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    AI / ML

    AI / ML — Pragmatic Models, Production-Ready

    ML is a business investment, not a research project. We build models tuned for the constraints that actually matter — latency, cost, drift, and the messy reality of your data.

    What you get

    RAG architecture with retrieval evaluation, not just vector-search guesswork
    Fine-tuning strategy: when it's worth it, when prompt engineering wins, and when neither is the answer
    Small-language-model strategy for cost-sensitive or on-prem deployments
    Data engineering and curation pipeline — the part most teams skip and pay for later
    MLOps: model registry, automated evaluation, drift monitoring, retraining triggers
    Model selection analysis comparing cost, accuracy, and latency across realistic options
    Infrastructure & deployment blueprint matched to your cloud and compliance posture
    Security & data-governance framework for model access, prompts, and outputs

    When it fits

    • You have data — even if it's messy — and a workflow where accuracy is measurable
    • You're past the 'is AI useful?' question and need engineering, not enthusiasm
    • Latency, cost, or compliance constraints make the obvious 'just call GPT' answer wrong
    • You want a model you can update, monitor, and reason about — not a black box

    When it doesn't

    • The data is too sparse or too noisy to learn from, and nobody's willing to fix it
    • The problem is genuinely better solved by deterministic logic or a SaaS product
    • Accuracy can't be measured, so 'better' will always be a matter of opinion

    Process

    Discovery starts with a data audit and a baseline — almost always a simple retrieval or zero-shot baseline that the fancy model has to beat. We build the evaluation harness before the model. Iteration is then driven by the harness, not by intuition. MLOps is wired in from the first deployment, not bolted on later.

    Full delivery process

    Pricing

    Fixed-price discovery (1–3 weeks). Implementation runs fixed-price by milestone or as a dedicated team for longer engagements. Inference cost modeling is included so you know unit economics before launch.

    See engagement models

    Case studies

    Financial Technology

    FinTech Mobile Banking Platform

    Secure, AI-powered mobile banking serving 500K+ users with instant transfers and biometric authentication.

    E-Commerce & Retail

    Multi-Vendor E-Commerce Platform

    Scalable marketplace processing $10M+ monthly with AI recommendations and real-time inventory management.

    FAQ

    RAG, fine-tuning, or agents — how do we choose?
    RAG when the answer lives in your documents and freshness matters. Fine-tuning when you need a specific format, tone, or domain capability that prompts can't reliably get to. Agents when the workflow needs actions across systems. Most production systems use two of the three; few need all of them.
    Do you build custom models or use existing ones?
    Default is to use existing foundation models with retrieval and prompting; that's usually right. We build or fine-tune custom models when there's a measurable accuracy, latency, or cost reason to — and we'll show you the math during discovery rather than defaulting to whichever is more interesting to build.
    How do you handle MLOps and model drift?
    Model registry, automated eval on every release, drift dashboards on production data, and retraining triggers tied to eval thresholds. The goal is that 'is the model degraded?' is answered by the dashboard, not by waiting for a user complaint.
    What about HIPAA, SOC 2, and data residency?
    We've shipped models in regulated environments and design for it from the start: VPC-only deployments, audit logging, and model providers selected for the residency rules you actually need. Compliance is part of the architecture, not an afterthought.

    Ready to talk ai / ml?

    30-minute scoping call. No obligation, no hard sell.