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DELOS AG
First paying enterprise customers in sixteen weeks
Agentic Development Workflow™Stop integrating AI.
Stop integrating AI.
Start delegating to it.
Agentic workflows that own the work end-to-end — in production, not in a slide deck.
Agent · Live
- Ticket received2s
- Intent classified4s
- Code written · CI green6s
- Human review (architecture)running
- PR merged to productionqueued
Part 1 — What we build with AI
AI that does the work — not just talks about it.
We put AI inside your product where it earns its place: autonomous agents that classify, decide and execute — not chatbots that wait to be asked.
Yesterday · AI Features
Chatbots & copilots
Suggestions in a sidebar. A human still drives every decision — AI just rides shotgun.
Now · Agentic AI
Systems that finish the work
Autonomous systems that complete work end-to-end. Humans review what matters; everything else just gets done.
The real difference
Everyone has the same AI. The speed comes from the team that knows how to use it.
Working with Streaver means a dedicated team that builds with AI every single day. The agentic workflow is baked into how we operate — so your processes get faster from week one, not after a year of figuring it out on your own.
Dedicated teams, fluent in AI
100% dedicated, native-English engineers who use these tools daily — not occasionally.
An AI Lead + Fractional CTO inside
Senior judgment on every project — so the speed never costs you quality.
Faster processes from day one
You don't adopt AI alone — you plug into a team already running on it.
Built with AI · in production
AI products we’ve already shipped.
91% precision · 4-agent systemRead the case study
Live
MANKA
AI on WhatsApp helping rural mothers feed their children
Live
Arkeo AI
Three AI agents went dark. We brought them back in three months.
Featured case study — a founder who shipped agentic AI to production
LiveVibe-code → production
Supreme Golf
Building a $1M product for $125K with a non-technical CEO at the keyboard
Five years after pausing the partnership, Supreme Golf came back. We rebuilt the way they build software — vibe coding with guardrails, AI-augmented stack, daily production deploys.
- 8× lower build cost vs. 2023 baseline
- 3–5 production deploys / day
- Non-technical CEO shipping code
8× lower cost · 3–5 deploys/dayRead the case study
Agentic Development Workflow
Part 2 — How we work
Beyond AI features. Workflows that own the work.
Our method, in three phases — repeatable, refined across 30+ deployments. Here's what actually happens in each.
01DESIGN
We map your real workflows, score each by ROI vs. feasibility, and define exactly where an agent takes over and where a human stays in the loop. You leave with a ranked plan — not a vague promise.
map → scope → human-in-loop
02BUILD
Production agents built on the models you already pay for and the tools your team already uses — model-agnostic, with custom harnesses and evals so quality holds as models change. Runs in your cloud, your repos, your keys.
your models · your stack · in prod
03OPERATE
Agents drift, so we don't ship and disappear. We monitor, evaluate against real cases, and iterate week over week — adding new workflows as you grow. The engine under your workflow only gets faster over time.
monitor → evaluate → iterate
What a run actually looks like
From Linear ticket to production PR, without context loss.
Scroll to follow the run ↓
the same pipeline, running
1,284,117
illustrative · AI agents + humans, continuously
the real need
Krisp callsEmailVideo callsTranscriptsSlack threadsAI ↔ humanDesignersPlaybooksIt hears the problem before it acts.
Signal
- 01Capture from every channel — calls, transcripts, chats, tickets, designs.
- 02Distil the real intent, not a vague brief.
- 03Nothing is lost between what you said and what we build.
Krisp · Linear · MCP
- Signal — AI-run (Krisp · Linear · MCP). Capture from every channel — calls, transcripts, chats, tickets, designs. Distil the real intent, not a vague brief. Nothing is lost between what you said and what we build.
- Safe site — AI-run (git worktrees · Docker). Spin up a dedicated worktree and container per task. Wall it off from main and from production. The work happens in full — at zero risk to your live system.
