AI integration services that ship to production

AI work usually starts with one feature and grows from there, so we build on a foundation that is observable, testable and easy to extend.

• Document search and RAG: question answering over contracts, technical manuals, policies and PDF archives, with every answer linked to its source.
• Generation and summarisation: proposal drafts, product descriptions, meeting summaries and suggested email replies.
• Classification and extraction: pipelines that read incoming tickets, invoices or forms and turn them into structured data.
• AI inside your admin tools: smart search, recommendations and auto-fill in the dashboards your team already uses.
• Agentic workflows: the model completes multi-step tasks using tools you define, such as a database query, a CRM update or a draft email awaiting approval.

AI is not the answer to every problem. It shines on work that is repetitive, text-heavy and hard to express as fixed rules: a support team triaging dozens of emails a day, a legal team searching hundreds of pages of regulation for the right clause, an online store that needs descriptions for thousands of products, or an operations team summarising field reports for managers.

In the first call we look at your process together and tell you plainly where a language model will help and where it will not. Sometimes the right answer is a simple automation or a better form, and we will say so.

Every piece of data sent to a model API is a design decision. We can mask personal data before it reaches the model, send only the fields a task needs, and review each provider's data retention terms against your requirements. For clients in Turkey we build KVKK obligations into the product design; for clients in the EU we work the same way with GDPR in mind.

To keep costs predictable we:

• Match the model to the task: a small, fast model for classification, a stronger one for hard reasoning.
• Use prompt caching and store results that repeat.
• Set per-user and per-day limits with monthly budget alerts.
• Log every call so you can see which feature spends how many tokens.

Every project is different, so we do not publish a price list. After a short call we send a free quote and propose packages that fit your budget. The main cost drivers are:

• How many data sources we connect and how clean they are (a tidy database and a messy PDF archive are very different jobs).
• Whether we add to existing software or build a new interface.
• The accuracy you need and how much testing that requires.
• User roles, permissions and audit logging.

Typical timelines: adding a single AI feature to an existing system takes 1–3 weeks, a RAG document search 3–6 weeks, and a broader AI module with several workflows 6–12 weeks.

Weras Labs is a small, senior software team led by founder Habip Dayan, an engineer who has built LLM-based systems on Google Cloud. Our own product, HookSlayer, runs AI in production every day, so prompt design, error handling and cost tracking are routine work for us, not theory.

There is no account manager in between: you talk directly to the person writing the code. We work with Next.js, React, Node.js, TypeScript, Firebase and Vercel, and use the Claude and Gemini APIs heavily. We are based in Turkey and work remotely with clients worldwide.