AI

GPT Apps with MCP

We build GPT-powered apps end-to-end and connect them to real business systems via MCP — securely, reliably, and fast.

The key is not “chat”. The key is an app that can safely use your existing backend: APIs, databases, internal services, CRM/ERP, and workflows — with proper permissions, guardrails, and observability.

Turnkey delivery

Product discovery, architecture, UI/UX, implementation, deployment, and ongoing iterations — shipped as a real product, not a demo.

MCP integration

We implement MCP servers/tools around your systems and make them usable by GPT apps: clean contracts, consistent schemas, versioning, and safe execution.

Adapt your existing backend

We don’t rewrite your business. We wrap and adapt what you already have (REST/GraphQL, DBs, queues, internal services) into reliable capabilities the AI can use.

Security & guardrails

Permissions, data access rules, redaction, rate limits, and safe tool execution. We design the system so failures are controlled and auditable.

Quality & observability

Logging, traces, evaluations, feedback loops, and cost controls — so you can improve outcomes and keep the system predictable.

Engagement format

A simple packaging so decision-makers can evaluate risks and move fast.

Deliverables: scope + roadmap, MCP tool layer, app UX, guardrails, observability, handover docs
Typical timeline: 2–6 weeks for MVP (data + permissions dependent)
Communication: weekly demo + async updates (Slack/Telegram/Email)
s01

How we build

01

Discovery & scope

Identify high-leverage workflows, map data sources, define permissions, and build a realistic roadmap.

02

MCP tool layer

Wrap your backend into MCP tools with schemas and safe execution, then validate on real scenarios.

03

App & UX

Build the product experience: UI, conversation flows, actions, approvals, and error states.

04

Launch & iterate

Ship to real users, measure outcomes, add guardrails, and improve quality with feedback and evaluations.