AI Enablement · Enterprise Orchestration
Move Beyond AI Pilots
to Production-Scale Intelligence.
Architect secure, scalable AI ecosystems that unify your SaaS and legacy environments — without the cost or risk of system replacement. Powered by Agentic AI, MCP integration, and proven multi-agent frameworks.
MCP Integration
Multi-Agent Orchestration
Zero Legacy Replacement
Salesforce + Agentforce Ready
CORE ORCHESTRATION CAPABILITIES
MCP Integration
Protocol Layer
Multi-Agent Routing
Orchestration
Legacy AI Bridge
Integration
Trust & Security Layer
Governance
Parallel Fan-out
Performance
DAG Pipelines
Workflow
The Problem We Solve
Most AI Initiatives Stall Before They Scale
The bottleneck isn't intelligence — it's orchestration. Enterprises get trapped building one-off integrations that create unsustainable complexity and unpredictable AI behavior.
⚠️
The 'M × N' Integration Trap
Every new AI model or data source requires a hand-crafted adapter. As your AI portfolio grows, so does the complexity — creating context bloat, context overflow, and unpredictable model behavior that makes production deployment nearly impossible.
- Bespoke adapters multiply with every new model or data source
- Context windows overflow, degrading AI response quality
- No consistent governance across AI agents and tools
- Legacy systems locked out of AI value creation
- Pilot projects fail to scale to production environments
✅
The Mirketa Orchestration Layer
Instead of building one-off integrations, we create a structured, reusable orchestration layer that connects AI models, enterprise tools, and legacy systems through a single, governed architecture — making your AI initiatives scalable from day one.
- Single MCP integration layer replaces dozens of custom adapters
- Managed context windows prevent overflow and ensure accuracy
- Centralized governance with Approval Gates and Trust Calibration
- Legacy systems exposed as secure AI-accessible Resources and Tools
- Production-ready architecture from Week 1, not an afterthought
Core Capabilities
What We Build for Your Enterprise
Every capability is designed around a specific enterprise challenge — not generic AI theory. Each one is production-ready, reusable, and built to integrate with your existing systems.
Model Context Protocol (MCP) Integration
We utilize Anthropic's open standard to collapse complex integration surfaces into a single, shared protocol. One integration layer connects AI to every system — CRM, ERP, databases, and legacy applications.
- Eliminates the M × N integration complexity permanently
- Exposes enterprise data as secure, AI-readable Resources
- Enables executable Tools for cross-system workflow automation
- Transport-agnostic: works via stdio, HTTP, and SSE protocols
Strategic Multi-Agent Orchestration
We don't just deploy models — we coordinate multiple AI agents, memory stores, and human touchpoints into reliable, long-running workflows. Complex tasks are decomposed and distributed across specialist agents for maximum precision.
- Orchestrator–Subagent patterns for complex task decomposition
- Parallel Fan-out to reduce total workflow latency
- DAG pipelines for sequential, dependency-aware processing
- Human-in-the-loop escalation for high-stakes decisions
Legacy System AI Enablement
Your legacy systems contain decades of business-critical data. We unlock that value by layering sophisticated AI orchestration directly onto your current infrastructure — without migration, replacement, or disruption.
- CRM and legacy databases exposed as AI-accessible Resources
- Secure read/write access governed by enterprise policies
- Zero system replacement — build on what you already own
- Bi-directional data flow between AI agents and legacy systems
Trust & Security Layer
Enterprise AI must be trustworthy before it can be transformative. Our Trust Layer implements progressive oversight — starting with maximum human control and systematically reducing approval friction as the system demonstrates consistent, reliable performance.
- Approval Gates for financial transactions and record modifications
- Ambiguity Resolution: agents escalate with structured options, never guess
- Trust Calibration: oversight reduces as performance is validated
- Bounded retry logic prevents runaway agent loops
Technical Architecture
Architectural Patterns for Production AI
Three proven orchestration patterns that power reliable, scalable AI deployments — each selected based on your specific workflow complexity and performance requirements.
