought LLM running cost down by about 90%.

PythonFastAPIClaudeLangChainRedisDynamoDB

03

A commercial construction subcontractor

Bid-Fit Scoring from Architectural Plans

When a new project goes out to tender, the client's estimators were opening every architectural PDF by hand to decide bid or no-bid, so strong projects slipped past and time went to poor-fit ones. We built a pipeline on the client's own Google Cloud that reads each project's plans, including image-based title blocks, with OCR and an LLM extraction step, pulls out square footage, owner, and building type, and turns mixed image-and-text plans into one bid-fit confidence score, so the team only chases the projects worth their time. A secure web app lets estimators tune the scoring and capture feedback, and that feedback sharpens future scores.

PythonGCPGKEOCRGPT-4oBigQuery

Our products

Same team, same standards.

Our own products, built with the discipline of our client work: measured results and data that stays where you decide. If one of them fits, write to us and we will walk you through it.

TestMy.AITestMy.AI

Know how often your AI fails under attack.

An independent black-box security assessment of your LLM or agent endpoint, tested from the outside the way an attacker would, prompt injection and jailbreaks included. You see how often it fails, not a single pass or fail.

  • 800+ tests · 25 categories
  • OWASP LLM + Agentic Top 10
  • Every test run 5+ times
Visit TestMy.AI ↗
FonTakip

IPO proceeds tracked daily, the KAP report drafted.

For finance teams at Borsa Istanbul listed companies: see every day where the IPO proceeds went, against the uses declared in the prospectus. FonTakip drafts the use-of-proceeds report; your committee reviews it and your company files it on KAP.

  • Rule-based and auditable
  • Offline, on your own PC
  • Turkish interface
Request a demo
CCB Company Data

Company data, filtered to your market.

A global B2B company database with a written profile for every company: what it does, what it sells, where it operates and which companies it is linked to. Filter it to the sectors and countries you sell to, and take the list into your CRM or our lead-generation agent.

  • About 150,000 companies worldwide
  • Filter by sector, products and location
  • Groups, subsidiaries and partners
Request a sample list
Lead-generation agent

Runs in your own cloud

A lead-generation agent that keeps your prospect data inside.

Our lead-generation agent runs on the same privacy-first architecture we deliver to consulting clients. The agent runs in your own cloud and your prospect data never leaves your environment; our central intelligence API sends back only researched, scored leads.

  • Runs in your cloud, or managed for you
  • Weekly outcome report
  • Your CRM owns the records
Request a demo
Lead-generation agent: two clouds, one perimeterProspect records, email logs and your CRM stay inside your cloud, where the agent runs. Researched, scored leads come in from the CCB-AI intelligence API, and only anonymous aggregate metrics go out through a narrow gate, so we can confirm the agent is working properly. Raw prospect data reaches the perimeter and never crosses it.YOUR CLOUD · AWS · GCP · AZURECCB-AI · INTELLIGENCE APIprospect recordsemail + repliesICP + signalsagent · runningCRM · calendarstays here.PERIMETERMETRICS GATEleads in. aggregates out.meeting_bookedscored_leadsopen_rateprospect_emailcontact_list
Crosses the line: scored leads in, aggregate metrics outNever leaves: names, emails, message bodies, contact lists

How it works

WK 01

You set the brief

You define your ideal customer profile and what counts as a qualified meeting.

WK 02

It deploys in your cloud

The agent is set up inside your own AWS, GCP, or Azure account.

WK 03

You approve before it sends

Nothing goes out until you have reviewed and approved the outreach.

WK 04 →

Leads land on your desk

Researched, scored leads and a short report, on your desk every Monday.

Quality & evaluation

AI quality you can measure.

Shipping untested AI is like manufacturing without quality control.

Every system you receive from us comes with scenario-based test sets, measured accuracy and hallucination rates, and acceptance criteria agreed before go-live. The quality bar is not a promise, it is a number you sign off on.

Common questions

The questions enterprise teams ask us first.

