More profit from every order.
Less waste behind your stack.

Buildeon is “build” plus “eon”: building things that last and compound over time instead of one-off deliverables. That idea runs through three parts of the business. Our services are run as continuing systems: we design, build and run what we ship, and keep improving it as usage grows. Our products are built as adaptable infrastructure that can change as models, tools and markets change. And our experiments on Buildeon Labs run in public, with their method and limits on the page, so today’s experiment can become the groundwork for tomorrow’s product or service.

We build applied custom AI agents and AI automation for businesses: support and store agents, workflow tooling, and supervised pipelines that do real work for a team. Our products show that work at different stages. AI SEO Ops is a design-partner build for SEO, AI visibility and agent-readiness audits; SignalOpsome is a pilot for lead generation and outreach; GrowthOps is an AI media buyer in development; and ContentOpsome is in internal use, designed to turn one sourced research pass into posts for each channel. For client teams, we also build agent pipelines for open-source work and reduce the token cost of existing model traffic.

It follows the same loop as everything we build. First we dig into your goals, users and constraints to scope the smallest agent worth building. Then we design the flows and architecture with you and validate them before any code is written. The build ships in weekly increments with tests, CI and demos, so you see progress every week. Once the agent is live, we run, monitor and harden it, and iterate as usage grows.

Control depends on the product. AI SEO Ops changes nothing on a site without sign-off, and its store agent add-on needs approval before anything goes live. SignalOpsome requires approval on every outreach email by default, and GrowthOps does not execute proposed ad changes without approval. Separately, our pre-registered halal-llm-gate experiment is designed so deterministic rules decide what is allowed, a language model can adjust only timing within those rules, and every model decision will be logged and published.

Every build ships with tests and CI, and you see working demos every week. When the question is whether a change actually helps, we measure it instead of assuming it: for token savings, a paired A/B trial on your real workload compares cost, tokens and output quality before rollout. Bigger questions get tested in public. The halal-llm-gate experiment is planned and pre-registers its question, metrics and verdict rule, while our study of agent-written open-source pull requests reports the outcome of every resolved pull request in its campaign window, merged or closed, and how maintainers reacted.

We cut token spend with context compression on the steps that carry large tool output, and with gateway-level prompt caching and routing. Before rollout, a paired A/B trial on your workload measures the change in cost, tokens and output quality. We publish measurements too. In one paired trial of an open-source context-compression tool, cost fell 65.7% on a real agent task, but input tokens fell only 4.4%, output quality was not independently assessed, and it was a single paired run, so the cause of the drop can’t be pinned down. An ongoing token-savings page reports what an open-source LLM gateway records on the traffic we send through it.

Agentic commerce is shopping where AI does part of the work: assistants and shopping agents find, compare and act on products for a shopper, while store agents handle work behind the counter. AI SEO Ops, our design-partner build, audits whether AI search engines and shopping agents can find, read and act on a store, covering SEO, AI visibility, agent readiness, agentic-commerce protocols and merchant feeds. For ecommerce sites it offers two optional add-ons: a custom store agent for product knowledge, support and order operations, and a catalog agent that turns raw product data and spreadsheets into clean, enriched product data. A human approves before anything goes live.

Buildeon Labs is where we test claims in public. Each experiment page says whether it is pre-registered or observational, shows its method and limits next to the data, and publishes results whatever they show. The work includes ongoing observational records of agent-written open-source pull requests and token savings, a concluded one-off context-compression trial, and a planned pre-registered paper-trading test of whether a language model’s timing beats a fixed weekly schedule. Our case studies link to the full write-ups there.

Email hey@buildeon.solutions or use the contact page. A short brief helps: your company, team size, current setup and timeline. You can pick a specific option there, such as token savings or an issue-to-release agent workflow, or just tell us where you’re headed. We reply within one business day with an honest take on how, and whether, we can help.