01
Orchestrator–Subagent
A central orchestrator agent decomposes complex tasks and delegates to specialist subagents — each optimized for a specific function. Results are aggregated and validated before returning to the user, ensuring precision at every step.
Best for: Complex, multi-step workflows
02
Parallel Fan-out
Multiple agents are spawned simultaneously across independent data streams, dramatically reducing total latency. Ideal for scenarios where tasks can be executed in parallel — such as multi-source data retrieval or concurrent analysis across business units.
Best for: High-volume, time-sensitive tasks
03
Directed Acyclic Graphs (DAG)
Sequential dependencies are modeled as a DAG, ensuring each processing step enriches the next in a controlled, auditable pipeline. Every node's output becomes the next node's context — building intelligence progressively through the workflow.
Best for: Sequential, dependency-driven pipelines
MCP Technical Foundation
Server Architecture
We expose your CRM and legacy databases as Resources (read-only context) and Tools (executable functions) through a secure, governed MCP interface that AI agents can query reliably.
Transport Flexibility
Whether via stdio for local developer tooling or HTTP + SSE for multi-tenant cloud environments, our architecture remains transport-agnostic — adapting to your infrastructure, not the other way around.
Reliability Engineering
Bounded retry logic handles tool failures gracefully, while selective summarization prevents context overflow. Every component is built for production uptime, not just proof-of-concept demos.
Industry Impact
AI Orchestration Across Every Vertical
Our orchestration frameworks are not generic — they are designed around the specific data architectures, compliance requirements, and workflow patterns of each industry we serve.
Healthcare AI Orchestration
Healthcare organizations manage a fragmented mix of EHR systems, payer platforms, scheduling tools, and clinical databases. Our orchestration layer bridges these silos — enabling AI agents to access patient data, trigger care workflows, and coordinate across clinical teams in real time, all within HIPAA-compliant governance frameworks.
- 50%+ Faster chart review with AI Patient Chart Summaries
- 40% Reduction in call center volume via Smart Appointment Agent
- 30-40% Fewer no-shows with predictive intervention workflows
- ↑ 25% First-pass claim acceptance via orchestrated pre-submission review
⚡ Healthcare Orchestration Use Cases
Clinical Notes AI
EHR → AI → Structured Notes → Provider Review
Smart Appointment Agent
NLP → Scheduling System → Confirmation → Reminder
Claim Rejection Prediction
Claims Data → AI Analysis → Flag → Correction → Submit
Lab Result Intelligence
Raw Data → Pattern Detection → Alert → Care Team
Financial Services AI Orchestration
Financial institutions operate across a complex web of core banking systems, compliance platforms, and customer-facing applications. Our orchestration layer enables AI agents to perform real-time fraud detection, automate compliance checks, and deliver personalized financial guidance — all within strict regulatory governance frameworks.
- 60% Reduction in manual compliance review time
- 3x Faster fraud alert triage with multi-agent analysis
- 45% Improvement in customer query resolution speed
- ↓ 30% Operational cost reduction via workflow automation
⚡ Financial Services Use Cases
Fraud Detection Agent
Transaction → Multi-Agent Analysis → Risk Score → Alert
Compliance Automation
Policy DB → AI Review → Flag → Human Approval → Report
Customer Intelligence Agent
CRM → Behavioral AI → Personalized Offer → CRM Update
Risk Reporting Orchestration
Multi-Source Data → DAG Pipeline → Consolidated Report
Nonprofit & Education AI Orchestration
Nonprofits and educational institutions often operate with limited IT resources but complex stakeholder relationships. Our orchestration layer connects donor management systems, student information platforms, and fundraising tools — enabling AI agents to drive engagement, predict churn, and optimize outreach at scale.
