44% → 98%
Clinical triage at Schmitt-Thompson
A guideline-executing agent lifts a raw LLM from 44% to 98% accuracy on the open 50-scenario clinical benchmark.
AI agent development company
AI agent development services, from Proof of Value to production
AI agent development services for mid-market and enterprise teams. Vstorm builds, tests and deploys custom agents in your environment, and you keep the code.
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1% → 100% knowledge-inquiry response rateBilingual English/Arabic agent inside ARIJ's Moodle environment, answering only from ARIJ's own knowledge base.
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44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmarkHIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark.
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2 hrs → 3 min to generate a workflowText-to-workflow agents building validated node graphs inside the platform.
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+11.76% orders from day 1 of the Australian launchThree-agent product advisor guiding customers through print-order configuration.
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Supply chain intelligence agents reducing manual coordination overhead.
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AI agent implementation for a global automotive enterprise.
Results in production
What our AI agents do for clients today
Most AI agent projects stall between the demo and the first real users. The three results below come from agents that made it into daily operations, each with a different architecture.
2 hrs → 3 min
Engineering workflows at Synera
An agent that validates its own steps turns a plain-language request into a working engineering workflow in minutes instead of hours.
95.4%
Print-order configuration at Mixam
A three-agent product advisor completes customer workflows across more than a billion product combinations.
Choose the architecture
Which kind of AI agent fits your workflow
The workflow decides the architecture. We recommend one in the first phase and explain why the alternatives lost.
Single-agent systems
One agent owns one bounded workflow and calls the tools it needs. Faster to build and easier to audit.
Multi-agent systems
Several specialist agents split a workflow that is too broad for one, with an orchestrator between them.
RAG development services
Retrieval over your documents and systems, so agents answer from your knowledge instead of the model's memory.
AI chatbot development
Customer-facing and internal chatbots grounded in your docs, with handoff to a person when needed.
Scope of work
What an AI agent development engagement covers
Six stages from the first workshop to a system your team runs. You can start at any of them.
See the TriStorm methodology 01
Workflow selection
We rank candidate workflows by value, feasibility and risk, and pick the one where an agent pays off first. You get the ranking, the target architecture and the points where a person stays in control.
02
Proof of Value on your data
A working agent runs on your data, your systems and your edge cases. It ends with a go or no-go decision backed by measured accuracy and running costs.
03
Production build
Integrations with your systems, tool design, guardrails and evaluation suites. The agent is tested end to end for stability, security and performance before release.
04
Deployment and monitoring
The agent goes live in your environment with monitoring on every run, so you can see what it did and why. Launch support resolves issues before they reach users.
05
Audit of an existing agent
Already running an agent? We review the architecture, model configuration, code, data security and observability, and hand you a prioritized list of fixes.
06
Ownership transfer
Code, runbooks and training move to your team. After handover the agent runs without us, with no licence or platform fee to Vstorm.
Engagement models
Three ways to work with us
End-to-end delivery
We run the whole engagement with the TriStorm methodology, from workflow selection to ownership transfer, with one team accountable for the result.
Team extension
Our AI engineers join your team and work in your repositories and rituals, billed for the time they work.
Audit and advisory
A review of an agent you already run, or a second opinion on an architecture before you commit to a build.
Technology
The stack behind our AI agents
We choose the stack per project and treat the model as a replaceable part of the system.
Pydantic AI
Typed agents with validated outputs. Vstorm contributes to the Pydantic AI ecosystem.
LangChain and LangGraph
Agent graphs and orchestration where the project already builds on the LangChain ecosystem.
Retrieval and vector stores
LlamaIndex with Milvus, Qdrant or pgvector, sized for the volume and the deployment model.
Self-hosted or cloud
Hosted models or open-weight models on your own servers when data cannot leave your environment.
