01
Email response automation
Reads incoming support emails and drafts a response grounded in your actual product and account data — the same context-grounded mechanism behind our multi-agent product advisor for Mixam.
Agentic AI in customer service
Support answers grounded in real account data .
We build agents that read a customer's actual account and order data, answer or route the request, and escalate only what genuinely needs a person — with the context already attached.
Trusted by operators
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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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37 kernels ported from CUDA to Intel GaudiProduction AI workloads moved to new hardware for on-prem LLM deployment.
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AI agent implementation for a global automotive enterprise.
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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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0 hallucinations across 329+ validation scenariosHIPAA-compliant agentic RAG over proprietary clinical triage guidelines.
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Why agentic AI
Most support automation answers from a script, not from your data
A scripted bot handles the questions someone anticipated. Everything else becomes a dead end the customer has to escape — and that escape route is not a nice-to-have. In a Gartner survey of 3,566 B2B and B2C customers run in February and March 2026, 87% said a company using generative AI for service must still provide access to a human agent. The same research found customers are markedly more willing to reuse a tool that did not make them fight their way past it.
every answer grounded in a named source
Use cases
Where agents earn trust in customer support
Requests that need real context, not a canned response.
02
Multilingual support chat
Answers questions in the languages your customers actually use, grounded in your own documentation rather than a generic translation layer.
03
Order and account status
Answers questions grounded in the customer's real order and account data, not a generic FAQ disconnected from their actual situation.
04
Intelligent request routing
Reads the request and routes it to the right specialist or system with reasoning attached, instead of a generic ticket queue.
The cost of answering from a script
Where support conversations lose customers today
ARIJ Network is a direct multilingual support deployment. Mixam and Schmitt-Thompson are adjacent (a guided product advisor and a clinical triage system), and they demonstrate the two mechanisms a support agent lives or dies on: answering only from a named source, and declining to answer when the source does not cover the case. Every figure below links to the case study behind it.
Agents do not decide refunds or rewrite policy on their own. They close the requests your own documentation already answers, and hand the rest over with the context gathered — which is the part that decides whether customers come back to the channel.
Knowledge-inquiry response rate before the agent
1%
ARIJ Network's Moodle knowledge base went largely unanswered outside a narrow set of scripted replies.
95.4%
Success rate for a production multi-agent advisor
Adjacent, not a support desk — A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations.
0 hallucinations
Hallucination events across 329+ validated scenarios
Adjacent, not a support desk — Nurse-triage guidance where a wrong recommendation is a patient-safety event — staged retrieval and validation rather than one model answering in a single pass.
Client results
Proof from production support deployments
ARIJ Network is a direct support deployment. Mixam and Schmitt-Thompson are the closest available proof for the mechanism underneath — grounded answers tied to a named source, and a refusal path that was designed rather than discovered in production.
Delivery path
From workflow audit to a production support agent
TriStorm keeps response quality and engineering aligned — the requests an agent should never close alone are named before full build commitment.
Map support workflows and data
We audit target request types, CRM and help-desk boundaries, and data quality — ranking automation candidates by volume and by the cost of getting the answer wrong.
- Workflow & data audit
- System integration map
- Prioritised use case
Build and validate the agent
We implement against real support data, with an evaluation suite scored for accuracy and for correct refusal before any response reaches a customer.
- Working prototype
- Evaluation suite
- Escalation rules
Deploy with monitoring
Production rollout with monitoring, audit logging and reporting on handoff and repeat-contact rates together, plus a structured handoff so your support team runs the system independently.
- Production deployment
- Audit trail & monitoring
- Operator runbook
Not sure where to start?
A 30-minute call is usually enough to find your highest-value use case
Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own support workflows. Every example is something already running in production.
Independence
How we help you stay independent
Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement.
Technological sovereignty
We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe.
Small language models
Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure.
Open source
We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source.
FAQ
Agentic AI in customer service, answered
What is the actual difference between a support chatbot and an agentic support system?
A chatbot answers one message at a time from a script. An agent reads the customer's account and order history, reasons across that context, and completes the request (a refund, a status update, a routing decision), escalating only when it should not decide alone.
Where does agentic AI actually pay off in customer service?
Wherever a support answer needs more than a canned response — grounded in a customer's real account data, a product's real documentation, or a multilingual audience that a single script cannot serve.
Do customers just ask for a human anyway?
Many will, and the design should assume it rather than fight it. In a Gartner survey of 3,566 B2B and B2C customers run in February and March 2026, 87% said a company using generative AI for service must still provide access to a human agent. Klarna is the cautionary case: on its own figures it announced in February 2024 that its assistant was handling two-thirds of service chats, and by May 2025 its CEO told Bloomberg the company had pushed cost too hard at the expense of quality and began hiring support staff again. We build the handoff as a first-class path with context attached, not as a fallback that only fires after the customer has given up.
Can an agent answer support questions in more than one language?
Yes — we built a RAG-based agentic chatbot in English and Arabic for ARIJ Network, taking their inquiry response rate from 1% to 100%.
What other support workflows suit an agent?
Email response automation grounded in your product catalog, order-status and account questions with real context, and routing complex requests to the right specialist instead of a generic queue.
Can this integrate with our existing CRM or help desk?
Yes, through your existing APIs. We map your support stack first, then build the agent to read and write through your current auth model.
How do you measure whether it is actually working?
Not with a deflection rate on its own. Deflection is easy to inflate, because a customer who gets a useless answer and closes the tab without asking for a person can be counted as a success. We report the share of requests resolved with a citation the reviewer can check, the repeat-contact rate on those same requests, and the handoff rate, because a handoff rate falling while repeat contacts rise is a system getting worse, not better.
Do we own the system after it is built?
Yes. Full ownership of agent logic and integrations — no proprietary runtime lock-in.
Start with one workflow
Map one support workflow worth automating
A 30-minute call identifies the request types, data sources, and a realistic path to a working agent.