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Governed AI for Regulated Enterprises

Governed AI Solutions with Human Oversight Built In.

From governed generative AI to fully autonomous agents. Built for regulated enterprises, deployed inside your perimeter, with a human checkpoint wherever judgment matters.

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300+ Consultants8 CountriesISO 27001CMMI Level 5Inc. 5000
Closed-LLM AI deployed inside an enterprise perimeter
Generate → Govern → Act
Agent loop, human-checked
Agent online412 records reviewed · 18 escalated to human
Our AI Services

Governed AI Services Built Around Your Business Outcomes

Every AI project starts with one question: where will this create measurable value? We organize our AI services around that answer. Agentic AI leads, because enterprises need AI that acts, not just AI that talks. Applied AI fits the practice into your industries and departments. Infrastructure runs underneath. Orchestration ties it all together. Human oversight governs all of it.

Agentic AI

Agentic AI

Autonomous agents that plan, execute, and adapt across multi-step workflows. Not chatbots. Not scripted RPA. Intelligent agents with defined boundaries, confidence thresholds, and audit trails. The evolution of the workflow orchestration systems Scadea has built for twenty years.

Multi-agent architectures, permission-aware RAG, knowledge graphs, Model Context Protocol integration. Built with closed-LLM deployments of OpenAI, Anthropic, Google Gemini, and open-source. MLOps pipelines for continuous monitoring and retraining.
Explore Agentic AI
AI by Industry & Department

AI by Industry & Department

Three lenses on applied AI: pre-built industry agents for workflows your sector already runs, sector-tuned models with the regulatory awareness your industry needs, and department-ready AI embedded in the tools your teams already use (Salesforce, ServiceNow, Oracle, Microsoft 365). One page, three entry points.

Industry agent library (Retail Banking, Commercial Banking, Insurance, with more verticals in development). Domain-trained models for BFSI, Healthcare, Hospitality, Manufacturing, and Transportation. Pre-built connectors for the platforms your teams already run. Closed-LLM deployments inside your cloud, configurable confidence thresholds, human escalation paths, full audit trails.
Explore AI by Industry & Department
AI Infrastructure

AI Infrastructure

The foundation everything runs on. Cloud platforms configured for AI workloads. Vector databases for retrieval. Model deployment pipelines with version control and rollback. Monitoring that catches drift before your users do.

AWS SageMaker, Azure ML, Google Vertex AI, Databricks Unity Catalog, Snowflake Cortex. Kubernetes-based deployment, CI/CD for model updates, automated data quality checks.
Explore AI Infrastructure
Enterprise AI Orchestration

Enterprise AI Orchestration

AI that operates across your existing stack, not in isolation. Multi-agent coordination, workflow routing, and system-to-system integration that connects CRM, ERP, and operational platforms. The connective tissue that turns isolated AI capabilities into a working enterprise practice.

Event-driven architectures, message queues, API orchestration layers. Integration with Salesforce, ServiceNow, SAP, Oracle, and Microsoft 365. Confidence-based routing with human escalation built into every cross-system handoff.
Explore Enterprise AI Orchestration
Architecture vs Component

Agentic AI Is the Architecture. Generative AI Is a Component.

Generative AI made it into enterprises as chatbots, content drafters, and code assistants. Useful. Still passive. The human had to prompt every step.

Enterprises want AI that takes action. Book the meeting. Update the record. Verify the data across systems. Escalate the exception. That's agentic AI. Generative AI sits inside it as the component that handles writing, reading, and language reasoning.

Scadea's AI practice leads with agentic because that's where the value is. Every agent we build uses generative models inside. But the architecture, the governance, and the integration work are what make the agent useful, and what makes it safe to deploy inside a regulated business.

Agentic AI

Autonomous agents that plan, execute, and adapt across multi-step workflows without constant human direction.

AI agents that break complex tasks into steps, execute them, and adapt when conditions change. Built with human escalation at defined checkpoints.

Agents that coordinate across departments, systems, and data sources. Pull from shared knowledge bases and hand off to humans when decisions carry weight.

Connect CRM, ERP, and operational systems through intelligent routing. Confidence-based automation with manual override.

Retrieval-augmented generation with permission-aware search. Your AI answers from your data, not the open web.

Configurable confidence thresholds determine what auto-processes and what routes to a reviewer. Audit trails on every decision.

Generative AI

The layer inside every agent that handles writing, reading, and language reasoning. Also deployed standalone for chatbots, content, and code work.

Omni-channel deployment, bot optimization, voice-integrated bots. Trained on your data, governed by your rules.

Document summarization, classification, extraction, and sentiment analysis. Tuned for department-specific language.

AI-assisted development workflows. Code generation, review, and documentation with human approval gates.

Drafting, editing, and compliance review for marketing, legal, and operational content.

Image classification, object detection, and visual inspection for manufacturing, healthcare imaging, and document processing.

See how generative AI fits inside every agent
AI Governance

Governance Is Not a Layer You Add Later. It's the Architecture.

Every agent we build runs inside a governance perimeter your compliance team helped design. Permissions, audit trails, and escalation rules are wired in from day one, not bolted on the week before launch.

Permission-Aware Models

Agents respect the same access controls your users do. If a person can't see the record, the model deployed on their behalf can't either. RBAC enforced at the retrieval layer, not in a prompt.

Full Audit Trail

Every prompt, every retrieval, every action the agent takes is logged with timestamps, model version, confidence scores, and source documents. Regulators can replay any decision the agent made.

Confidence Thresholds

You set the bar. Below a configurable confidence score, the agent stops and routes to a human reviewer instead of guessing. Above it, the agent acts and logs the decision for sampling.