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Print on Demand
Order configuration, catalog scale, POD operations
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E-commerce
Product data, on-site search, order configuration
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Healthcare
Providers, payers, and clinical workflows
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Technology & SaaS
Agentic features shipped inside your own product
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Financial Services
Close, reconciliation, claims, and underwriting
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Supply Chain
Exception routing across purchase and vendor documents
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Manufacturing
Engineering and production workflows that close the loop
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Customer Service
Answers from your docs, handoffs with context attached
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Retail operations
Assortment, merchandising, and store operations
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Construction & Engineering
RFIs, submittals, and field report synthesis
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Mining & Resources
Shift decisions traced back to their source layer
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Defence & Public Safety
Assessment work with a cleared reviewer as the gate
Case studies
AI agents we took to production
Clinical triage, investigative-journalism support, engineering workflow generation and print-order configuration.
Track record
Agents that reached production
Counted deployments and people, not projections.
30 +
Production deployments since 2017
25 +
AI engineers on the team
12
Industries delivered into
FAQ
Questions about AI agent development
What do AI agent development services include?
The full path to production: choosing the workflow, designing the agent architecture, a Proof of Value on your data, the production build with integrations and guardrails, deployment with monitoring, and handover of code and runbooks to your team. You can also bring us in for one part of that path, such as an audit of an agent you already run.
Should we build a single agent or a multi-agent system?
Start with one agent when the workflow is bounded and the data sources are manageable, because it is faster to build and easier to audit. A multi-agent system is worth the extra complexity when the work is too broad for one agent or needs several specialists running in parallel. We make that call in the first phase of the engagement, before any build.
How is a custom AI agent different from RPA or an automation platform?
RPA and workflow platforms follow fixed rules and break when the input or the process changes. An AI agent chooses the next step or tool based on the situation, so it can work with unstructured documents and handle exceptions. That flexibility needs evaluation, guardrails and monitoring, and we build them in from the start.
How long does AI agent development take?
The first milestone is a Proof of Value on your own data, so you see whether the agent works before committing to a full build. The production timeline depends on the integrations, the state of your data and your compliance requirements, and we estimate it once the Proof of Value is done.
How much does AI agent development cost?
Cost depends on the number of workflows, the systems the agent connects to, and your hosting and compliance requirements. Starting with a Proof of Value keeps the first commitment limited and shows the running costs of the agent before the production build.
Do we own the AI agents you build?
Yes. Code, configuration and runbooks move to your infrastructure, and the system keeps running without us. Vstorm charges no licence or platform fee for the agent itself; model and hosting costs stay yours and are visible from the Proof of Value onwards.
Can the agent run on our own infrastructure?
Yes. For data-sovereignty requirements we deliver fully self-hosted agents with no external API calls, running on your servers and under your control. The model is designed as a replaceable component, so changing it does not mean rebuilding the system.
Which frameworks and models do you use?
We build with Pydantic AI, LangChain, LangGraph and LlamaIndex, and choose the model per task, including open-weight models for self-hosted deployments. Vstorm contributes to the Pydantic AI ecosystem and publishes its agent tooling as open source.
What happens after the agent goes live?
An agent that works on day one can drift as your data and workflows change. We set up monitoring and feedback cycles at launch, and can keep maintaining models and integrations afterwards, or train your team to do it.
Which companies do you work with?
Mid-market and enterprise companies, and larger startups with a production system to build, in Europe, the United States, the United Kingdom and the Middle East.
From the blog
Reading on AI agent development
Agentic AI
Top 10 AI agent development companies for business automation (2026)
Ten AI agent development companies for business automation, ranked on delivered agent projects, team credentials, services and thought leadership.
Agentic AI
Single agent vs multi-agent systems: when one agent is not enough
One agent or several? Deterministic orchestration beats both when the flow is predictable. Pros, cons and the architectures, with production examples.
Agentic AI
AI agents can act: can you still control them?
Agents that issue refunds need identity, authorisation, approval and audit before the call lands. What an agentic AI control plane is, and when you need one.
Agentic AI
Dynamic Tool Discovery: A Systematic Evaluation of 8 Search Strategies for AI Agents
51 tools cost 18,492 tokens per query. Eight discovery strategies — regex, BM25, RAG and more — evaluated on accuracy, latency and token cost.
Start with one workflow
Ready to scope your first production AI agent?
Pick one real process, see the agent work on your data in a Proof of Value, and decide on the production build with the numbers in front of you.