Matter Innotech Matter Innotech
← Back to services

AI Solutions

Practical AI that solves real business problems, not proofs of concept. We build LLM-powered features (chatbots, document search, summarization, entity extraction), computer-vision pipelines (OCR, defect detection, quality control), and predictive analytics — using OpenAI, Anthropic Claude, and open-source models where they fit. Every project starts with a clear use case and ROI target, not hype.

Purpose

Ship AI features that move a real business metric — support cost, conversion, defect rate — not demos that sit in a slide deck.

Who it's for

  • Product teams who want AI-native features (search, summarization, chat) inside an existing product.
  • Operations teams with backlogs of documents, images, or tickets that a human currently has to read.
  • Leadership evaluating where AI can create measurable value before committing budget.

What we build

1. LLM-powered features

  • Chatbots & assistants — grounded on your data with retrieval-augmented generation (RAG).
  • Document search — semantic search across contracts, tickets, wiki, PDFs.
  • Summarization — meeting notes, long emails, support-ticket recaps.
  • Entity extraction — pull structured fields (dates, amounts, parties) out of unstructured text.

2. Computer vision

  • OCR — including handwriting and Thai/mixed-script documents.
  • Defect detection — manufacturing QA, image-based classification.
  • Object detection & counting — from CCTV, drone, or mobile inputs.

3. Predictive analytics

  • Forecasting — demand, churn, revenue, inventory.
  • Anomaly detection — surface unusual transactions or system behavior.
  • Recommendations — product, content, or next-best-action.

Models we use

  • Frontier LLMs — OpenAI GPT, Anthropic Claude, Google Gemini.
  • Open source — Llama, Mistral, Qwen for on-prem or air-gapped deployments.
  • Vision — CLIP, YOLO, custom fine-tuned CNNs.
  • Classical ML — XGBoost, scikit-learn where it beats deep learning on cost.

How we scope

  1. Use-case discovery — start with the metric to improve, not the technology to use.
  2. Baseline — measure the human-only status quo (accuracy, cost, latency).
  3. Prototype (2–4 weeks) — narrow-scope proof against real data.
  4. Evaluation — offline metrics + human review + red-teaming.
  5. Production hardening — evals in CI, guardrails, rate limits, fallback paths.
  6. Ship & monitor — dashboards for quality drift and cost.

Guardrails

  • Prompt injection defense — inputs are sanitized; system prompts are versioned.
  • PII redaction — sensitive data is masked before it reaches third-party LLMs.
  • Deterministic fallbacks — when the model can't answer, the user sees a helpful non-AI path.
  • Human review sampling — a slice of outputs is reviewed weekly to catch regression.

What we won't do

  • "AI everything" — if a rule-based system works better and cheaper, we'll say so.
  • Undefined success — no engagement starts without a concrete metric and target.