Service
AI & Automation
We turn artificial intelligence into enterprise solutions that run in production: LLM integrations, intelligent workflow automation and AI-powered product features.
AI that runs in production
Not demos, but solutions on duty in daily operations: document analysis, intelligent response systems, content generation. Output quality is measured with evaluation sets and kept under control with guardrails.
Enterprise workflow automation
Operational processes hundreds of steps long are automated: CRM synchronization, notification chains, reporting, data collection. Your teams shift from repetitive work to strategic work.
RAG and knowledge-base architectures
Your organizational knowledge is connected to AI: accurate, source-citing answers over your own documents. Data boundaries and access control are designed on day one.
Voice assistants and chatbots
Assistant systems that answer the phone, book appointments and log requests; built on the production experience of Sekreter.co, which holds real conversations in more than 50 languages.
How we work
- 01 Use Case The point where automation generates return is identified: which process, what volume, what gain. Every assessment is made in numbers.
- 02 Proof of Concept A limited-scope first version runs on real data within 2-4 weeks. Investment decisions rest on evidence.
- 03 Production Rollout Guardrails, evaluation sets, retry mechanisms and human approval steps are established. AI joins the process under control.
- 04 Monitoring & Governance Cost, latency and quality metrics are tracked on dashboards; model and prompt changes are managed by measurement.
A significant share of AI investments never makes it from proof of concept to production. That is where our difference begins: we deploy AI not as a showcase project but as an enterprise capability on duty in daily operations, with measured return on investment.
An approach validated in our own products
Sekreter.co is our voice-AI infrastructure holding real phone conversations in more than 50 languages; imzala.org runs AI-assisted document workflows in production. The same patterns carry into client work: Müşavir.co runs business processes with AI assistants across seven categories, and UXAudit.Now performs AI-assisted interface audits in production. Every architecture we propose rests on an example already validated under live traffic.
Typical use cases
The areas where we see the fastest return in organizations: voice assistants that answer and log inbound phone requests, document pipelines that extract structured data from contracts and invoices, source-citing question answering built on the corporate knowledge base, and workflow automations that move data between sales and operations tools. Every scenario starts from the same point: the current cost of the process is measured, and the return of automation is stated in numbers.
An engineering standard for automation
In n8n-based workflow automation, processes hundreds of steps long are built to production standard with retry mechanisms, error compensation and observability. On the LLM side, cost and latency control, evaluation sets and guardrails are standard components of every project. Data protection compliance is addressed at design time: which data may be sent to which model under which conditions is defined in the project’s first document.
Frequently asked
Which models do you work with?
The model that fits the task is selected, primarily Claude and GPT; where appropriate, several models are combined for the right cost-quality balance. Our commitment is to measurable outcomes, not to a vendor.
How is our corporate data protected?
Data classification and KVKK compliance are part of the design. Sensitive-data masking, data processing agreements and isolated deployment environments are planned together where required.
How are LLM costs controlled?
Cost monitoring is established from day one: per-request cost tracking, caching and model tiering keep the budget predictable. Spend is visible on dashboards in real time.
How is the risk of incorrect answers (hallucination) managed?
Source-citing answers through RAG, regular measurement with evaluation sets and human approval in critical flows. The areas where AI may not decide alone are defined at the outset.
Let's talk about your project.
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