AI & MCP services

AI agent development that does the work, inside your workflows.

AI integration services, MCP server development, and AI agents built on Claude, connected to the browsers, catalogs, documents, and data your team already works in every day.

Talk Through Your Workflow See AI Work

<10 min

Down from 2–3 hrs a day

MCP tools in one server

70%

Less document review time

Featured · Agentic trading marketplace

A 2–3 hour daily workflow, now under 10 minutes.

A collectibles dealer trades on a B2B marketplace portal with no public API, so every price check, offer, listing, and inbox message was done by hand. We built a REST API and an MCP server over the portal, so both scripts and Claude can work the market in plain English: search products, read the order book, compare prices, review offers, and manage listings.

Claude can act, not just read, with guardrails:

Safe-mode previewsConfirm before any live writeViewer / editor / admin rolesFull audit logSlack inbox digest

29MCP tools

5,000+Products

36Categories

<1sCache response

Node.js · TypeScript · Express · Puppeteer · PostgreSQL · MCP SDK · Claude API · GCP

Daily dealer workflow

Before: manual2–3 hrs

After: natural-language, via MCP<10 min

For the same client · Supplier data pipeline

Two supplier catalogs, watched around the clock.

The same client buys wholesale from two suppliers whose prices only exist behind a logged-in session: no API, no feed, no change alerts. Staff were checking pages by hand and missing price moves, new drops, and delistings.

We built a pipeline that scrapes both catalogs on a schedule, compares every product against the last run, and writes up what changed in plain English. A Slack digest goes out after each run, and a typed REST API feeds the catalog through n8n into NetSuite.

~11,000

SKUs tracked

~8 min

Per steady-state run

1 day

To add a second supplier

Node.js · TypeScript · Fastify · Zod · Drizzle · PostgreSQL · n8n · NetSuite · Slack

How the pipeline runs

2 supplier portalsLogged-in · no API

Scheduled scrapeNew & changed items first

Diff against the databaseAdded · changed · removed

Slack digestPlain-English changes

REST API → n8nInto NetSuite

More AI work

More AI in production.

AI SaaS

AI writing tools, one subscription

AI Academic-Writing Platform

A subscription SaaS for college students. Five AI providers sit behind one API and a single metered word quota, with Stripe billing across three tiers and a study helper that turns PDF, Word, and PowerPoint files into summaries and quizzes.

React · Django · PostgreSQL · Stripe · Claude · OpenAI

Document AI

70%

Less document review time

AI Document Processing

AI-assisted document processing that took most of the manual reading out of a client's review workflow.

Construction reporting

Reporting areas summarized by AI

AI Layer for Procore Reporting

Claude and GPT write report summaries and answer natural-language questions against Procore data: overdue tasks, close rates, site observations, employee ranking, and project performance.

See the construction work →

Also delivered: an MCP server product, built for a separate client through to beta.

Capabilities

AI integration services, from MCP servers to Claude.

MCP & agents

Browser & data automation

AI in the workflow

How we work

Our AI agent development process.

01

Map the workflow

We start with the manual work: who does it, how long it takes, and which systems are involved, so there is a baseline to measure against.
02

Decide what the agent may do

Read-only first. Any action that writes to a live system gets guardrails: a safe mode that previews changes, an explicit confirm step, and role-based access.
03

Build the tools

We build an MCP server or agent layer on the Claude API, exposing your systems as tools the model can use, with browser automation where a system has no public API.
04

Test with real data

Every request is recorded in an audit log, so we can see exactly what the agent did while it is tested on real work.
05

Launch & measure

We compare against the baseline. For a B2B marketplace client, a 2–3 hour daily workflow now takes under 10 minutes.

Want to estimate the savings first? Try our free MCP automation savings calculator.

FAQ

Questions about AI agent development services.

What is an MCP server, and why would we need one?

An MCP (Model Context Protocol) server exposes your systems, such as inventory, orders, or documents, as tools an AI assistant like Claude can use. It lets people work those systems in plain English instead of clicking through them by hand.

Is it safe to let an AI agent change data in our systems?

It can be, with the right guardrails. Our builds use a safe mode that runs actions without committing them, an explicit confirm step before any live write, role-based access, and a full audit log of every request.

Can an AI agent work with a system that has no API?

Yes. For a B2B marketplace portal with no public API, we built a REST API and MCP server on top of it using browser automation, so both scripts and Claude can operate it.

Which AI models do you work with?

Mostly the Claude API, and also GPT and other providers where they fit. One SaaS product we built routes requests to five AI providers behind a single API and quota system.

What results have AI projects delivered?

A 2–3 hour daily dealer workflow cut to under 10 minutes, and document review time cut by 70% with AI-assisted document processing.

How long does an AI agent project take, and what does it cost?

It depends on how many systems the agent needs to use and whether it only reads data or also takes actions. We scope it after mapping the workflow, so the estimate is based on your actual process.