- 25% Higher donor retention through predictive engagement
- 40% Improvement in fundraising campaign ROI
- ↓ 50% Reduction in manual donor outreach effort
- 3x Faster grant application processing via AI workflows
⚡ Nonprofit & Education Use Cases
Donor Management AI
CRM → Lapse Prediction → Personalized Outreach → Track
Student Engagement Agent
SIS Data → Risk Detection → Advisor Alert → Intervention
Grant Management Orchestration
Requirements → AI Draft → Review → Submission → Track
Campaign Optimization AI
Historical Data → AI Segmentation → Targeted Campaign → ROI
Technology & SaaS AI Orchestration
Technology companies face unique challenges: rapid product iteration, complex DevOps pipelines, and high-velocity sales cycles. Our orchestration layer accelerates every stage — from automated code review and deployment pipelines to AI-powered sales intelligence and customer success workflows.
- 35% Shorter DevOps cycle times via agentic automation
- 50% Reduction in mean time to remediate (MTTR) security issues
- 20-30% Improvement in sales pipeline conversion rates
- ↑ 40% Agent productivity with AI-powered case resolution
⚡ Technology & SaaS Use Cases
Vulnerability Remediation Agent
Scan → AI Triage → Auto-Remediation → Follow-up → Close
Sales Intelligence Orchestration
CRM → AI Scoring → Prioritized Pipeline → Guided Actions
DevOps Agentic Pipeline
Code Commit → AI Review → Test → Deploy → Monitor
Case Resolution AI
Ticket → Knowledge Base → AI Suggestion → Agent → Resolve
Our Delivery Approach
From Architecture Assessment to Go-Live in Weeks
Our proven delivery framework ensures every AI orchestration project is deployed with precision, speed, and measurable business impact — not months of consulting engagements.
1
Week 1–2
Architecture Assessment
Evaluate your existing systems, data quality, integration landscape, and identify the highest-ROI orchestration opportunities for your specific business context.
2
Week 2–3
Framework Design
Design the MCP integration layer, agent routing logic, data models, security protocols, and Trust Layer governance framework tailored to your enterprise policies.
3
Week 3–8
Build & Connect
Develop the orchestration hub, connect APIs and legacy databases, configure multi-agent workflows, and run iterative demos with your team throughout the build cycle.
4
Week 8–10+
Deploy & Scale
Launch to production, train your teams, monitor agent performance in real time, and continuously optimize context windows and routing logic based on live outcomes.
Why Mirketa
Mirketa vs. Building In-House vs. Generic Platforms
Not all AI orchestration approaches are equal. Here is how our approach compares to the alternatives most enterprises consider.
| Capability | Mirketa Orchestration | Build In-House | Generic AI Platform |
|---|---|---|---|
| Time to production deployment | ✓ 8–10 weeks | 6–18 months | 3–6 months (with customization) |
| Legacy system integration | ✓ Zero replacement required | ~ Requires migration effort | ✗ Often requires system upgrade |
| MCP / open standard support | ✓ Native MCP integration | ~ Requires custom build | ~ Vendor-specific protocols |
| Enterprise Trust & Security Layer | ✓ Built-in, configurable | ~ Must be designed from scratch | ~ Basic governance only |
| Client IP ownership | ✓ 100% client-owned | ✓ Client-owned | ✗ Platform-dependent |
| Industry-specific use cases | ✓ Pre-built accelerators | ✗ Built from zero | ~ Generic templates only |
| Salesforce / Agentforce integration | ✓ Deep native integration | ~ Requires Salesforce expertise | ~ API-level only |
Client Outcomes
What Our Clients Say About AI Orchestration
Zero Legacy Replacements
Systems integrated without migration
"We were convinced we'd need to replace our legacy EHR system to participate in AI. Mirketa proved us wrong. Their MCP integration layer connected our existing systems to AI agents in weeks — not the 18-month migration project we feared. We now have production AI workflows running on infrastructure we've had for years."
8 Weeks to Production
From assessment to live deployment
"What impressed us most was the Trust Layer. We were nervous about AI agents making autonomous decisions in our financial workflows. Mirketa's Approval Gates and Trust Calibration framework gave our compliance team the confidence to approve production deployment. The system started with maximum oversight and earned its autonomy over time."
FAQ
You Have Questions. We Have Answers.
The most common questions we hear from enterprise leaders before starting their AI orchestration journey.
