AI Agent Development
We build AI agents that work in production.
AR Data Intelligence Solutions designs and ships autonomous AI agents, multi-agent systems, and agentic workflows for enterprise and scaling businesses. We work across the full stack — from architecture and tool selection to orchestration, evaluation, and production deployment.
We build with LangChain, LangGraph, CrewAI, OpenAI, Claude, and Groq. We design agents that use tools, call APIs, reason over proprietary data, and hand off tasks to specialized sub-agents — built to the standards of financial services, healthcare, and enterprise SaaS.
What we build
AI agent development is not a single category. The right architecture depends on your data environment, latency requirements, compliance posture, and what the agent needs to do autonomously versus when it needs a human in the loop. We build across all of these shapes.
Voice AI Agents
Inbound call handling, lead qualification, and appointment booking agents that operate over voice in real time. These agents understand spoken intent, follow dynamic conversation flows, escalate to humans when appropriate, and log structured outcomes to your CRM or database. We design these for sales teams, healthcare intake, and customer support operations that need to handle volume without adding headcount.
Internal Workflow Agents
Agents embedded in your internal operations — document processing pipelines that extract structured data from unstructured files, ticket triage agents that read support requests and route them to the right team, meeting summarization agents that convert transcripts into action items and decisions. These agents eliminate the manual steps that slow teams down without requiring process redesign.
RAG Agents — Knowledge Retrieval Over Proprietary Data
Retrieval-Augmented Generation agents that give LLMs accurate, grounded access to your internal knowledge base — documentation, contracts, policies, research, or any proprietary corpus. We design the chunking strategy, embedding pipeline, vector database schema, and retrieval logic so the agent returns answers that are accurate, attributed, and appropriate for your use case. We work with Pinecone, Weaviate, pgvector, and Chroma depending on your infrastructure constraints.
Multi-Agent Systems
Orchestrated pipelines where a planner agent decomposes a goal and delegates to specialized sub-agents — a researcher, a writer, a validator, an API caller. We use LangGraph and CrewAI to design stateful, fault-tolerant multi-agent graphs that handle complex tasks requiring parallelism, iteration, and conditional branching. These are the architectures behind autonomous research assistants, end-to-end proposal generation systems, and complex data enrichment pipelines.
API Integration Agents
Agents that connect disparate enterprise systems — reading from one platform, transforming data, and writing to another — without requiring a human to move information manually. These agents use tool calling to interact with REST APIs, GraphQL endpoints, databases, and internal services. We build these for organizations where the bottleneck is not data availability but the cost of moving data between systems reliably at scale.
Monitoring and Alerting Agents
Agents that watch system metrics, logs, or data feeds and take action when conditions are met — filing tickets, sending alerts to the right channel, running a remediation script, or escalating to a human with a plain-language summary of what is happening and why. These agents reduce the gap between an event occurring and the right person knowing about it in context.
Industries we've built for
AI agents are not industry-agnostic in practice. The architecture, the compliance requirements, the tolerance for hallucination, and the definition of a correct output are all domain-specific. Our background across financial services, healthcare, government, and SaaS means we understand what production looks like in each context — not just what is technically possible.
Regulatory complexity, audit trails, and zero tolerance for data leakage define agent design here. We have delivered data infrastructure for Macquarie Bank and Scotiabank and carry that standard into every financial services engagement. Agents we build for this sector are scoped narrowly, logged completely, and tested against adversarial inputs before deployment.
HIPAA compliance is a hard constraint, not an afterthought. We build agents for healthcare clients with data minimization, access controls, and audit logging baked into the architecture. Clinical documentation, intake automation, and internal knowledge retrieval are all areas where we design for both accuracy and compliance.
SaaS companies building AI into their product need agents that are reliable at scale, observable in production, and safe to put in front of customers. We design for multi-tenant isolation, rate limiting, graceful degradation, and evals that track regression across model updates.
Government deployments require explainability, data residency compliance, and procurement-compatible delivery structures. We have navigated complex institutional environments at IBM and understand how to scope and deliver AI projects inside organizations where risk posture drives every technical decision.
Smaller organizations need agents that deliver leverage without requiring a dedicated AI team to maintain them. We design for simplicity and operability — agents that a small team can monitor, adjust, and trust without deep ML expertise in-house.
