Turn Claude Desktop
and ChatGPT into
your central intelligence hub.
Stop jumping across fifty admin screens. Ask your AI in plain language — it pulls the data, runs the report, drafts the email, flags the anomaly. Your tools, operated by your AI, on your terms.
Just say it.
Your AI handles the rest.
These aren't hypothetical. They're the kind of prompts a business owner can type into Claude Desktop or ChatGPT once we've wired your tools up. No SQL. No screenshots. No three open tabs.
Ask questions
Pull data from across your stack without opening a single dashboard.
Get things done
Take action across your tools — with confirmation gates on anything risky.
Check on things
Catch problems before they catch you.
Five tabs.
Two dashboards.
Forty-seven clicks.
Or one sentence.
- 01Open the admin panel
- 02Find "Reports" in the sidebar
- 03Pick "Sales", set range to last month
- 04Wait for the chart to render
- 05Notice it's missing the channel breakdown
- 06Switch to the Marketing dashboard
- 07Filter by the same date range
- 08Export both to CSV
- 09Open in Excel, add a delta column
- 10Screenshot, attach, email the team
Talk directly
to your company data.
Manual dashboards are accurate. They're also slow, hard to read, and stressful to navigate after the third meeting of the day. Let your AI obliterate that stress for good — your data, conversational.
- — Built for "every possible question someone might ask"
- — Three months out of date with what teams actually need
- — Numbers without context — you go ask the AI anyway
- — Add a new metric? File a ticket. Wait two weeks.
- — Built for "this exact question, right now"
- — Always current — pulled live the moment you ask
- — The AI shows its work — you see what it queried
- — New metric? Just ask differently.
Focus on your company.
Let your team do the same.
Your employees stop being human dashboard-navigators. The hours they used to spend extracting, copying, formatting, and re-formatting numbers go somewhere that actually compounds.
Improve client relationships
Customer-facing teams stop being report-fetchers. They become the warm, human layer your AI can't be.
Solve urgent problems faster
When something breaks, your team is already on the ground — not waiting for a dashboard to render or a query to finish.
Promote, design, ship
Marketing has time to write and post. Product has time to build. Hours that used to extract numbers go to actually creating things.
Compound the AI itself
Better internal docs and guidelines feed straight back into smarter AI answers. Every hour your team writes is an hour every employee borrows back.
"Won't the AI
wipe my database?"
The honest answer: it can't. Not because we trust the AI — but because we don't, and we built the system that way from day one.
The AI never touches your database directly.
It calls a small, fixed set of tools we wrote. Each tool does one thing. Anything outside that set is invisible to the AI — it can't even ask for it.
Risky actions require explicit confirmation.
Mass deletes, sending emails, charging customers, changing permissions — these never run silently. The AI proposes; a human approves.
Read by default. Write by exception.
Most tools we build are read-only. Write tools exist where they earn their keep, scoped narrowly to the records the request is actually about.
Every call is logged, with the receipt.
Caller identity, parameters, outcome. If something looks wrong tomorrow, you have evidence — not a vague "maybe the AI did it."
You own every line we write.
Each tool ships in your private repository. Your team can read it, audit it, extend it, replace it. There's no black box — there's just code, written carefully.
You don't trust "the AI." You trust the small set of tools we exposed to it — every one of them written by us, reviewed line by line, and owned by you. The AI is an interface. The system is yours.
Model Context Protocol
servers, built like
infrastructure.
Most public MCP servers are 200-line scripts that wrap an API and call it production. We build the version that holds up when an autonomous agent invokes it at 3 a.m. — authenticated, audit-logged, refactor-safe.
- → zero-knowledge / privacy-preserving servers
- → flutter test & build orchestration
- → clean architecture / DDD-disciplined node.js
The agent never sees
the plaintext.
Field-level encryption, per-user keys, deterministic identifiers. The server returns derived signals — never raw values. The same zero-knowledge architecture we ship in our Flutter clients, applied to agent tooling.
read · zero-knowledge in flutter{
"patientRef": "vk_8f3aa1...",
"riskBucket": "elevated",
"lastSync": "2026-04-26T11:02Z"
}<encrypted blob — AES-256-GCM> key custody: client device only server view: ciphertext + index hash
Native tooling for
the Flutter pipeline.
Give an AI assistant first-class control over your Flutter project: run tests, parse golden-test diffs, trigger flavored builds, inspect pubspec drift. Built by a studio that ships Flutter every week — not a generic connector wrapped around a CLI.
Layered. Hexagonal.
Refactor-safe.
Most MCP servers collapse transport, business logic, and side effects into a single file. Ours separate them — domain at the centre, adapters at the edges, transport pluggable. Add a tool by writing one use case. Swap stdio for SSE without touching anything important.
// touching domain logic. add a tool by writing one use case.
The baseline every server we ship meets — by default, not on request.
OAuth 2.1 + PKCE Remote Servers
Remote MCP servers your team can actually trust. OAuth 2.1 with PKCE, scoped permissions, rate limiting at the server boundary — not the unauthenticated demos that fill the public registry.
Audit-Trail-First Tools
Every tool call logged with caller identity, parameters, and outcome. Human-in-the-loop confirmation for destructive operations. Logs your compliance team can read without grepping JSON.
Domain-Specific Connectors
Generic Postgres and Slack connectors are everywhere. Domain-shaped servers — for invoicing, content workflows, internal CRM — are missing. We build the one that maps to your business, not your database schema.
We don't hold SOC2 or HIPAA certifications. We build servers whose architecture supports those regimes — encryption at rest and in transit, scoped access, tamper-evident audit logs, data minimisation by design. Your compliance partner certifies the system; we write the system.
Our public open-source presence is being rebuilt. Until it returns, every server we ship lives in your private repository, written to the same standard we'd publish — Clean Architecture, full test coverage, no shortcuts.
Frequently Asked Questions
MCP-specific questions — security, scope, pricing, what we will and won't claim.
Model Context Protocol is the standard way to give an AI agent (Claude, Cursor, ChatGPT, internal agents) controlled access to your tools, data, and APIs. If you're planning to put an LLM in front of any internal system, you need MCP servers. The question isn't whether to build them — it's whether yours will be a 200-line demo or a piece of infrastructure your team can rely on.
Tell us the system
you want exposed.
Describe the tools, the constraints, the security regime. We come back with a fixed quote, a scope, and the shape of the architecture — usually within 48 hours. Scoping call is free.
- ✓Fixed quote · no hourly billing
- ✓Source code · in your private repository
- ✓Test suite · every tool, every path
- ✓Audit logs · structured and queryable
- ✓Auth wired · OAuth 2.1 / PKCE / scoped tokens
- ✓Architecture doc · so your team can extend it without us
- ✓30-day post-ship support · for production-shaped issues
// your code lives in your repo from commit one.