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Research-led
Let an AI assistant call your business without letting it run it.
An MCP server, tool API and machine-readable surface let ChatGPT, Claude and the agents your customers use query your catalogue, check availability, get a quote or start a booking, with every tool scoped, rate-limited and logged. We run one for our own business.
Who it is for
Built for the team that does this by hand.
Businesses whose customers already ask an AI assistant before they search
Product companies that want their data queried by agents on their terms rather than scraped
Teams that need internal tools an assistant can call safely: read a price, open a ticket, check a slot
What it does
MCP servers and agent-callable surfaces, by the job.
Exposes bounded tools
Each tool does one thing an assistant should be allowed to do: read a price, check a slot, quote a job, open a ticket. Nothing an assistant should not do exists to be called.
Describes the business to machines
llms.txt, structured data, a WebMCP surface on the site and a public MCP endpoint, so an assistant finds the right tool instead of guessing from a page.
Guards every call
Authentication where needed, rate limits, input validation, and a log of who called what with which arguments. An assistant's mistake is contained to one bounded action.
Converts assistant traffic
A quote returned to an assistant carries a link a person can complete. The assistant did the finding. Your funnel still does the closing.
How it is built
The build, in order.
Every system here is scoped as one bounded workflow with a written success metric and a kill threshold agreed before the build. The steps below are the ones specific to this one.
Choose the first tools
The three or four questions an assistant is most likely to ask about you, read from the API you already have. Read tools first, always.
Build and publish the server
An MCP endpoint with a tool list, input schemas, rate limits and logs, plus the site-side surfaces that let assistants discover it.
Watch what gets called
The log of real assistant calls is the product research no specification gives you. Tools nobody calls are removed. Tools that get called with the wrong arguments get better schemas.
Add write actions behind a person
Bookings, quotes and tickets that create obligations are added last, with authentication and a human step for anything irreversible.
In production at Strataigize
We run this ourselves.
mcp.strataigize.com is our own public MCP server: an AI agent can query our services, pricing and case studies directly. This site also publishes llms.txt, a full-text index for retrieval systems, and a WebMCP surface. Running one is how we learned which tools an assistant actually calls, which is the part no specification tells you.
Governance
What a person still approves.
- Each tool is scoped to one bounded action, and anything irreversible sits behind a human step
- Every call is validated, rate-limited and logged with its caller and its arguments
- Tools can be withdrawn in minutes, and the server can require authentication per tool
The rest of the posture is published on trust and security: client-granted access you can revoke, credentials in managed stores, an AI governance summary aligned to the NIST AI Risk Management Framework, our subprocessors, and a DPA on request. We do not hold SOC 2 and we say so there.
Price
Published, not gated.
Pilot builds $25,000 to $60,000 fixed, production retainer from $8,000 per month. Every AI build starts with a $7,500 AI Opportunity Audit over two weeks that is credited toward the build if you proceed, and a pilot is one bounded workflow on your real data over 30 to 90 days with a written success metric and a kill threshold. A first server with a handful of read tools on an existing API is at the low end of the band.
The full price list is on the pricing page, and the cost drivers, with the questions that expose a padded quote, are in how much AI agents cost in 2026.
Questions
MCP servers and agent-callable surfaces, answered.
What is an MCP server?
Is it safe to let AI agents call our systems?
How long does MCP server development take?
Related
Where this connects.
Can an AI assistant buy from you? · Agent-led growth · AI agent development
The other systems in the catalogue: lead qualification agents, outbound sales agents, customer support agents, document processing agents, internal knowledge assistants, ad optimization systems, client reporting automation, content engines, computer vision systems, always-on operations agents.
Teams that hand us the number
Pilot builds $25,000 to $60,000 fixed, production retainer from $8,000 per month
Real ranges, published. Ad spend stays in your own accounts.
See the full pricing table →Where this fits: see the Strataigize Growth System, the ladder every engagement runs on.
Talk to the build team about mcp servers and agent-callable surfaces
Name the workflow you want it to run and we reply within 24 hours with whether it is a fit, the bounded scope we would propose, and the payback math.
Start with one workflow.
A free 30-minute audit call with the senior team names the workflow this system should run first and what it should cost, whether you build it with us or not.
Book your growth audit →Canadian and looking at funding? How the BDC LIFT program works. Public sector? How we build for government.
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