AI orchestration is the coordinated management of multiple AI agents, data sources, and enterprise systems to achieve complex business goals reliably and at scale. Without orchestration, AI initiatives remain isolated pilots — each model operating independently, unable to access the full context of your business data or trigger actions across systems. Orchestration is what transforms individual AI capabilities into an integrated, production-grade intelligence layer that actually runs your business processes.
Model Context Protocol (MCP) is an open standard developed by Anthropic that provides a universal way for AI models to interact with external data sources and tools. Instead of building custom integrations for every system your AI needs to access, MCP creates a single, standardized interface. Mirketa uses MCP to expose your enterprise systems — CRM, ERP, legacy databases, and APIs — as AI-accessible Resources (for reading context) and Tools (for executing actions). This eliminates the M × N integration complexity and makes your entire data estate available to AI agents through one governed layer.
Absolutely not — and this is one of our core differentiators. We have never required a client to replace their legacy systems to implement AI orchestration. Our MCP integration layer is designed to connect to existing infrastructure, exposing legacy databases and applications as secure, AI-readable resources. Whether your systems are decades-old mainframes or modern SaaS platforms, our architecture adapts to what you have — not the other way around. The value of your existing data and workflows is preserved and amplified, not discarded.
Our Trust & Security Layer is built into every orchestration deployment. It operates on three principles: Approval Gates require explicit human consent before any consequential action — such as financial transactions, record modifications, or customer communications. Ambiguity Resolution ensures that when an agent reaches a confidence threshold below a defined level, it escalates to your team with structured options rather than guessing. Trust Calibration starts every deployment with maximum human oversight and progressively reduces approval friction only as the system demonstrates consistent, validated performance. You remain in control at every stage.
Our standard delivery timeline runs 8–10 weeks from initial Architecture Assessment to production launch. Week 1–2 covers the assessment and opportunity identification. Week 2–3 is dedicated to framework design and security architecture. Weeks 3–8 involve the build, integration, and iterative testing cycle. Weeks 8–10+ cover production deployment, team training, and performance optimization. Complex enterprise environments with many legacy systems may require additional time, but our pre-built accelerators and MCP integration framework consistently reduce delivery timelines by 40% compared to custom-built approaches.
Yes — Salesforce and Agentforce are among our deepest integration competencies. We are a Salesforce partner with extensive experience deploying Einstein AI, Agentforce, and Data Cloud. Our orchestration frameworks are designed to work natively within the Salesforce ecosystem, connecting Agentforce agents to your external systems, legacy databases, and non-Salesforce platforms through our MCP integration layer. This means your Salesforce investment becomes the orchestration hub for your entire enterprise AI strategy, not just a CRM.
You do — 100%. While we leverage our pre-built accelerators and frameworks to speed up delivery, the underlying intellectual property developed during your engagement belongs entirely to your organization. This is a non-negotiable principle for us. We believe that the AI systems running your business should be owned by your business, not licensed from a consulting firm. Our accelerators reduce your time-to-value; your custom orchestration layer is yours to own, operate, and evolve independently.
Featured Insights
AI Orchestration Intelligence
Practical guides, technical deep-dives, and strategic frameworks from Mirketa's AI architecture team.
🔗
Technical Guide
Model Context Protocol: The Enterprise Integration Standard for Agentic AI
How MCP eliminates the M × N integration problem and creates a universal interface between AI models and enterprise systems.
🤖
Architecture Guide
Orchestrator–Subagent vs. Parallel Fan-out: Choosing the Right Pattern
A practical framework for selecting the right multi-agent architecture pattern based on your workflow complexity and performance requirements.
🛡️
Enterprise Strategy
Building Trust in Autonomous AI: The Progressive Oversight Framework
How enterprise leaders can deploy AI agents with confidence using Approval Gates, Ambiguity Resolution, and Trust Calibration.
Ready to Scale?
Your AI Pilot Deserves to Become
Production-Scale Intelligence.
You don't need to replace your core systems to participate in the AI revolution. You need a robust orchestration and data access layer that unifies what you already have.