The data layer for AI agents.
Aggregate business data from every tool you use into one live operational context. AI agents read it, act on it, and write results back — with scoped access controls so each agent sees only what it should.
AI agents need a data layer. Until now, there wasn't one.
Every company is about to run dozens of AI agents — for product operations, customer operations, supplier operations, marketing, finance. Each one needs the same thing: live operational context it can act on.
The models are ready. The agent frameworks are ready. The bottleneck is the data underneath them — fragmented across 30+ tools, locked behind point integrations, stale by the time an agent reaches it.
Analytical data warehouses weren't built for this. Vertical AI tools solve one job and fragment your data further. iPaaS moves records but doesn't unify them.
Boost.space is the layer that does.
One foundation.
Every operational AI workload.
The five layers behind
production AI agents
Each layer solves a different production problem. Together, they make Boost.space the data foundation agents need to read, act, and write back safely.
We're the layer underneath the tools you already use
Boost.space doesn't replace your existing infrastructure. It connects to it, unifies it, and exposes it to AI.
Your LLMs
Claude, OpenAI, Gemini, Mistral. Connected via MCP.
Your automation
Make, n8n, Zapier, Workato. Run on top of Boost.space's data.
Your stack
Shopify, NetSuite, Salesforce, HubSpot, Klaviyo, ad platforms. Synced bidirectionally.
Your developers
REST APIs, SDKs, sandbox.
The first agent suite is for product catalog. The platform underneath isn't.
Boost.space ships with six ready-made AI agents for product catalog operations — enrichment, supplier onboarding, channel distribution, dynamic pricing, GEO optimization, and product recommendation. In production today with retailers and brands like Decathlon, Stada Pharma, and Europa Biosite.
The platform underneath is horizontal. The same data foundation powers AI agents for customer operations, sales, finance, marketing — anywhere fragmented data blocks AI from acting.
Build your own agents now with our SDKs and MCP, or wait for the next suites we're shipping.
See the catalog agent suite
Why Boost.space wins where the alternatives stall
vs. Building on Snowflake or Databricks
Analytical warehouses read fast, write slow, and model data for analysts — not agents. Building an operational, bidirectional, agent-ready layer on top means a data team, custom pipelines per source, sync engineering, MCP development, and 12–18 months before the first agent runs in production. Boost.space is operational from day one. Production in weeks.
vs. Vertical AI tools
Akeneo AI, Salsify AI, and category equivalents. Each one solves a single job inside a single silo and stores its own data. Adopt three and your fragmentation problem gets worse. Boost.space is horizontal infrastructure — one data foundation, many agents.
Ready to build on Boost.space?
For product and engineering leaders
Walk through your architecture, your data sources, and how Boost.space fits.
For developers and technical partners
MCP quick start, vibecode walkthrough, sandbox access, integration patterns.