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AI & ML systems that actually ship.

LLM integrations, custom ML models, and intelligent automation — engineered for production, not demo day. We handle everything from data pipelines to inference infrastructure.

[02]
§ Capability

What we build.

  • [01] RAG · Agents · Evals

    LLM integrations

    RAG over your docs, function-calling agents, evals and guardrails. Claude 4, GPT-5, Llama 4 — whatever fits.

  • [02] GBM · Transformers · XGBoost

    Custom models

    Gradient boosting, fine-tuned transformers, classical ML. Trained on your data, deployed to your infra.

  • [03] YOLO · OCR · CLIP

    Vision systems

    Detection, classification, OCR, and document understanding — edge or cloud, your choice.

  • [04] MLflow · DVC · Weights & Biases

    MLOps

    Training pipelines, model registry, drift monitoring, CI/CD for ML. Your team keeps what we build.

[03]
§ Process

How an engagement unfolds.

  • Wk 0

    Discovery

    Data audit, stakeholder interviews, success metric.

  • Wk 1

    Spec + POC

    Tight written scope. Throwaway POC on real data.

  • Wk 2-4

    Build

    Model + infra + eval harness. Weekly demos.

  • Wk 5

    Ship

    Staged rollout, dashboards, runbook handoff.

[04]
§ Stack

Tools we reach for.

We pick the boring best tool for the job. Here's what most AI engagements lean on.

  • § Languages
    PythonTypeScriptRust (edge)
  • § Models
    Claude 4 (Opus / Sonnet / Haiku)GPT-5Llama 4Fine-tuned encoders
  • § Infra
    AWSModalReplicateGroq
  • § MLOps
    MLflowDVCWeights & BiasesLangSmith
  • § Data
    PostgrespgvectorSnowflakeParquet
  • § Serving
    FastAPIvLLMTritonLambda
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