Closed-LLM Deployment

Models deployed inside your cloud perimeter or in private compute. Your prompts and data don't leave your tenant. No vendor training on your traffic. No surprises in a model update.

Drift Monitoring

Continuous evaluation against ground-truth datasets, with alerts when performance degrades. Drift caught before users see it, with automated rollback to the last-known-good model version.

Human Review Gates

Compliance-grade decisions, customer-impacting actions, and financial transactions all pause for a human review by default. You decide what counts as material; we wire the gate.

Human-in-the-Loop

Decision-Grade AI. A Person on Call When It Matters.

Autonomous doesn't mean unsupervised. Every Scadea agent has a configurable checkpoint where the system pauses, surfaces its reasoning, and waits for a human signoff before taking action that carries weight.

Which decisions need a checkpoint is a business question, not a model question. We work with your compliance team to define the line, then build the agent around it.

  • Configurable confidence thresholds set the auto-vs-review line per workflow.
  • Reviewers see the agent's reasoning, source documents, and alternatives side by side.
  • Every human override becomes training signal for the next model evaluation cycle.
  • Audit logs separate auto-processed decisions from human-approved ones for sampling.
See how Human-in-the-Loop works at Scadea
Reviewers working with an AI system
Reviewer approval required

Ready to see what governed AI looks like in your business?

A 30-minute working session with a senior AI consultant. We'll map your current workflows, identify where agents create measurable value, and outline the governance fit for your regulators. No deck.

Book a Working Session
Data Bridge

Your Agents Are Only as Good as the Data Underneath.

Every AI engagement starts with a data audit. Where does the model need to read from? Where does it need to write back to? What's clean, what's stale, what's locked behind a system no one wants to touch? That's the work that makes the agent useful.

Scadea's data engineering practice connects directly into the AI practice. The same team that builds your agent maps the data pipelines, the lineage, the permissions, and the freshness contracts. No handoffs. No "the data team will get back to you."

68%
of stalled AI pilots cite data quality as the primary blocker.
3x
faster from pilot to production when data work runs in parallel with agent build.
Explore Data & Analytics
Data infrastructure for AI

Data not AI-ready? Start with a Data Readiness Assessment.

A one-page diagnostic of your data estate, the lift required, and the path to your first governed AI deployment.

Run the Diagnostic
Related Case Studies

AI in Production. Regulated Industries.

AI-Powered Compliance Management in Financial Services

Automated regulatory review for a financial services firm — replacing manual processing of thousands of policy pages with AI-driven compliance management.

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Enhancing Patient Care with AI-Powered Predictive Analytics

Helped a leading US hospital network predict patient volumes and optimise staffing levels — reducing overcrowding, wait times, and operational costs.

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AI-Powered Solution Drives Sales Growth for an E-commerce Retailer

Built an AI engine that analysed customer behaviour and purchase patterns for a struggling online retailer, reversing declining sales with personalised recommendations.

Read more
Engagement Models

Your Path to Data & AI Transformation. Choose How We Partner.

Scadea data and AI team
Book Time With Our Data & AI Team
Advisory Services
We help you figure out the right path forward and what steps to take next.

We assess your AI readiness and create practical roadmaps from pilot to production. Our advisors understand the real challenges regulated enterprises face: governance, data quality, integration, and change management. We build transformation plans that actually get followed.

People Solutions
We bring you professionals with the specific skills and experience to get your projects moving.

Our global network of AI engineers, ML specialists, agent architects, and integration experts plug directly into your teams. From model selection to agent deployment, our consultants deliver. 300+ people across 8 countries, all Scadea employees.

Implementation Services
We take ownership of your technical projects, whether it's saving one that's off track or running your sprints and teams.

Our dedicated pods and sprint teams own your AI delivery start to finish. Whether rescuing a stalled pilot or leading a new agentic build, we bring enterprise experience and stay until the system ships.

Built on Track Record

AI Work That Has Shipped Inside Regulated Enterprises.

300+
Consultants in the Scadea AI & data practice, all employees.
8
Countries with delivery teams operating in your time zone.
20+
Years building workflow orchestration for regulated industries.
ISO 27001 ISO 9001 CMMI Level 5 ★ Inc. 5000 SOC 2 Aligned
FAQ's

Need answers? Find them here.

An agentic deployment is a configured set of LLM-backed agents, retrieval pipelines, system connectors, escalation rules, and observability, running inside your cloud tenant. Not a generic chatbot, not RPA. Each agent owns a specific workflow and pauses for a human at the checkpoints your compliance team defined.

Closed-LLM deployments. Models run inside your AWS, Azure, or GCP tenant, or in private compute. Vendor agreements explicitly prohibit training on your traffic. Prompts and outputs never leave your perimeter.

For a single-workflow agent with clean data and an existing system of record, eight to twelve weeks. For multi-system orchestration with new data pipelines and a regulator review, twenty to twenty-six. The data work is usually the longer leg.

A reviewer queue inside your existing case management or ticketing system. The agent surfaces its proposed action, the sources it pulled from, confidence score, and any alternatives it considered. The reviewer approves, rejects with reason, or edits. Every override becomes training signal.

Yes. Pre-built connectors for Salesforce, ServiceNow, SAP, Oracle, and Microsoft 365. Custom integration for core banking, claims, EHR, ERP, and operational systems through APIs or event streams.

Generative AI entered enterprises mostly as chatbots and content tools. Useful, but passive. Enterprises now want AI that takes action, not just AI that talks. Agentic AI is how that gets delivered safely. Generative AI is still part of every solution, but it's a component inside the agent, not the endpoint.

Yes. Our AI by Industry & Department page lists pre-built agents for retail banking, commercial banking, and insurance, with more verticals being added. Every agent deploys inside your governance perimeter.

Technology Partners

Built on platforms enterprises already trust.