Why Algomo
The hard part is connecting an agent to the systems that hold the answers. Most vendors hire engineers to do it per customer; we built the agent to do it itself.
An agent that cannot integrate itself is not finished
Every AI agent vendor has the same secret, and it is human. Sierra staffs ["a dedicated agent engineer and product manager"](https://sierra.ai/careers) on each of its deployments, in its own words. Decagon built an [Agent Deployment Engineering organisation](https://decagon.ai/careers) and tells candidates that customers cannot just "set it and forget it". Salesforce certifies a partner network of engineers it calls, in as many words, forward-deployed engineers.
The fastest-growing job in AI is doing integration by hand. Postings for forward-deployed engineers [grew more than 800% over nine months of 2025](https://www.pymnts.com/artificial-intelligence-2/2026/forward-deployed-engineers-emerge-as-one-of-ais-fastest-growing-jobs/). Salesforce has [committed to a thousand of them](https://www.salesforce.com/ap/blog/forward-deployed-engineer/). OpenAI and Anthropic embed theirs with their largest customers. It is a model only enterprises can afford; everyone smaller gets a chatbot and a knowledge base.
And still the deployments die. [Gartner predicts over 40% of agentic AI projects will be canceled by the end of 2027](https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027), and names integration with legacy systems among the reasons. [S&P Global measured 42%](https://www.spglobal.com/market-intelligence/en/news-insights/research/ai-experiences-rapid-adoption-but-with-mixed-outcomes-highlights-from-vote-ai-machine-learning) of companies abandoning most of their AI initiatives in 2025, up from 17% the year before. [Enterprises take nine months or longer](https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf) to get from pilot to production. [As one forward-deployed engineer put it](https://thenewstack.io/forward-deployed-engineers-ai/): "The model is usually the cleanest part. The hard part is finding the workflow nobody documented."
We made the opposite bet: the agent does the engineer's job. A forward-deployed engineer on an agent project does two things: wires the agent into your systems, and translates what the business wants into how the agent behaves. Algomo does both itself. It finds the endpoints your pages already call and builds its own tools, which is self-integration. You state the rules in plain language and it implements them, which is AI-first.
We can make that bet because of where the agent runs. Everyone else integrates server-side, which means API contracts, credentials and meetings: humans. Algomo works inside the visitor's own session, riding the interface your website already exposes to every visitor. The integration surface already exists, and it already works. And when your site changes, keeping up is the agent's job, not a project milestone.
Everything else follows. No implementation fee, no configuration language, nothing to schedule, and pricing that spends your monthly amount only on conversations that actually helped someone, never above it. The honest edge of the claim: what sits behind a login is connected in a setup session, never an engineering project. Everything your pages already do out in the open takes minutes.
And you do not have to take any of this on faith. The argument above is testable: point Algomo at your site on the free plan and watch it wire itself in.
The standard deployment, and ours
What the industry calls a deployment, next to what Algomo asks of you.
|  | The standard deployment | Algomo |
| --- | --- | --- |
| Design workshop | 90 minutes | None |
| Implementation | Six weeks | Minutes, on your own site |
| Engineers | Staffed per customer | None |
| Meetings | Weekly, indefinitely | None |
The left column is the deployment these vendors describe in their own hiring and methodology pages, sourced on our thesis page. The two durations illustrate that shape rather than quoting any single vendor.
Read the argument in full
Our thesis
The hidden cost of AI agents is the humans behind them, with every figure sourced.
[Our thesis](/resources/thesis)
The three consequences
Self-integration: the agent connects itself
It finds the APIs your pages already rely on and uses them. Private systems are connected in a setup session.
AI-first: you run it by talking to it
You set what it can do, what it must never do, and when to stop. Ask for a missing capability and it can write itself the tool.
Co-navigation: it can work the page with the visitor
It sees what they see and can operate the site for them, which is often faster than explaining where to click.
Proof before commitment
You can have the agent running on your own site before you talk to anyone here.
On your site, the agent acts inside the visitor's own browser session: the price it quotes is the price they would be shown, even on sites that block server-side traffic.