01
Automated source monitoring
An agent scrapes and reasons over unstructured data from thousands of news sources, surfacing what matters instead of every keyword match.
Agentic AI in media
Agents that read and answer across languages and sources .
We build agents that monitor unstructured content at scale and answer reader or trainee questions grounded in your own material — in more than one language when the audience needs it.
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
Media operations run on more content than any team can read
Monitoring thousands of sources, answering reader questions accurately, and supporting training at scale are all retrieval problems: a large body of unstructured content and a stream of specific questions that need a sourced, current answer. Keyword tools and static FAQ pages do not scale with the volume.
every answer traces to a retrieved source
Use cases
Where agents earn trust in media operations
High-volume, unstructured-content workflows with a real audience on the other end.
02
Multilingual reader & trainee support
A RAG-based chatbot answers questions in multiple languages, grounded in your editorial or training material — not a generic translation layer.
03
Editorial knowledge-base retrieval
Agents answer from your archive with sourced, auditable citations rather than a generic model's general knowledge.
04
Content pipeline automation
Coordinates multi-step editorial or distribution workflows across systems, escalating judgment calls to a human editor.
The cost of unanswered questions
What changes when an archive can answer for itself
These numbers come from real shipped agentic AI engagements. ARIJ Network's is a direct media deployment — investigative-journalist training, English and Arabic, answered from ARIJ's own knowledge base. The Mixam and Synera figures are not media; they are here because they measure the multi-tool orchestration and multi-step validation an editorial retrieval agent runs on. Every figure below links to the case study behind it.
Agents do not decide what gets published or what an editor puts their name to. They retrieve, cross-check and draft what sits before that decision. Every source it used is logged.
Knowledge-inquiry response rate before the agent
1%
A direct media deployment — ARIJ Network's Moodle knowledge base answered almost nothing outside a narrow set of scripted replies.
95.4%
Success rate in workflow results
Not a media deployment — A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations.
2 hrs → 3 min
to generate a validated workflow
Not a media deployment — Engineers moved from hours of tedious setup to minutes, through multi-step validation rather than a single generation pass.
Client results
Proof from a production media deployment
ARIJ Network is direct media proof — a bilingual English/Arabic agent answering investigative journalists from ARIJ's own knowledge base across 22 countries. Mixam and Synera are not media deployments; they are here because they measure the same multi-tool orchestration and multi-step validation an editorial retrieval agent depends on.
Delivery path
From workflow audit to a production agent
TriStorm keeps content accuracy and engineering aligned — retrieval quality questions surfaced before full build commitment.
Map content and workflow
We audit target processes, content sources, and language requirements — ranking automation candidates by reach and impact.
- Content & workflow audit
- Language requirements review
- Prioritised use case
Build and validate the agent
We implement against your real editorial content, with an evaluation suite scored for accuracy before any output reaches a reader.
- Working prototype
- Evaluation suite
- Escalation rules
Deploy with monitoring
Production rollout with monitoring and audit logging, plus a structured handoff so your editorial 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 editorial and archive 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 media, answered
Where does agentic AI fit into media and publishing operations?
In workflows that need to monitor, retrieve, or respond across large volumes of unstructured content — source monitoring, multilingual reader support, and training or onboarding content that needs to answer real questions, not just serve static pages.
How is this different from a keyword-based news monitoring tool?
A keyword tool matches strings. An agent reads and reasons over the content it retrieves — the same mechanism we used to build a platform scraping unstructured data from thousands of news sources using LLMs, LangChain, and LlamaIndex.
Can an agent support readers or trainees in multiple languages?
Yes — we built a RAG-based agentic chatbot in English and Arabic for ARIJ Network, supporting investigative-journalist training at scale across 22 countries.
What other media workflows suit an agent?
Automated data scraping and monitoring across thousands of sources, multilingual content support, and retrieval-grounded answers over a large editorial knowledge base.
What is the typical timeline to a working system?
A scoped Proof of Value (one workflow, real data, a working agent) typically lands in three to six weeks, following the same TriStorm phases as any Vstorm engagement.
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 content workflow worth automating
A 30-minute call identifies the content sources, language needs, and a realistic path to a working agent.