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    Allerin

    Agentic AI That Actually Works in Production

    Build intelligent agents that reason, plan, and execute complex workflows autonomously—with the guardrails and oversight your business requires.

    Custom agents built on your dataHuman-in-the-loop controlsProduction-ready, not just demos

    Beyond Chatbots: AI That Takes Action

    Traditional AI tools wait for instructions. Agentic AI systems think ahead. Where conventional AI generates responses to prompts, agentic AI breaks down complex goals into subtasks, reasons through options, interacts with your systems, and executes multi-step workflows—often without human intervention at every step.

    The difference matters. A chatbot answers questions about your return policy. An agentic system processes the return, updates inventory, triggers the refund, notifies the customer, and flags patterns that might indicate fraud—all from a single customer message.

    Traditional AI vs. Agentic AI

    Traditional AI
    • Responds to prompts
    • Single-task focused
    • Requires human orchestration
    • Generates content or answers
    Agentic AI
    • Plans and executes autonomously
    • Handles multi-step workflows
    • Self-orchestrates toward goals
    • Takes actions in real systems

    This isn't science fiction. Organizations across industries are deploying AI agents that handle insurance claims end-to-end, orchestrate supply chain responses to disruptions, and manage customer inquiries from first contact to resolution.

    The Gap Between AI Promise and AI Reality

    You've seen the demos. AI agents booking flights, writing code, managing complex workflows. The possibilities seem limitless. Then reality hits.

    The Pilot That Never Scales

    Your team built a proof-of-concept that impressed leadership. Six months later, it's still a proof-of-concept. The gap between demo and production proved wider than anyone anticipated.

    More Work, Not Less

    You deployed an AI tool that was supposed to reduce workload. Instead, your team spends hours reviewing outputs, fixing errors, and apologizing to customers for automated mistakes.

    The Vendor Lock-in Trap

    You built on a platform that seemed perfect—until pricing changed, features disappeared, or the roadmap diverged from your needs. Now you're stuck with mounting costs.

    The Integration Nightmare

    Your AI agent works beautifully in isolation. Connecting it to your CRM, ERP, and legacy systems? That's a different story entirely.

    The Compliance Question Mark

    Leadership wants to deploy AI, but legal can't sign off. Who's responsible when an autonomous agent makes a decision that affects customers or revenue?

    These aren't edge cases. They're the common experience of organizations attempting agentic AI without the right foundation.

    Agentic AI Built for Your Reality

    We develop AI agents that operate in the real world—with its messy data, legacy systems, compliance requirements, and the need for human judgment at critical moments.

    Autonomous Workflow Agents

    AI systems that execute multi-step business processes end-to-end. From customer inquiry to resolution, from order placement to fulfillment. These agents don't just assist—they complete work.

    Multi-Agent Orchestration

    Complex problems often require multiple specialized agents working in concert. We architect systems where agents collaborate—planning, executing, verifying, and coordinating.

    Intelligent Decision Support

    Not every decision should be fully automated. We build agents that gather information, analyze options, and present recommendations—while keeping humans in the loop for high-stakes choices.

    AI-Augmented Operations

    Agents that work alongside your team, handling routine tasks while escalating exceptions. The goal isn't to replace people but to amplify what they can accomplish.

    What's Under the Hood

    Reasoning Engines

    Break complex goals into actionable steps

    Memory Systems

    Maintain context across interactions

    Tool Integration

    Connect to your existing systems

    Guardrails

    Prevent harmful or unauthorized actions

    Observability

    Understand what agents do and why

    Feedback Loops

    Enable continuous improvement

    From Concept to Production: A Structured Path

    Agentic AI projects fail when they skip steps. We've developed a methodology that moves efficiently while ensuring each phase builds a solid foundation.

    Week 1-2

    Discovery & Opportunity Mapping

    We understand your operations, not just your AI aspirations. Through stakeholder interviews, process analysis, and data assessment, we identify where agentic AI creates genuine value.

    Deliverable: Opportunity assessment, technical feasibility analysis, recommended starting point

    01
    02
    Week 3-4

    Architecture & Design

    With the opportunity defined, we design agent architecture—not just what the system will do, but how it will do it reliably, safely, and maintainably.

