figtures
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    Commercial Pillar Guide

    How to pick a production-grade AI development partner in Turkey

    Related service: AI Development

    PoC vs production

    Most AI projects in Turkey stall at PoC because eval, fallback, cost, and security layers are unplanned. Production AI needs prompt + retrieval + tool-call + monitoring + versioning discipline.

    A strong AI partner asks which user decision accelerates before which model. Model choice follows metrics: accuracy, latency, token cost.

    LLM, agents, automation: when to use what

    Chat assistants generate answers; agent workflows orchestrate multi-step tool use; automation accelerates repeatable ops with human approval. Pick one high-value flow before blending all three.

    Cost and token economics

    AI cost is not just build—it is inference burn rate. Caching, routing, smaller models, batching, and context trimming are production requirements. Figtures sets cost ceilings and alerts per feature.

    Security, privacy, and leakage prevention

    Enterprise AI needs PII redaction, prompt-injection defense, and audit logs. For Turkey operations, data residency and KVKK processes are clarified in discovery.

    Figtures AI delivery framework

    We progress discovery → eval set → guardrails → integration → hardening. Each sprint ends with demos measured on real product data—not slides.

    Figtures delivery scope

    Deliverables

    • • LLM features (chat, summarization, extraction)
    • • Agent workflows and tool-integration layer
    • • Evaluation, monitoring, and fallback setup
    • • Product API and frontend integration

    Process

    1. Discovery

      We define use cases, risks, and success metrics.

    2. Architecture

      We design model, prompt, retrieval, and tool chain.

    3. Build

      We implement production-grade features in your product.

    4. Hardening

      We harden with observability, quality, and cost control.

    Comparison

    CriteriaAlternativeFigtures
    Eval disciplineManual "looks good" testingScenario sets + regression + cost tracking
    Product integrationDetached chat widgetAI embedded in core UX flows

    Sample timeline

    Use-case discovery

    1 week

    Metrics + risk matrix

    Production sprint

    4–8 weeks

    Live AI feature

    Frequently Asked Questions

    Is ChatGPT integration enough?+

    Integration is a start—production is risky without eval, fallback, and cost control.

    Which models do you support?+

    OpenAI, Anthropic, and open models—chosen by use-case metrics.

    Can you add AI to an existing product?+

    Yes. We audit the current architecture and integrate AI features incrementally with low migration risk.

    Do you handle model strategy and cost optimization?+

    Yes. We set model/runtime strategy around quality, latency, and cost tradeoffs.

    Do you only consult, or also build?+

    We work from strategy through production delivery, including embedded execution with your team.

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