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04 · Service

AI Solutions

We implement AI where it creates clear value: assistants, document intelligence, classification, search, and agent-style workflows — with evaluation and guardrails, not hype.

Problem

Generic AI demos rarely survive contact with production data, privacy requirements, or user expectations.

Solution

We scope tightly, use the right models and retrieval patterns, and ship features you can measure and maintain.

Value

AI that saves time or improves quality in a specific workflow — not a novelty chat box.

What you get

  • LLM-powered assistants
  • Document Q&A and extraction
  • Classification & tagging
  • Semantic search
  • Agent-style automations
  • Evaluation & monitoring

Process

  1. 01Use-case and data assessment
  2. 02Prototype and evaluation metrics
  3. 03Architecture and safety constraints
  4. 04Integration into product or ops
  5. 05Monitoring and iteration

Use cases

  • Internal knowledge assistants
  • Support ticket triage
  • Content and data extraction
  • Product recommendation logic
  • Ops decision support

Technologies

  • OpenAI / Anthropic APIs
  • Python
  • Vector stores
  • Next.js

FAQ

Do you train custom models?

Usually we fine-tune or RAG on top of strong foundation models. Full custom training is rare and only when data and ROI justify it.

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