Scan
Free scan of your stack and your workflows. We hand back a PDF report mapped to a four-level adoption ladder, with a ranked list of agents to ship next.
READ THE BRIEFTurion plans, deploys, and operates AI integrations for companies — built on the SDKs your team already runs and shipped through the CI you already trust.
We work with companies end-to-end: from introducing AI and finding where it fits, through deploying the first agents, to keeping them healthy in production.
Free scan of your stack and your workflows. We hand back a PDF report mapped to a four-level adoption ladder, with a ranked list of agents to ship next.
READ THE BRIEFPer-agent build, priced for SMBs. From $500/agent for simple work; complex agents quote higher in writing before we start. Source under your repo from day one.
READ THE BRIEFMonthly retainer that keeps the deployed agents healthy. CI/CD, evals on every change, model migrations on a quarterly cadence, on-call humans for hard errors.
READ THE BRIEFWe work with the current industry-standard SDKs to build robust AI agents in production. Pick whichever one your team already knows — we ship through the CI/CD pipeline you already trust, and the runtime underneath stays boring on purpose.
Default when the agent ships inside an existing Next.js or Node app and the team wants the runtime co-located with the rest of the product.
import { streamText } from "ai";
import { openai } from "@ai-sdk/openai";
const result = await streamText({
model: openai("gpt-4.1"),
prompt: ticket.body,
tools: { searchKb, jiraCreate },
}); First-class agent loop with tools, file editing, and bash. Picked when the work is multi-step reasoning, code, or long-context document workflows.
import Anthropic from "@anthropic-ai/sdk";
const client = new Anthropic();
const stream = client.messages.stream({
model: "claude-opus-4-7",
tools: [searchKb, jiraCreate],
messages: [{ role: "user", content: ticket.body }],
}); Responses API and function calling. The right pick when the team is already on the OpenAI stack or needs the managed runtime + web search out of the box.
from openai import OpenAI
resp = OpenAI().responses.create(
model="gpt-4.1",
instructions="You triage tickets for Acme.",
input=ticket.body,
tools=[search_kb, jira_create],
) Model-agnostic Agent Development Kit centred on Gemini and Vertex. Picked when the data, IAM, and observability already live in GCP.
from google.adk.agents import Agent from google.adk.runners import Runner agent = Agent( name="triage", model="gemini-2.5-pro", tools=[search_kb, jira_create], ) Runner(agent).run(ticket.body)
Standard install · lint · eval · deploy workflow. Every promotion runs the regression suite and posts the delta on the PR before a human merges.
# .github/workflows/deploy.yml
jobs:
ship:
runs-on: ubuntu-latest
steps:
- run: pnpm install
- run: pnpm eval --gate 0.92
- run: pnpm deploy --env prod Agentic steps inside CI. Triages failed eval runs, drafts postmortems, and grades PR diffs against the agent's expected behaviour before they reach prod.
# .github/workflows/eval-grade.yml
jobs:
grade:
steps:
- uses: anthropics/claude-code-action@v1
with:
prompt: "Grade eval delta vs main." Same shape as the GitHub workflow, on the runner pool your team already operates. Picked when GitLab is the company default.
# .gitlab-ci.yml
ship:
image: turion/agentic-runner:1
script:
- npm ci
- npm run eval -- --gate 0.92
- npm run deploy -- --env prod Six of the agents currently running for design partners. Each one shipped, instrumented, and on-call.
Resolves T1 tickets end-to-end. Knows your product. Files Jira when it can't.
Drafts technical answers in RFPs and security questionnaires. Cites source.
Long-horizon investigative agent. Plans, browses, synthesizes, defends.
Slack-native. Reads the wiki, writes the runbook, schedules the maintenance.
Daily close, anomaly flags, board-ready memos. Reads NetSuite & Snowflake.
Headless browsing, scraping, form-filling. Acts on the open web like a person.
No bespoke runtime, no proprietary DSL. The agent is an SDK call you can read in twenty lines. The deploy is a workflow file your team already maintains. You can fire us on a Friday and ship on a Monday.
import { generateText, tool } from "ai";
import { openai } from "@ai-sdk/openai";
import { z } from "zod";
// One agent. One SDK. Tools your team already wrote.
const result = await generateText({
model: openai("gpt-4.1"),
system: "You triage support tickets for Acme.",
prompt: ticket.body,
tools: {
searchKb: tool({
description: "Search the support knowledge base.",
inputSchema: z.object({ query: z.string() }),
execute: async ({ query }) => kb.search(query),
}),
jiraCreate: tool({
description: "Open a Jira ticket when escalation is needed.",
inputSchema: z.object({ title: z.string(), body: z.string() }),
execute: async (input) => jira.create(input),
}),
},
stopWhen: stepCountIs(8),
});from openai import OpenAI
client = OpenAI()
# Responses API + tool definitions. Same SDK, same agent loop.
resp = client.responses.create(
model="gpt-4.1",
instructions="You triage support tickets for Acme.",
input=ticket.body,
tools=[
{
"type": "function",
"name": "search_kb",
"description": "Search the support knowledge base.",
"parameters": {"type": "object", "properties": {"query": {"type": "string"}}},
},
{
"type": "function",
"name": "jira_create",
"description": "Open a Jira ticket when escalation is needed.",
"parameters": {"type": "object", "properties": {"title": {"type": "string"}, "body": {"type": "string"}}},
},
],
)# .github/workflows/deploy.yml
name: deploy-agent
on: [push]
jobs:
ship:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: pnpm/action-setup@v4
- run: pnpm install --frozen-lockfile
- run: pnpm eval -- --golden support_v3 --gate 0.92
- run: pnpm deploy -- --env prod --canary 10
# ✓ install · 22s
# ✓ eval · 184 / 200 passed · 0.92 >= 0.92 gate
# ✓ deploy · canary 10% → 100% traffic Every agent decision is a span. Every span is replayable. Every replay produces a delta. We treat agent quality like SREs treat reliability.
“ We tried two other platforms before Turion. Both demoed beautifully and died in production. Turion shipped a working agent in three weeks and has been on the pager with us ever since. That's the part nobody else does.
Sized for SMBs and enterprises alike. A free scan to see what's worth building, per-agent pricing for the build, and a small monthly retainer to keep it healthy. Token spend with the model provider stays on your account.
Pair with one of our solutions architects. Two weeks from kickoff to a deployed, evaluated, observable agent in your stack.