CS/14CASE STUDYInsurance

Agentic AI Delivery
for an Enterprise Platform

A large enterprise partnered with We As Web to implement a scalable Agentic AI Delivery Platform — enabling autonomous planning, decision-making, and execution across core enterprise tools and datasets. The result: 20-40% faster cycle times, ~30% reduction in manual effort, and ROI within a single quarter.

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At a Glance
Capabilities
Autonomous Plan-Decide-Act Workflows
Model-Agnostic Multi-Cloud Deployment
Governance-First Compliance (GDPR, AI Act, DORA)
Human-in-the-Loop Fallbacks
Technologies
Agentic AI PlatformModular AgentsRAGWorkflow DAGsMulti-Cloud (AWS/Azure/GCP)Model-Agnostic LLMs
Delivery Model
Phased: Discovery → Reference Architecture → Pilot → Hardening → Rollout & Enablement
[01]Business ContextTHE CLIENT · THE LANDSCAPE

A large enterprise ready to move beyond static chatbots

A large enterprise organization sought to modernize its automation capabilities by moving beyond static chatbots. The client partnered with We As Web to implement a scalable Agentic AI Delivery Platform capable of autonomous planning, decision-making, and execution across their existing tools and data. The project aimed to streamline key workflows, reduce manual effort, and ensure full compliance with strict data regulations like GDPR, the AI Act, and DORA, positioning the organization for the rapid expansion of governed AI-driven operations.

Key Context
Existing automation and assistant tools lacking true autonomy
Heavy dependency on specific vendor models creating lock-in risk
Weak auditability and governance over AI workflows
High manual effort and long cycle times on key workflows
[02]Client NeedsREQUIREMENTS · OBJECTIVES

What prompted the project

To successfully transition to an agentic AI model, the enterprise needed governance, vendor independence, measurable improvements, and scalable deployment.

N/01

Full Governance and Compliance

Robust audit capabilities to align with strict regulatory standards including GDPR, the EU AI Act, and DORA.

N/02

Vendor Independence

A multi-cloud, model-agnostic architecture designed to avoid dependency on any single AI vendor.

N/03

Measurable Improvements

Tangible enhancements in operational cycle times, manual effort reduction, and overall accuracy.

N/04

Scalable Deployment

A framework that supported rapid pilot launches followed by seamless rollouts across various teams.

[03]The ChallengesPRE-PROJECT · EXECUTION

Why an expert partner was required

The modernization initiative faced several critical obstacles — autonomy gaps, vendor lock-in, compliance blind spots, and inefficient workflows.

Pre-Project ChallengeStatic Legacy Systems
No True Autonomy
Existing automation and assistant tools lacked true autonomy and the necessary orchestration capabilities.
Manual Handoffs
Every workflow required a human to bridge steps that should have been automated.
Impact
Throughput was capped by human availability, not system capability.
Pre-Project ChallengeVendor Lock-In Risks
Heavy Vendor Dependency
A heavy dependency on specific vendor models threatened long-term flexibility.
Pricing & Roadmap Risk
Single-vendor reliance meant pricing changes and roadmap shifts could derail the entire AI strategy.
Impact
Strategic risk, not just procurement risk — the wrong choice today locked the org in for years.
Pre-Project ChallengeCompliance Blind Spots
Weak Auditability
The organization suffered from weak auditability, governance, and visibility into AI workflows and their resulting outcomes.
No Audit Trail
When AI made decisions, there was no clear record of why — or what data it used.
Impact
A regulatory environment where the AI Act, DORA, and GDPR all apply demanded more.
Pre-Project ChallengeInefficient Workflows
High Manual Effort
High manual effort and long cycle times plagued key operational tasks.
Data Prep & Document Review
Tasks like data preparation, document review, and query response were particularly painful.
Impact
Skilled teams were spending their time on mechanical work, not on the decisions that actually needed them.
[04]Solutions ProvidedARCHITECTURE · IMPLEMENTATION

What We As Web delivered

We As Web delivered a comprehensive Agentic AI Delivery Platform with six core components — built for governance from day one.

