01
Benefits & permit eligibility review
An agent reads the application and supporting documents, checks them against the eligibility rules, and drafts a sourced determination for a caseworker to approve.
Agentic AI in government
Agents that work inside public sector casework .
We build agents that read case files against statute and policy, draft sourced determinations, and log every step — with the human sign-off and audit trail a records officer or IG audit will actually accept.
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 government AI pilots die in legal review, not in engineering
Benefits eligibility, permit review, records requests and procurement compliance are rule-bound, document-heavy decisions where being wrong is expensive. Legal and records review is where the pilots die, and technical capability is rarely the reason. What sinks them is an agent that cannot show its work: no citation back to the policy clause it applied, no log of what it read, no clean escalation path when a case falls outside the rule set.
every determination traces to a cited policy clause
Use cases
Where agents earn trust in public sector operations
Workflows with a documented rule set and a review step already built into the process.
02
Public records & FOIA response assembly
Searches across document stores, applies redaction and exemption rules, and assembles a response packet with a log of every inclusion and exclusion.
03
Procurement & compliance checks
Cross-references vendor submissions against procurement rules and prior awards before a contracting officer signs off — flags gaps instead of missing them.
04
Correspondence & inquiry triage
Classifies incoming citizen correspondence, drafts a response grounded in current policy, and routes anything ambiguous to the right department.
What the mechanism delivers
The reliability bar a public body has to clear before go-live
None of these figures come from a government deployment — we have not shipped one, and we will not dress up a number from another sector as public sector proof. They come from Schmitt-Thompson (healthcare), Synera (engineering software) and Mixam (print on demand), and they are here because they measure what casework depends on: staged retrieval and validation instead of a single generation pass, multi-step process orchestration, and coordination across many tools without drifting. Every figure links to the case study behind it.
Agents do not issue determinations, grant benefits, or release records. They gather the evidence, apply the documented rule set, and draft what a caseworker, records officer, or contracting officer signs off on — with every step logged.
Manual setup per multi-step workflow
2 hrs
Engineers on Synera's platform assembled each complex workflow by hand, node by node.
0 hallucinations
across 329+ nurse-validated scenarios
Not a government deployment — 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.
95.4%
Success rate in workflow results
A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations.
Client results
Proof from production agentic deployments
We have not shipped a production agent inside a government body. These are the closest available references: Schmitt-Thompson is the strongest regulated-environment case on this site (nurse-triage guidance where a wrong recommendation is a safety event), and Synera and Mixam show the same orchestration discipline at process and tool scale.
Delivery path
From workflow audit to a production casework agent
TriStorm keeps legal, records, and security review aligned with engineering, so a pilot survives contact with an audit.
Map workflow and compliance
We audit the target casework process, the statute or policy it runs against, and your security and records-retention boundary — ranking workflows by volume and review complexity.
- Workflow & policy audit
- Security boundary map
- Prioritised use case
Build and validate the agent
We implement against real case-file shapes, with an evaluation suite scored against your own policy documents before any draft reaches a reviewer.
- Working prototype
- Evaluation suite
- Escalation rules
Deploy with monitoring
Production rollout inside your existing security boundary, with audit logging and a structured handoff so your team operates 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 casework and compliance-reporting 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 government, answered
Where does agentic AI actually fit inside a government agency?
In workflows that are high-volume, rule-bound, and already generate a paper trail — permit and benefits eligibility review, records requests, procurement compliance checks, correspondence triage. We do not deploy agents to make final determinations on citizen-facing decisions. We deploy them to gather evidence, apply the documented rule set, and draft a recommendation that a caseworker or officer signs off on.
How is this different from the chatbots agencies already tried and abandoned?
A citizen-facing chatbot answers one question against a knowledge base and stops there. An agent works the back office: it reads a case file, cross-references it against statute and policy, checks prior determinations for consistency, and produces a sourced draft with every step logged. A chatbot that could not answer off-script says nothing about whether an agent can work a case file against statute with every step logged — the two fail for different reasons.
What does an agent actually do on a benefits or permit application?
It reads the submitted documents, checks them against eligibility rules and required fields, flags missing or inconsistent information, and drafts a determination with citations back to the specific policy clause. A human reviewer approves, edits, or rejects — the agent never issues the final decision unsupervised.
Can agents help with public records or FOIA-style requests?
Yes. An agent can search across document stores, apply redaction and exemption rules, and assemble a response packet with a log of what was included, excluded, and why. The redaction logic and its reasoning are reviewable line by line, which is the part records officers usually cannot get from keyword search alone.
How do you handle audit and records-retention requirements?
Every agent action (the input it read, the rule it applied, the output it produced, and any escalation) is logged and retained to your records schedule. We treat the audit trail as a first-class deliverable, not an afterthought bolted on before go-live. That is the same rigor bar we hold in other zero-tolerance-for-error environments: our clinical triage system for Schmitt-Thompson Clinical Content passed 329+ validation scenarios with zero hallucination events before it touched production.
What happens when the agent is uncertain or the case falls outside policy?
It escalates instead of guessing. Confidence thresholds and policy-coverage gaps route the case to a human reviewer with the full reasoning chain attached, so the reviewer sees exactly why the agent stopped rather than starting the review from a blank file.
Can this run on infrastructure that meets FedRAMP, state, or agency security requirements?
The agent layer is built to sit inside your existing security boundary — your cloud environment, your identity and access controls, your data residency requirements. We do not ask an agency to move data to a new platform to get agentic automation; we integrate with the systems and authorization boundary already in place.
What is the typical path from a pilot to something IT and legal will actually approve?
A scoped Proof of Value on one workflow, using real (or properly de-identified) case data, typically takes a few weeks. From there, TriStorm's discover-build-deploy phases add the monitoring, audit logging, and reviewer handoff a security and compliance sign-off will require before wider rollout.
Start with one workflow
Map one casework or compliance workflow worth automating
A 30-minute call identifies the security and records-retention constraints, the review points, and a realistic path to a working agent your legal and IT teams will sign off on.