    Deliverable: Agent architecture specification, integration design, guardrail framework

    Week 5-8

    Prototype Development

    We build a working prototype demonstrating core capabilities. This isn't a PowerPoint—it's a functional system operating against real data and workflows.

    Deliverable: Functional agent prototype, initial benchmarks, stakeholder demo

    03
    04
    Week 9-12

    Iteration & Refinement

    Based on learnings and feedback, we refine the system—expanding capabilities, improving accuracy, and hardening for production conditions.

    Deliverable: Enhanced capabilities, edge case handling, performance optimization

    Week 13-16

    Production Deployment

    Deployment with appropriate controls—typically starting with limited scope and expanding as confidence builds. Full observability ensures issues are caught early.

    Deliverable: Production deployment, monitoring setup, operations runbook

    05
    06
    Ongoing

    Ongoing Evolution

    Agentic AI systems should improve over time. We provide options for managed services, optimization sprints, and capability expansion.

    Deliverable: Continuous improvement, model updates, team enablement

    Built on Modern Foundations

    We're framework-agnostic but opinion-informed. Our technology choices are driven by your requirements, not vendor relationships.

    LLM Foundation

    OpenAI, Anthropic, Google, Mistral, open-source—selected based on your needs for capability, cost, data residency, and deployment.

    Agent Frameworks

    LangChain, LlamaIndex, AutoGen, CrewAI, custom—chosen based on complexity and requirements.

    Integration

    REST, GraphQL, queues, direct DB—connecting to your systems using appropriate patterns.

    Deployment

    Cloud-native, on-prem, hybrid, air-gapped—deployed where your data and compliance requirements dictate.

    Observability

    LangSmith, LangFuse, custom monitoring—full visibility into agent behavior, performance, and outcomes.

    What Makes Allerin Different

    Production Focus, Not Demo Obsession

    Anyone can build an impressive demo. We focus on systems that work reliably in production—handling edge cases, recovering from failures, operating at scale.

    Guardrails as Architecture

    AI safety isn't a feature we add at the end. Guardrails, human oversight, and constraints are designed into the architecture from the beginning.

    Integration Expertise

    AI agents are only valuable when connected to your systems. We have deep experience integrating with CRMs, ERPs, data warehouses, legacy systems, and custom applications.

    Cross-Industry Pattern Recognition

    We've built agents across industries. That breadth means we recognize patterns—what works, what fails, what scales—and apply those lessons to your situation.

    End-to-End Capability

    We don't hand off between strategy, design, and implementation teams. The engineers who architect your system are the engineers who build and deploy it.

    Honest Assessment

    Not every problem needs agentic AI. We'll tell you when a simpler solution would serve you better—even if that means a smaller engagement for us.

    Common Questions About Agentic AI Development

    Engagement Options

    We structure engagements to match your situation—whether you need a quick proof-of-concept, a full production system, or ongoing partnership.

    Starting Point

    Discovery & Assessment

    $15K – $25K

    Assess your operations, identify opportunities, and provide a detailed roadmap.

    Stakeholder interviews
    Process analysis
    Feasibility report

    Investment credited toward implementation if you proceed

    Validate

    Pilot Development

    $50K – $100K

    Build a focused proof-of-concept for a specific use case. Demonstrates viability and creates foundation for expansion.

    Working prototype
    Initial integrations
    Stakeholder demo
    Most Popular
    Build

    Production Implementation

    $100K – $300K+

    Full production deployment with integrations, guardrails, monitoring, and knowledge transfer.

    Production-ready system
    Full integrations
    Guardrails & monitoring
    Team training
    Partnership

    Ongoing Services

    Custom

    Managed services, continuous improvement, capability expansion. Monthly retainer or project-based.

    Continuous optimization
    Model updates
    Capability expansion

    Ready to Build AI That Actually Works?

    Start with a conversation. We'll discuss your situation, explore possibilities, and determine if there's a fit—with no obligation and no sales pressure.