S/01

Autonomous Workflow Architecture

A "Plan → Decide → Act" architecture with autonomous agents routed by a lightweight planner.

Defined a "Plan → Decide → Act" architecture featuring autonomous agents.
Activities are routed by a lightweight planner — minimal overhead, maximum autonomy.
S/02

Modular Agent Design

Composable agents that can be easily plugged in, upgraded, or replaced — for maximum flexibility.

Created composable agents that can be easily plugged in, upgraded, or replaced.
Guarantees maximum flexibility as new capabilities and models emerge.
S/03

Model-Agnostic Deployment

Support for major LLMs across multiple cloud environments or on-premise setups.

Ensured support for major LLMs (OpenAI, Anthropic, Google, Azure, Bedrock, OSS).
Across multiple cloud environments (AWS, Azure, GCP) or on-premise setups — no vendor lock-in.
S/04

Robust Governance Layer

Policy-as-code, RBAC/ABAC, and full audit trails for GDPR, AI Act, DORA, SOC2/ISO compliance.

Integrated policy-as-code, RBAC/ABAC, and full audit trails.
Meets major compliance standards (GDPR, AI Act, DORA, SOC2/ISO) — every AI action is logged and reviewable.
S/05

Advanced Observability

Evaluation tools for prompt/tool/handoff tracing, structured logging, metrics, and human-in-the-loop fallbacks.

Deployed evaluation tools for prompt/tool/handoff tracing.
Structured logging, metrics, and human-in-the-loop fallbacks — so the system is observable and correctable.
S/06

Structured Delivery Model

A proven phased approach: Discovery → Reference Architecture → Pilot/PoC → Hardening → Rollout & Enablement.

Executed the project through a proven phased approach.
From Discovery to Rollout & Enablement — no big-bang deployments, just steady value at each step.
[05]Results AchievedBUSINESS IMPACT · BENEFITS

Faster, leaner, governed — and ROI in one quarter

The deployment of the Agentic AI platform delivered significant operational and financial benefits — measured against the metrics the client cared about most.

R/01
20-40% faster cycle times

Achieved 20-40% faster cycle times across recurring workflows — the headline metric the client cared about most.

R/02
~30% reduction in manual effort

Decreased manual effort on data-heavy tasks by approximately 30% — skilled teams back to skilled work.

R/03
10-25% higher accuracy on governed queries

Delivered 10-25% higher accuracy on governed data queries, complete with verifiable citations.

R/04
ROI typically in under one quarter

Achieved ROI typically in under one quarter. A Swiss enterprise finance department (~100 FTE) using the platform reported materially faster month-end closes and double-digit reductions in manual data preparation during their initial pilot.

[06]Technology & TeamSTACK · COMPOSITION

Agentic AI, model-agnostic and governance-first

A purpose-built stack — agentic workflows, RAG, modular agents, and a multi-cloud foundation — designed for the unique constraints of regulated AI.

Technology Stack
Agentic AI PlatformModular AgentsRAG (Retrieval-Augmented Generation)Workflow DAGsMulti-Cloud Infrastructure (AWS / Azure / GCP)Model-Agnostic LLMs (OpenAI, Anthropic, Google, Bedrock, OSS)Governance & Audit LayerHuman-in-the-Loop Fallbacks
Team Composition
AI ArchitectSolution EngineersIntegration SpecialistsGovernance & Compliance LeadClient Product Owner
[07]ConclusionSUMMARY · NEXT STEPS

When AI moves from chatbot to autonomous execution

Static chatbots and rigid automation cannot deliver the cycle-time improvements modern enterprises need. By delivering a scalable Agentic AI Delivery Platform with autonomous Plan-Decide-Act workflows, modular agents, model-agnostic deployment, and a governance-first design, we gave this client a platform that's both fast and compliant — without forcing a choice between the two.

The result is 20-40% faster cycle times, ~30% reduction in manual effort, 10-25% higher accuracy on governed queries, and ROI in under one quarter. For any large enterprise ready to move beyond static chatbots while staying inside strict regulatory guardrails, this is what governed, autonomous AI looks like in production.

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