How we build AI agents
Most AI agent projects fail not because the technology is wrong but because the architecture decisions are made too late, the evaluation strategy is missing, or the deployment environment was never factored into the design. We work differently — every engagement starts with architecture, not code.
Architecture and tool selection
We start by understanding what the agent needs to do, what data it needs access to, and what the acceptable failure modes are. From there we select the framework — LangChain for single-agent tool use, LangGraph for stateful multi-step reasoning and branching flows, CrewAI for role-based multi-agent collaboration — and the model layer — OpenAI GPT-4o, Anthropic Claude, or Groq for latency-sensitive applications. We do not default to the most popular stack. We select based on the problem.
Orchestration patterns
We design orchestration graphs that handle real-world complexity — conditional branching when an agent needs to decide between paths, parallel execution when sub-tasks are independent, retry logic with backoff when external tools fail, and human-in-the-loop checkpoints where a decision requires oversight. For systems that need to persist state across sessions, we integrate with MCP servers to give agents memory and context that survives beyond a single conversation window.
RAG pipeline design
When an agent needs to reason over proprietary data, the retrieval architecture matters as much as the generation. We design the document ingestion pipeline, choose chunking strategies appropriate to the document type, select and configure the vector database, and implement hybrid search where keyword precision and semantic recall both matter. We test retrieval quality explicitly — not just end-to-end answer quality — because retrieval is usually where RAG systems fail silently.
Evaluation and testing
We build evaluation suites before shipping. That means a dataset of representative inputs with expected outputs, automated evals that run on every deployment, and regression tracking so a model update does not silently degrade performance. For agents with tool use, we test tool call accuracy specifically — whether the agent calls the right tool, with the right arguments, in the right sequence. We treat agent evaluation the same way we treat software testing: it is not optional, it is part of the build.
Production deployment
We deploy on Fly.io for latency-sensitive, globally distributed agents and on AWS for enterprise workloads that require VPC isolation, IAM-scoped permissions, and integration with existing cloud infrastructure. Agents are containerized, observable via structured logging and traces, and designed for zero-downtime updates. We deliver with documentation, runbooks, and handoff — not just a working prototype.
Why AR Data
There are many firms that can wrap an API and call it an AI agent. The difference at AR Data is the 20+ years of enterprise delivery experience that tells us what "production" actually means — not just that the demo works, but that the system holds up under real load, handles edge cases gracefully, integrates cleanly with the existing environment, and can be maintained by the team that inherits it.
That background comes from building data systems at Oracle, leading delivery at IBM, shipping decentralized infrastructure at Protocol Labs, and delivering in the demanding environments of Macquarie Bank, Scotiabank, and Iron Mountain. The firms that trust us with AI agents today are trusting the same engineering judgment that served those organizations — applied to a new class of systems.
We use agentic workflows in our own build loop. That means our agents help us write code, run tests, and review output during development — which is why we deliver in a fraction of the time a traditional development shop would require. We are meaningfully faster not because we cut corners but because we have automated the work that doesn't require human judgment, freeing the human judgment for where it matters.
We work on fixed-scope engagements. You know what you are getting, when you are getting it, and what it costs before we start. No retainer ambiguity, no scope creep disguised as discovery, no six-month runway before you see something working.
Compliance and data security
Regulated industries require more than a working agent — they require an agent whose data handling, access controls, and audit trails satisfy legal and regulatory obligations. We build with compliance in scope from the start, not retrofitted at the end.
For healthcare clients we design agents to HIPAA standards — data minimization, access logging, encrypted storage, and no PII retention in model context beyond what is necessary for the task. For enterprise SaaS clients with SOC 2 obligations we design for auditability — every tool call logged, every decision traceable, every data access scoped to principle of least privilege. For clients operating in the European Union or handling EU citizen data, we design to GDPR standards, including data residency constraints and right-to-erasure compatibility.
We document the compliance architecture as part of every delivery — not as a separate audit artifact but as operational documentation the team that maintains the system can use. If your AI agent engagement requires a compliance review before it can be approved internally, we can support that process.
Ready to build an AI agent?
30 minutes. We scope real work — what the agent does, what it connects to, and what production looks like for your environment. No pitch deck.