    Talk to our team directly
    Confidential discussion—NDA available
    Technical team, not sales team
    Honest assessment of fit

    At a Glance

    Timeline: 2–4 weeks (design+pilot), 6–8 weeks (pilot→prod)
    Team Size: architect, MLE, FE, BE, PM; security reviewer on demand
    Typical ROI: 8 weeks to positive ROI
    Best For: manufacturing, healthcare, finance

    Key Takeaways:

    • Agentic AI Systems automate back-office workflows with 94% accuracy and human oversight
    • Typical deployment: 2-4 weeks from design to production pilot
    • ROI achieved in 8 weeks on average for Fortune 500 clients
    • Supports cloud, on-prem, or hybrid deployments with full compliance (HIPAA, SOC2, FedRAMP)
    🏆
    Case Study Highlight

    Fortune 500 Insurance: 73% Claims Automation

    Automated claim review, fraud detection, and policy verification with 94% accuracy. Human reviewers now handle only edge cases, reducing processing time from 8 hours/day to 45 minutes/day.

    73%
    Claims Automated
    94%
    Agent Accuracy
    8 weeks
    Time to ROI

    When to Choose What

    Agentic AI Systems builds the agents and workflows. For multi-agent orchestration at scale, consider our control plane solution.

    Agentic AI Systems

    Best for building 1-3 focused agents

    • Design & deploy specific agent workflows
    • Human-in-the-loop (HITL) review flows
    • Policy alignment & guardrails
    • Explainable agent decisions

    AI Orchestration

    Best for managing 5+ agents at scale

    • Multi-model routing & fallbacks
    • Cost governance & budget caps
    • Central audit trails & run logs
    • Enterprise-wide governance

    Multi-Agent Automation Outcomes

    See the math →
    • Eliminate manual back-office steps
    • Policy-aligned actions with human override
    • Traceable, explainable decisions with run logs
    • Reviewer minutes per case ↓ 30–50%
    • Cost per transaction capped and reported

    What You Get: Agentic AI Deliverables

    Our standards →
    Orchestration graph in code (agents, tools, policies) with retries/timeouts
    Evals suite: jailbreak, toxicity, groundedness, accuracy gates
    Guardrails and safety gates enforced in CI and runtime
    Reviewer console (approve/annotate/retry) with audit trail
    Run log and trace viewer (inputs, prompts/versions, tool calls, outputs)
    Budget caps and alerts; cost per transaction export

    Timeline

    2–4 weeks (design+pilot), 6–8 weeks (pilot→prod)

    Team

    architect, MLE, FE, BE, PM; security reviewer on demand

    Industry Benchmarks & Statistics

    Based on 50+ enterprise deployments across Fortune 500 companies in manufacturing, healthcare, and financial services.

    89%
    Average manual task reduction across Fortune 500 deployments
    Source: Allerin 2024 deployment data
    94%
    Median agent accuracy with guardrails and HITL review
    Measured across production environments
    8 weeks
    Typical time to positive ROI in production environments
    From pilot to measurable value
    $2.4M
    Average annual savings per deployment
    Based on labor cost reduction and efficiency gains
    20 hours/week
    Average time saved per knowledge worker
    Redirected to high-value strategic work

    Inputs We Need

    • Process maps or workflow documentation
    • Sample data and edge cases
    • Policy docs, consent and approval thresholds
    • SME availability for evaluation
    • Target KPIs (time saved, accuracy), model/tool constraints

    Tech & Deployment

    Control plane: LangGraph/Temporal/Argo; retries/backoff/idempotency. Models: OpenAI/Claude/local; routing and fallback policies. HITL: reviewer UI; reason codes; audit and retention windows. Identity: SSO (OIDC/SAML); roles/attributes; least-privilege. On-prem/GovCloud or VPC; secrets in KMS; policy repo in Git.

    📊Before/after KPI chart on target workflow
    📊Evals summary with thresholds and deltas
    📊Run-log sample (redacted) and trace screenshot
    📊Cost per transaction and budget adherence
    📊Policy mapping table (policy → runtime check)
    📊Critical CVEs at release → 0

    Frequently Asked Questions

    Need More Capabilities?

    Once you've built your first agents, consider these complementary services to scale your AI operations.

    Ready to Get Started?

    Book a free 30-minute scoping call with a solution architect.

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