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Service Area

AI & ML
Implementation

Most companies are stuck in AI pilot purgatory — interesting demos that never make it to production. We help you move from experimentation to systems that work reliably at scale and deliver real ROI.

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The Challenge

AI doesn't fail because of the model. It fails because of the data and the process.

The AI failure mode we see most often isn't a model that can't learn — it's a use case that wasn't scoped to a real business problem, running on data that isn't clean enough to train on, with no production infrastructure to deploy into, and no process for monitoring drift or updating the model after launch.

We approach AI and ML differently. Before writing a line of model code, we ask: what decision does this model need to support, how will the output actually get used, and what happens when it's wrong? The answers to those questions determine everything else.

The result is AI that your business can depend on — not demos that impress in a meeting and disappear six months later.

87%
of ML models never make it from development to production — the most common reason is poor data quality
40%+
forecasting accuracy improvement achieved for a retail client through demand prediction ML implementation
6–12 wk
typical time to a production-deployed LLM application from scoped requirements and clean data

Types of Projects

What an AI & ML engagement looks like

From LLM-powered internal tools to production ML models, here are the types of work we take on — and what each one delivers.

💬
LLM / GenAI

LLM Integration & Internal AI Tools

Connect large language models to your internal data, documents, and workflows to automate knowledge work, surface insights, and reduce the time your team spends on repetitive tasks. Built with proper guardrails, access controls, and logging — not just a chatbot wrapper around an API.

What you get
  • Production-deployed LLM application with auth and logging
  • Prompt engineering and evaluation framework
  • Latency and cost optimization
  • Feedback loop for continuous improvement
📚
LLM / GenAI

RAG System Design & Build

Retrieval-Augmented Generation allows LLMs to answer questions grounded in your specific data — contracts, documentation, support tickets, product knowledge bases. We design and implement RAG architectures that balance accuracy, latency, and cost at production scale.

What you get
  • Designed vector store and chunking strategy for your content type
  • Retrieval pipeline with hybrid search (dense + sparse)
  • Evaluated accuracy with a benchmark test suite
  • Production deployment with monitoring and refresh cadence
📉
Predictive ML

Predictive Analytics & Forecasting

Demand forecasting, churn prediction, lead scoring, revenue forecasting — trained on your historical data and deployed into your decision-making workflows. We scope these to specific business decisions with measurable impact, not academic exercises.

What you get
  • Trained and validated model with documented feature logic
  • Baseline comparison showing improvement over current approach
  • Integration into the workflow where the prediction is used
  • Monitoring for data drift and model degradation
⚙️
MLOps

ML Infrastructure & MLOps

For teams that have models in development but can't get them to production reliably — or who have models in production but no process for retraining, monitoring, or governance. We build the infrastructure that makes ML operations repeatable and trustworthy.

What you get
  • ML platform design (feature store, model registry, serving layer)
  • CI/CD pipeline for model training and deployment
  • Monitoring stack for drift detection and performance regression
  • Runbook and team training for ongoing operations
🗺️
Strategy

AI Readiness Assessment & Roadmap

Before you invest in AI, know whether your organization is actually ready. We assess your data maturity, infrastructure, team capabilities, and candidate use cases — and deliver a prioritized AI roadmap that sequences investments in the right order and sets realistic expectations.

What you get
  • AI readiness scorecard across 5 dimensions
  • Prioritized use case inventory with ROI estimates
  • Gap analysis: what needs to be true before each use case is viable
  • 12-month sequenced roadmap with build/buy/partner recommendations
🔄
Automation

AI-Powered Workflow Automation

Identify the high-volume, rule-based, or judgment-intensive workflows in your organization that are ripe for AI augmentation — and build the automation that actually reduces cost or improves output quality. Document processing, classification, routing, summarization, and more.

What you get
  • Automated workflow with human-in-the-loop review where appropriate
  • Accuracy and throughput benchmarks vs. manual baseline
  • Cost-per-unit analysis showing ROI
  • Handoff documentation and process integration

Our Approach

How we think about responsible AI in the enterprise

🎯

Business case first

Every AI project we take on starts with a specific decision, process, or outcome it's meant to improve. If we can't articulate the ROI before writing code, we don't start.

🔍

Data quality gates

We assess data quality before we scope the model. If the data isn't good enough to support the use case, we'll tell you — and help you fix it first.

🧪

Rigorous evaluation

Every model is evaluated against a business-relevant benchmark — not just accuracy metrics that sound good in a slide. We're explicit about what the model gets wrong and how often.

🔭

Production isn't the end

We design monitoring, retraining triggers, and escalation paths from day one. Models degrade. The question is whether you have a process to catch it before the business feels it.

Tech Ecosystem

Tools & frameworks we work with

We stay current with the rapidly evolving AI landscape — and we give you honest guidance on what's production-ready vs. what's still a demo.

LLM Providers & APIs

OpenAI (GPT-4o) Anthropic (Claude) Google Gemini AWS Bedrock Azure OpenAI

RAG & Orchestration

LangChain LlamaIndex Pinecone Weaviate pgvector ChromaDB

ML & Python Ecosystem

Python scikit-learn XGBoost / LightGBM PyTorch MLflow Weights & Biases

Deployment & MLOps

SageMaker Vertex AI Databricks ML FastAPI Docker / Kubernetes Evidently AI

Engagement Models

How we structure this work

Scoped to your level of AI maturity and the complexity of the use case.

Common Questions

What people ask before starting

In most cases, it means three things: your data is clean enough and accessible enough to train on or retrieve from, you have at least one specific use case where a model would make a real difference, and you have the infrastructure to deploy and maintain something in production. Many companies are close on all three but have gaps that need to be filled in sequence. Our AI readiness assessment identifies exactly which gaps matter most for your specific goals.
It depends heavily on the use case, your data's competitive value, and your team's ability to maintain what gets built. For many workflows, a well-configured SaaS tool or a light LLM wrapper will outperform a custom model in cost-effectiveness. For use cases where your proprietary data is the differentiator, custom beats generic. We'll tell you honestly which bucket you're in — and we're not biased toward building because we charge more for it.
Every AI system we build has an explicit error budget and a human escalation path. For high-stakes decisions, we design human-in-the-loop review flows. For LLM applications, we use RAG with grounded sources, structured outputs where possible, and evaluation test suites that measure factual accuracy on your specific content. "Good enough" is defined by the business context, not abstract benchmarks.
Every engagement includes a handoff package: documentation, a monitoring runbook, defined retraining triggers, and a support window. For clients who want ongoing oversight, we offer a fractional AI advisor arrangement. We won't deploy something into your business and disappear — that's how you end up with a model that degrades silently for six months before anyone notices.

Related Services

Services that lay the groundwork

🏗️

Data Strategy & Architecture

AI is only as good as the data it runs on. We often address this first.

→
📊

Analytics & Business Intelligence

Augment your BI environment with predictive and generative AI features.

→

Ready to move past the pilot phase?

Let's talk about your AI use cases and what it would actually take to get them into production.

Book a Free Discovery Call →