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Support deflection grounded in your docs
An agent answers from your own documentation and account data via RAG — sourced answers, not hallucinated ones, with clean handoff to a human when it should not decide alone.
Agentic AI for technology providers
Ship agentic features your customers actually use .
We design and deploy production-ready agent systems inside your product — integrated with your existing infrastructure and data pipelines, not a bolt-on demo.
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
Agentic AI is a roadmap item until it ships in production
Technology providers usually have the engineering foundations already. What they have not built yet are the agentic-specific patterns: orchestration across multiple systems, evaluation before an agent touches a customer, and observability once it is live. Without those, a promising internal demo stalls indefinitely.
every agent action logged and traceable
Use cases
Where agentic features earn their place in the product
Multi-step workflows that justify autonomy — not a chat window.
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Proactive usage alerts
Agents analyze product usage and infrastructure signals to surface problems before a customer opens a ticket.
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Multi-agent workflow automation
For workflows spanning multiple systems or user contexts, agents coordinate, hand off tasks, and escalate — reducing the failure surface of a single monolithic agent.
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Agent audit and optimization
For teams with an AI feature already live but underperforming, we diagnose integration gaps and reasoning failures against your target infrastructure.
The cost of demo-mode AI
What production-grade agentic features actually deliver
These numbers come from our shipped technology-provider engagements — not industry averages. Every figure below links to the case study behind it.
What most product teams are missing is the agentic-specific patterns: orchestration, evaluation harnesses, observability. We transfer the pattern and your team owns the system.
Manual workflow configuration in-product
2 hrs
Synera's platform users assembled each complex workflow by hand — the agentic feature was a roadmap item until it shipped.
1% → 100%
Knowledge-inquiry response rate before and after
A bilingual English/Arabic autonomous agent embedded in ARIJ's Moodle environment, answering only from ARIJ's own knowledge base — across 22 countries.
0 hallucinations
across 329+ nurse-validated scenarios
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 technology deployments
“My wish was to come to at least an 80% success rate in the workflow results, and by the time we finished the project, the success rate is, I believe, over 95.4% — so it definitely exceeded expectations.”
ARIJ Network
A bilingual English/Arabic autonomous agent embedded in ARIJ's Moodle environment, answering only from ARIJ's own knowledge base — across 22 countries.
1% → 100%
Knowledge-inquiry response rate before and after
Delivery path
From product roadmap to a production agent
TriStorm keeps engineering and product priorities aligned.
Scope product and data infrastructure
We audit target workflows, existing infrastructure, and data pipelines — ranking automation candidates by customer impact and integration effort.
- Product & data audit
- Integration feasibility review
- Prioritised use case
Build, test and validate
We implement against your real product data, with an evaluation harness scored before any output reaches a customer.
- Working prototype
- Evaluation suite
- Escalation rules
Deploy with observability and support
Production rollout with monitoring and cost tracking, plus a structured handoff so your team owns and extends the system.
- Production deployment
- Observability dashboard
- Engineering handoff
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 product roadmap. 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 for technology providers, answered
What is agentic AI for technology providers?
Agentic AI for technology and SaaS providers is software embedded in a product that plans and completes a multi-step task across the provider's own APIs and data — answering from proprietary documentation, taking an in-product action, and escalating to a human when it should not decide alone. Unlike a chatbot that answers one message at a time, an agent coordinates across systems and holds context across steps, which is why it ships as an architecture decision rather than a prompt.
Why do agentic features stay stuck in demo mode for so many products?
Because teams build a single-turn wrapper around an LLM and call it an agent. A real agent coordinates across systems, holds context across steps, and knows when to hand off to a human — that requires an architecture decision made before the first line of integration code, not a prompt tweak after launch.
How is this different from adding a chatbot to our product?
A chatbot answers one message at a time. An agent completes a multi-step workflow (verification, routing, decision-making, escalation) coordinating across your APIs and data sources without a human driving every step.
What agentic features do technology providers actually ship in production?
Support deflection grounded in your own documentation, usage-based proactive alerts, and multi-step workflow automation embedded directly in the product — not a side panel bolted onto the UI.
Can agents reason over our own product documentation and customer data?
Yes — this is a RAG engineering problem: retrieval pipelines built for your latency and data-governance requirements, so agents answer from your proprietary knowledge base instead of the model's general training data.
We already have engineers. Why bring in Vstorm?
We bring the agentic-specific patterns (orchestration, evaluation harnesses, failure-mode handling) and work alongside your existing team, transferring the capability rather than leaving you dependent on us to maintain it.
Can you help if we already shipped an AI feature that is underperforming?
Yes. We audit integration gaps, reasoning failures, and observability blind spots in existing deployments and fix them with your target infrastructure in mind — this does not require starting over.
Do we own the code after the engagement?
Yes. Full ownership of agent logic, integrations, and evaluation harnesses — designed to run inside your existing product infrastructure without requiring a replatform.
What kind of impact does an embedded agentic feature deliver in production?
It depends on the workflow, but the gains come from collapsing manual, multi-step work. On Synera's platform, users assembled each complex workflow by hand in about two hours; the embedded text-to-workflow agent generates a complete workflow from text in roughly three minutes. Their published library of 1,000+ existing workflows was the training input for that agent, not output it has since produced.
Start with one workflow
Ship one agentic feature that survives production
A 30-minute call identifies the workflow, the data it needs, and a realistic path to a working agent inside your product.