- Right material — AI-run (Claude / Codex · pinned per repo). Profile the repository and the task at hand. Pin the model that fits — per repository, not per fashion. The approach matches your code, not a generic template.
- It holds — AI-run (Gitleaks · Snyk · CodeRabbit). Run the full automated gauntlet before any human looks. Secrets, vulnerabilities, quality, types — all checked. Machines catch the routine so people never waste time on it.
- Human review — human-owned (architecture, not nitpicks). A human reviews the load-bearing decisions. Architecture and judgment calls — not the nitpicks machines handled. Senior judgment where it counts; speed everywhere else.
- Ship — AI-run (measured, reversible deploys). Merge to production in small, measured steps. Each one observable — and reversible. Progress you can see, with nothing you can't undo.
- Self-heal — AI-run (Sentry → auto fix-PR). Production breakage is detected automatically. A fix-PR opens itself and re-enters this same pipeline. Things don't stay broken; reliability doesn't wait on a ticket.
Plus a library of reusable skills, harnesses and evals — every new workflow starts from accelerators, not from scratch.
Full stack & costs
the AI-Native Toolkit
Coming soon
Security & trust
Worried about security?
Your code, your cloud, your keys. Agents run inside your infrastructure — we never hold your data or your IP. Enterprise-grade guardrails, from day one.
Runs in your environment
Your cloud, your repos, your keys. Your data never touches ours.
Supply-chain hardened
Secret scanning, Snyk + Socket on every dependency, and a 7-day package-age gate in CI.
Human-controlled
Every agent action is logged; a human holds the controls on anything irreversible.
Standards-aligned
OWASP guidance for LLM apps, with secure-SDLC baked into the workflow.
Why now, not next year
In the last four months, AI advanced more than in the previous three years.
This is how much human work a model finishes on its own, in one unsupervised pass. It isn’t linear — it’s a doubling curve, and it just went vertical.
Each point is an AI model — hover for what changed. Every few months the work it finishes on its own roughly doubles; the dashed line is where it’s heading next.
3% → 95%
real coding problems an AI can solve (’23 → now). A strong human engineer: ~86%.
~4,000×
cheaper per task, 2024 → 2025.
18–36 mo
before the advantage levels out. That window is the opportunity.
Start with the basics
New to AI? Start here.
What is an agentic workflow?
A system where AI owns a task end-to-end — it classifies, decides and executes, with humans on the exceptions. Different from a chatbot that just answers.
Agent vs chatbot vs copilot?
A chatbot waits. A copilot suggests. An agent finishes the work.
What does “AI-native” mean?
AI in how the software is built and, when it earns its place, in the product itself — not bolted on at the end.
Do I need to be technical to start?
No. An AI Lead + Fractional CTO sit inside the project so you don’t have to.
Before you book the call
Questions execs ask us.
“What’s this going to cost me?”
A 2-week Discovery Sprint, then a fixed-scope Build Sprint. ROI measured before you scale.
“Is our data and IP safe?”
Agents run in your cloud, with your keys — your data and IP never touch ours. We follow OWASP guidance for LLM apps, with secret scanning, dependency review (Snyk + Socket) and a 7-day package-age gate built into CI.
“What if the model changes in 6 months?”
We build model-agnostic. Better model ships, we swap it in — your workflow stays.
“How fast until I see something real?”
First agent in production in 4–8 weeks, not quarters.
How an engagement unfolds.
Three phases, in order — start small, prove it in production, then scale only what works.
- Phase 012 weeks
Discovery Sprint
Map workflows, score by ROI, pick the one to ship first.
- Phase 024–8 weeks
Build Sprint
Ship one agentic workflow to production. Measured ROI, real users.
- Phase 03Ongoing
Operate Retainer
We monitor, evaluate and improve. New workflows as you grow.
Are you going to stay
on the platform?
30 minutes with our CEO, Larry. No pitch. He’ll tell you honestly whether AI is the right move for your business right now, what to build first, and what to wait on.