itsbydavid
    AI agents & MCP

    Let AI query your business directly.

    Your data lives in a database an AI cannot reach. I build the layer that changes that — a clean API, an MCP server, and agents that can actually do something with it.

    • Shipped one in production
    • Fixed scope
    • Published pricing
    • One person
    Book a 20-min call

    The problem

    An AI can only use what it can reach.

    Most business data is one step away from being useful to a model, and that step is architectural.

    Your data is locked in prose

    Prices, availability, coverage and inventory live in page copy, PDFs and images. A model can read a sentence about your pricing. It cannot filter, compare or compute with one.

    Every integration is bespoke

    Wiring your data into each assistant separately means rebuilding the same connector repeatedly. One standard interface replaces all of them.

    Agents that answer but cannot act

    A chatbot that describes what it would do is a demo. Booking, updating a record or filing the ticket is where the value is, and it needs real permissions and guardrails.

    What I actually do

    What I build

    Agent-readable APIs

    A clean, documented, read-optimised interface over the data you already have — the foundation everything else sits on.

    MCP servers

    The emerging standard for exposing tools and data to AI clients. Build it once; Claude, Cursor, Codex and anything else MCP-compatible can use it.

    Retrieval over your knowledge base

    So answers come from your documents, with citations, instead of a model guessing plausibly.

    Agents that take real actions

    Scoped permissions, audit trails and human checkpoints where the action is consequential.

    Evaluation and guardrails

    The unglamorous part that decides whether a prototype survives contact with real users. This is what I do at TELUS by day.

    The data layer underneath

    Often the real work: turning scattered, inconsistent sources into one structured store worth querying.

    Proof

    I have already shipped one.

    Parcela Nova scores all 340 municipalities in Guatemala from 12+ official sources, 10+ global datasets and 5 purpose-built tools — and exposes the result through an open, keyless, read-only MCP server with eight tools any AI client can call.

    Point Claude or Cursor at it and ask about buildable land, hazard exposure or drive time to the capital, and it answers from verified data rather than guessing. Before it existed, that information lived in public registries, municipal offices, spreadsheets and brokers' memory.

    Read how it was built

    Pricing

    What it costs

    Published rates. No discovery call required to find out what things cost.

    Single System Build

    $8,000–$15,000

    4–6 weeks

    One workflow, agent, or visibility fix, built and live.

    AI-Legible Platform

    $25,000–$60,000

    8–16 weeks

    Structured data layer, agent-readable API, MCP server, and the site on top.

    See the full rate card

    Questions

    Common questions

    Could an AI answer questions about your business?

    Tell me what data you hold and what you'd want an agent to do with it. I'll tell you whether it's a weekend or a quarter.

    Book a 20-min call