  • No. We deploy the AI system on your own infrastructure, your cloud (AWS, GCP, or Azure) or your on-prem hardware, so your documents, prompts, and outputs stay in your environment under your keys. Nothing is sent to a shared vendor cloud or to a third-party model API unless you explicitly choose that.
  • Yes. We set up a private, ChatGPT-style platform on your own infrastructure, running open-source models served behind an OpenAI-compatible API, so it answers from your data, not a public service. Your existing applications connect to it with a simple config change rather than a rebuild.
  • Every project starts with a two-day discovery workshop, and the rest of the timeline is set there, in phases sized to your environment and scope. The first build phase takes one use case live at pilot scope, with real data and real users. We deploy the same way whether your infrastructure is your cloud (AWS, GCP, or Azure) or your own on-prem hardware. Each phase ends with a working deliverable, and the decision to continue to the next phase is always yours.
  • No. We work in fixed-price phases with no long lock-ins, and every engagement ends with full knowledge transfer: documentation and training so your team can run the system without us. The platform runs on open-source models on your own infrastructure, so you are never dependent on a single proprietary API.
  • Because your data stays on your own infrastructure and under your control, the architecture is built to make GDPR and KVKK compliance straightforward for your privacy and legal teams. We do not claim certification on your behalf; we give you a design where data residency and access control are yours to govern.
  • Every system ships with scenario-based test sets run against your real cases, with measured accuracy and hallucination rates and acceptance criteria agreed before go-live. Retrieval-based answers cite their sources, and when the system does not know, it is built to say so rather than guess.
  • Yes. We build an internal knowledge chatbot that runs on your own servers or in your own cloud account (AWS, GCP or Azure) and answers from your documents, procedures and archives. Every answer shows the source it came from, access control is built in, and when it does not know, it says so. No data goes to OpenAI or any other outside model provider unless you decide it should.
  • Yes. We build document automation that reads invoices, contracts and forms, scanned ones included, extracts the fields you need, checks them against your rules and writes them into your ERP or database. Anything the system is unsure about goes to a person for review instead of being guessed, and the whole pipeline can run on your own infrastructure, so the documents never leave.
  • Yes. We set up automated report generation that pulls figures from your databases, spreadsheets and documents, writes the commentary in plain language and delivers the finished report on your schedule, by email, Microsoft Teams or as a file. The numbers come straight from the source and are traceable; the model writes the narrative, not the figures. You decide which reports wait for a person's approval before they go out.
  • Less than most teams expect. From your side we need three things: access to the relevant systems and data, one decision-maker who answers questions and signs off each phase, and short sessions with the people who know the process. A small senior team owns the rest from the first day: we map your systems, write the code, test it and take it live ourselves. Most of those sessions happen during the two-day discovery workshop and at acceptance. That is what end-to-end AI project delivery means to us.
  • No to both. We work with the stack you already run, cloud or on-prem, CRM, ERP, databases, .NET or anything else, and connect to it through standard APIs instead of asking you to rebuild. You do not need in-house AI engineers either: we bring the senior people, and at handover your team gets the documentation and training to run the system day to day.

About

Meet the founder.

Burcin Sarac, founder and AI architect at CCB AI Solutions, Istanbul
Burcin SaracIST · 41.0082°N

Burcin Sarac

Founder · AI Architect · Istanbul

Burcin is an AI engineer with 10+ years across machine learning, NLP, and LLM systems, and the founder of CCB AI Solutions, an enterprise AI consultancy based in Istanbul, Turkey.

He has delivered tailored AI products for clients across Europe, MENA, and the US, and is a member of the Toptal network.

CCB AI Solutions is a resident company at Dijitalpark Teknokent, a technology park in Ataşehir, Istanbul, Turkey.

US invoicing available

Book a discovery call about your AI initiative.

Book a free 30-minute consultation, whether that is a private LLM platform in your own cloud, a specific automation you want off your team's plate, or one of our products: TestMy.AI, FonTakip, the lead-generation agent or CCB Company Data. No pitch deck, no pressure, and we reply within one business day.

or write directly: info@ccb-ai.com

Dijitalpark Teknokent, Ataşehir, Istanbul, Turkey

Resident at

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Custom AI Development on Your Infrastructure | CCB AI