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Service Area

Enterprise AI
Implementation

Most companies are stuck in AI pilot purgatory: interesting demos that never make it to production. We deploy agent workflows, eval loops, and AI systems optimized for real value, not token spend, at enterprise scale.

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The Challenge

AI doesn't fail in the model. It fails before production, and again after nobody's watching.

The failure mode we see most often isn't a model that can't learn. It's a use case scoped to impress stakeholders instead of move a metric, an agent with no eval loop to catch drift, inference costs that make the economics impossible at volume, and no executive owner to push it past the pilot committee.

We approach enterprise AI differently. Before writing a line of code, we ask: what outcome does this need to deliver, what does "good enough" cost per task, and what happens when the agent is wrong? Then we build with eval harnesses, observability, and human gates where stakes are high.

The result is AI your business can depend on: scoped agent workflows that deliver real value, not demos that 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 agent workflow from scoped requirements and validated eval criteria

Types of Projects

What an AI & ML engagement looks like

From production agent workflows to predictive ML systems, here are the types of work we take on, scoped to outcomes rather than buzzwords.

Agents

Enterprise AI Agents & Workflow Automation

Multi-step agents that retrieve from your systems, call internal APIs, reason over documents, and complete real work, with eval loops, observability, and human-in-the-loop gates where stakes are high. Not chatbot wrappers. Scoped workflows tied to measurable outcomes.

What you get
  • Production agent workflows scoped to specific business outcomes
  • Tool integration with CRM, ERP, ticketing, and internal APIs
  • Knowledge retrieval architecture where grounding is required
  • Eval suite, observability, and cost-per-task analysis
LLM / GenAI

Production LLM Systems & Integrations

Enterprise LLM deployments built for production: auth, logging, structured outputs, model routing, and token efficiency optimization that keeps inference costs sustainable at volume. We maximize value per dollar, not tokens per request.

What you get
  • Production-deployed LLM application with auth and logging
  • Model routing and context design for cost-per-outcome efficiency
  • Prompt evaluation framework with business-relevant benchmarks
  • Build-eval-deploy loop for continuous improvement
Predictive ML

Predictive Analytics & Forecasting

Demand forecasting, churn prediction, lead scoring, and 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, then 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

Our Approach

How we think about responsible AI in the enterprise

01

Value per dollar, not demos per quarter

Every AI project starts with a specific outcome and a cost-per-task target. If we can't articulate the ROI before writing code, we don't start. Real value is the metric, not feature count.

02

Build-eval-deploy loops

Every agent and model ships with an eval harness, production observability, and feedback from real usage. We iterate in loops until the system meets business thresholds, not until the demo looks good.

⚡

AI economics at enterprise volume

Model routing, context design, caching, and token efficiency optimization keep inference costs sustainable. We optimize for cost-per-outcome because AI that can't afford to run at scale isn't production AI.

🔭

Production isn't the end

We design monitoring, drift detection, retraining triggers, and human escalation paths from day one. Agents and models degrade. The question is whether you catch it before the business feels it.

Tech Ecosystem

Tools & frameworks we work with

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

LLM Providers & APIs

OpenAI APIs Anthropic models Google Gemini AWS Bedrock Azure OpenAI

Agent Frameworks & Orchestration

LangGraph LangChain LlamaIndex MCP / Tool Protocols FastAPI

Knowledge & Retrieval

Pinecone Weaviate pgvector Hybrid Search Context Engineering

Observability & Eval

LangSmith Braintrust Arize Evidently AI Custom Eval Harnesses

ML & Python Ecosystem

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

Deployment & MLOps

SageMaker Vertex AI Databricks ML Docker / Kubernetes Semantic Caching

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 agent and LLM applications, we ground responses in verified sources, use structured outputs where possible, and run eval test suites that measure accuracy on your specific content and workflows. "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.

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Analytics & Business Intelligence

Augment your BI environment with predictive and generative AI features.

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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.

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