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Services / AI & Machine Learning

AI/ML Solutions

We build AI that your team can trust and explain. Practical intelligence embedded in your systems, not a black box nobody understands. We use AI daily in our own delivery, and we built RothAI, a multi-model AI platform running in daily production at Roth & Co.

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Technology

The AI stack we build with

Model choice is an implementation detail. These are the tools and the places we have put them into production.

Models and techniques

  • Large language models
  • Retrieval-augmented generation
  • NLP extraction
  • Document OCR
  • Speech-to-text
  • Classification
  • Forecasting
  • Anomaly detection

Frontier models

  • OpenAI
  • Anthropic Claude
  • Google Gemini
  • Multi-model routing

Engineering

  • Python
  • .NET
  • Vector search
  • Prompt and eval pipelines
  • Retraining pipelines

Deployment and governance

  • AWS
  • Microsoft Azure
  • Usage reporting
  • Budget controls
  • Role-based access
  • Drift monitoring

Most companies did not decide to adopt AI. Their employees did it for them. By the time leadership discusses an AI strategy, staff are already pasting data into public tools, departments are expensing overlapping subscriptions, and nobody can say what any of it costs. We know this pattern well, because we lived it. When Roth & Co needed secure, multi-model AI access with real cost control, we built the platform. RothAI now runs there in daily production, with usage reporting, budget controls, and permission-based access.

That working experience shapes our client work. We build applied machine learning inside business systems. The pricing intelligence platform in our case studies watches competitor prices across more than fifteen retail categories and turns them into same-day decisions. In veterinary practice software we put language models into the clinical workflow, generating pre-appointment summaries from a patient's full history and turning recorded consultations into structured records. In property management we put a tenant assistant in front of a portfolio of more than 24,000 units, where it settles around seven in ten enquiries without a manager. In insurance we automated claims intake, where nine in ten submissions now reach a decision without anyone reading the document. We also build governed AI platforms that give teams the models they prefer without giving up security, visibility, or budget control. AI is also part of how we run projects internally, from analysis through delivery, which keeps our own work fast and our estimates honest.

Our approach starts from the business outcome, not the technology. We define what the model must achieve before we build it, we validate it against your real data, and we deploy it with the monitoring that keeps it accurate as your business changes. AI that cannot be explained, governed, and priced is not an asset, so we only build the kind that can.

What AI looks like when it's built for your business

  • Intelligent Automation

    Automate repetitive, high-volume tasks with models that learn from your data and improve over time without manual retraining.

  • Predictive Analytics

    Turn historical data into forward-looking intelligence, including demand forecasting, churn prediction, anomaly detection, and pricing signals.

  • Custom Model Development

    Models trained on your data, not generic datasets, calibrated to your domain, your edge cases, and your accuracy requirements.

  • Fits your existing stack

    AI capabilities embedded into the software you already run, through APIs and integrations, without rebuilding your platform from scratch.

  • Governed and Explainable

    Transparent models with clear output reasoning, usage reporting, and access controls. This matters for regulated industries and for leadership that needs to trust both the output and the spend.

Our AI Development Process

  1. 1

    Problem Definition

    Defining the outcome before choosing a technology.

    • Business Goal Mapping

      Identifying which business outcomes AI can measurably improve, and which problems it cannot solve.

    • Success Metrics

      Defining evaluation criteria before any model is built, including accuracy targets, latency budgets, and acceptable error rates.

  2. 2

    Data Assessment

    Establishing whether your data can support the outcome.

    • Data Audit

      Assessing data quality, completeness, and coverage across your sources.

    • Feature Engineering

      Transforming raw data into meaningful signals that improve model performance.

    • Data Pipeline Design

      Building reliable, automated pipelines that keep training and inference data fresh.

  3. 3

    Model Development

    Selecting and training the model the problem calls for.

    • Architecture Selection

      Choosing the right model family, from classical ML to LLMs, based on your data, latency, and cost requirements.

    • Training and Validation

      Rigorous train/validation/test splits with cross-validation to prevent overfitting.

    • Bias and Fairness Review

      Evaluating model outputs for unintended bias before any production deployment.

  4. 4

    Deployment and Monitoring

    Production AI requires production engineering.

    • Serving Infrastructure

      Low-latency, scalable inference endpoints with appropriate hardware selection.

    • Drift Detection

      Continuous monitoring for data drift and model degradation in production.

    • Retraining Pipelines

      Automated retraining triggered by performance thresholds, not manual calendar reminders.

Why RothTech

Most AI vendors sell either a demo or a subscription. We build working systems, with models embedded in your operation, governed access to the AI tools your team already uses, and reporting that tells leadership what it all costs. We ran into these problems inside our own group and built RothAI to solve them; that experience is part of every engagement.

Frequently Asked Questions

Not every problem needs it. The first step of every engagement is mapping which business outcomes AI can measurably improve. Where a conventional system, an integration, or a better report solves the problem for less, that is what we recommend. AI earns its place when the problem genuinely involves prediction, language, or volume beyond what rules can handle.

The same way we price everything, scoped and quoted in full before you commit. Cost drivers for AI work are data readiness, integration depth, and how accurate the output must be before it is useful. Model usage costs are part of the quote, with usage reporting built in, so AI spending never becomes a black box on your P&L.

It stays yours. Models trained on your data are your asset, and we design deployments so sensitive data does not leak into public AI tools. Access controls, guardrails, and usage visibility are part of the build, not an afterthought. This is the same standard we applied when building RothAI.

It is the most common situation we walk into, and the honest answer is that unmanaged AI use carries real risk, from client data in public tools to duplicate subscriptions and no oversight. We built RothAI to solve exactly this, giving employees the models they prefer inside one governed environment with budgets and reporting. We build the same for clients.

We are deliberately model-agnostic. Classical machine learning where the data calls for it, large language models where language is the problem, and multiple frontier models side by side where teams have different needs. The model is an implementation choice; the requirement is that it serves the outcome we agreed on.

Related Case Studies

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