Service
Agentic AI Solutions
We move enterprises beyond AI experimentation, building agentic AI that operates inside real workflows with the data foundations, guardrails, and integration to be dependable at scale.
What we build.
Six disciplines, from first architecture to production support.
Agentic AI implementation
Autonomous agents that reason, plan, and execute multi-step work with human oversight.
- Multi-step automation
- Tool-using agents
- Human-in-the-loop workflows
- Monitoring and guardrails
Intelligent document processing
AI extraction and classification for contracts, invoices, and legal documents.
- Contract analysis
- Invoice and AP automation
- Legal document review
- Classification and routing
LLM-powered business agents
Custom assistants on GPT, Claude, or open models, grounded in your domain.
- Domain knowledge bases
- Retrieval-augmented generation
- Enterprise search and Q&A
- Customer-facing assistants
Workflow orchestration
Automation that makes decisions, handles exceptions, and routes work by context.
- Decision automation
- Exception handling
- Process mining
- Cross-system orchestration
Multi-agent systems
Specialized agents that collaborate on complex work, each owning part of the flow.
- Coordination patterns
- Role-based agents
- Shared memory and context
- Scalable architectures
AI strategy and assessment
We find the high-impact opportunities and build a practical roadmap.
- Readiness assessment
- Use case identification
- ROI and prioritization
- Proof of concept
Why Aedista
Why Aedista for AI
Our stack
Modern tools we build with
.NET Core
Enterprise web and service backends
Java
Large-scale enterprise systems
Node.js
APIs and real-time services
PHP
High-volume web platforms
React
Modern web front ends
Angular
Enterprise front ends
Next.js
Server-rendered React apps
Swift
Native iOS apps
Kotlin
Native Android apps
Azure
Cloud infrastructure and DevOps
AWS
Cloud infrastructure at scale
Docker
Containerized deployment
Engagement models
Three ways to work with us
Fixed cost
A defined scope, priced upfront, and paid by milestone. Best when requirements are clear and stable.
Time and materials
Built for evolving scope. You pay for delivered work, and priorities can shift as you learn.
Dedicated team
Our engineers work as an extension of your team, on your priorities, at your cadence.
Aedista didn't just advise, they built the system alongside us. They understood our operations, made the complex parts simple, and delivered something our teams actually use every day. The result was immediate and, more importantly, built to last.
Trusted by global brands
Frequently asked questions
What AI solutions does Aedista build for enterprises?
Aedista builds production-grade enterprise AI solutions including agentic AI systems that autonomously handle multi-step business processes, intelligent document processing for contracts, invoices, and legal documents, LLM-powered business agents using GPT-4, Claude, and open-source models with RAG (retrieval-augmented generation), workflow orchestration with AI-driven decision-making, and multi-agent systems where specialized AI agents collaborate on complex tasks.
What is agentic AI and how does Aedista implement it?
Agentic AI refers to autonomous AI agents that can reason, plan, and execute multi-step business tasks with human oversight. Aedista implements agentic AI using frameworks like LangChain, LangGraph, AutoGen, and CrewAI, building tool-using agents that interact with enterprise systems such as ERPs, CRMs, and databases. Every implementation includes human-in-the-loop workflows, monitoring dashboards, and guardrails to ensure reliability and compliance in production environments.
How long does it take to implement an enterprise AI solution?
Implementation timelines depend on complexity. An AI proof of concept or pilot project typically takes 4 to 8 weeks. A production-ready AI solution with system integrations generally requires 3 to 6 months. Enterprise-wide AI deployments with multiple agents, custom model fine-tuning, and cross-system orchestration can take 6 to 12 months. Aedista recommends starting with an AI readiness assessment and a focused pilot to demonstrate ROI before scaling.
What data requirements exist for implementing AI solutions?
Data requirements vary by solution type. RAG-based knowledge systems need structured or semi-structured documents such as contracts, manuals, or knowledge bases. Document processing AI requires sample documents representative of the types to be processed. Workflow automation needs access to existing system APIs and historical process data. Aedista conducts a data readiness assessment as part of every AI engagement, identifying gaps and building data pipelines using tools like LlamaIndex, Pinecone, and Azure AI Search.
What is the ROI of implementing enterprise AI?
ROI varies by use case, but Aedista clients typically see 40 to 70 percent reduction in manual processing time for document-heavy workflows, 60 to 80 percent faster response times for customer-facing AI assistants, and significant cost savings from automating repetitive decision-making processes. The team conducts ROI analysis and prioritization during the AI strategy assessment phase, focusing on high-impact opportunities where AI delivers measurable business outcomes rather than technology for its own sake.
How does Aedista ensure the security of enterprise AI systems?
Aedista builds AI systems with enterprise-grade security including data encryption at rest and in transit, role-based access controls, audit logging for all AI decisions, and compliance with industry standards. AI guardrails prevent hallucination and data leakage, while monitoring systems track model performance and flag anomalies. Solutions integrate with existing enterprise security models, including Azure Active Directory and SSO, so AI operates within established governance frameworks.
