⚖ Patent Pending Technology
Operated by Claude Code · 42 skills · 138 MCP tools

Tell Claude Code to build the integration.
It ships to production.

42 skills and 138 MCP tools cover the full lifecycle of deep, productized integrations with any API: build a custom connector, compose multi-step automations, run branch-coverage tests, deploy across tenants, monitor production, support customers, upgrade. End to end.

APIANT Partners
What APIANT is

APIANT is the platform. Claude Code is the operator.

For 10+ years, builder teams have used APIANT to ship turnkey integration products with any API: deep custom connectors, multi-step automations, multi-tenant deploys, production monitoring, customer support, version upgrades. Not Zapier-style 2-step recipes. Real platform depth. Real product. Now driven end-to-end by Claude Code.

Custom connectors
Build a connector for any API. OAuth, API key, webhooks, dynamic field discovery, rate-limit throttling, retries.
AI-operable
Deep automations
Multi-step orchestrations with branching, mapping, transforms, scheduled triggers, branch-coverage tests.
AI-operable
Multi-tenant deploy
Push the same automation to one customer or hundreds. Versioning, rollback, linked accounts, tenant isolation.
AI-operable
Monitor, support, upgrade
Alerts, webhook replay, SQL diagnostics, customer keyvault, version diffs, rollbacks. Run integrations as products in production.
AI-operable
Every tool a human integration engineer used to build, test, deploy, monitor, support, and upgrade integrations on APIANT, across any API, at scale, is now fully operable end-to-end by Claude Code. That's the change.
Any API. Any depth. Any scale. End-to-end from Claude Code.
Guardrails, not vibes

Same AI, two outcomes. Guardrails are the difference.

Claude Code can write integration code. That was never the question. The question is whether you can trust what it wrote once your customer data flows through it. And that depends entirely on what the AI is operating.

Dim tangled wireframe threads sprawling with no structure, representing free-form AI-generated integration code
Free form: the AI wanders

Unconstrained code generation picks a plausible solution with no ground truth. The AI cannot tell right from wrong integration behavior on its own, and it emits more code than any team can put eyeballs on, so correctness surfaces late, as failures in production data. We build with frontier models every day. Free-form integration code still surfaces severe data-handling defects weeks in, and each fix means re-scrubbing everything already built.

A single emerald light beam traveling a straight guided rail toward a glowing crystal, representing AI operating APIANT along a constrained path
On APIANT: the AI walks a path

The same frontier models, operating APIANT through MCP. Intent passes through a constrained schema, the deterministic compiler refuses invalid structures before they exist, and execution replay proves behavior on real data before deploy. Hallucinations have nowhere to land, whether a field mapping, a structure, or a piece of logic: months of daily production builds with none observed reaching production. The platform turns the model's fluency into working integrations.

Building blocks, not black boxes.

On APIANT the AI does not emit a pile of one-off code you then own forever. It assembles integrations from proven platform building blocks: connectors, mappings, subroutines, tests, deploys. Everything is stored as structured data you can read in a visual editor. Ask the AI for something structurally invalid and the compiler refuses it before it exists.

Logic you can see
Every flow is structured data in a visual editor. You verify it at a glance, not by reading code line by line.
Proven before production
Branch-coverage tests and execution replay prove behavior on real data before anything reaches your customers.
Watched for years
Full run history on every execution. API drift surfaces as alerts with replay and rollback, not 2am support tickets.
Not our claim alone. The research says the same thing
45%
of AI-generated code samples failed security tests, and results stayed flat regardless of model size or sophistication. Better models do not fix this. Guardrails do.
205,474
unique nonexistent packages hallucinated across 16 code-generation models. Hallucinated dependencies are exactly how free-form integration code goes wrong.
2,500%
projected increase in software defects by 2028 from prompt-to-app building without a governance layer, triggering what Gartner calls a quality and reliability crisis.
Gartner, Predicts 2026 (Dec 2025)
"Without robust control systems... an increase in change volume leads to instability."Google DORA, 2025 State of AI-assisted Software Development

None of this is an argument against AI. It is the argument for guardrails: the same frontier models produce reliable integrations when the platform constrains, compiles, and proves their work.

Claude Code is the operator. APIANT is the architecture.

APIANT is the guardrailed foundation layer for AI-built integrations: the AI does the building, the platform makes its work trustworthy. The speed of AI with the reliability of a platform that has run production integrations for over a decade. AI-built, platform-proven.

Open-ended by design

No fixed catalog. Full endpoint depth on any API.

A prebuilt-app catalog stops at the apps and endpoints someone already decided to add. APIANT has no catalog ceiling: point Claude Code at the API docs and it builds a connector exposing every trigger and action that API actually offers, including the private and partner endpoints catalogs never list.

8,000+ apps
Their catalog (endpoints unlisted)
Any App
AI reads the API docs and builds it
Any Endpoint
Every trigger and action the API offers
The architecture PATENT PENDING

Integration logic stored as structured data.

APIANT stores every integration as structured data, not compiled code. That is what makes the whole platform AI-operable: Claude Code edits a single node in a data tree, not a line in a source file.

Surgical Precision

AI edits a single node in a data tree. Not a line in a file. No risk of breaking adjacent logic.

Replay-Based Testing

Feed the exact production input through the exact path. See every decision the integration made.

Context Efficiency

Structured data is compact. AI can hold an entire complex integration in context, not just fragments.

Built-In Transparency

Every integration is fully readable. No compiled code, no hidden state. What you see is what runs.

Why APIANT

Claude Code CLI + APIANT vs. the alternatives

AI can write integration code. But code without a platform is a maintenance nightmare. Here is why the combination matters.

AI alone (no platform)
Writes code. You handle everything else.
  • You host, monitor, and maintain every script
  • No auth management, rate limiting, or retry logic
  • No visual editor to inspect or modify flows
  • No white-label or multi-tenant support
  • Breaks silently when APIs change
Other platforms
Pre-built connectors. Shallow by design.
  • Limited to field-level mapping, no business logic
  • AI "assists" but doesn't build autonomously
  • Shared infrastructure, not dedicated
  • Per-task pricing gets expensive at scale
  • No white-label; your customers see their brand
PATENT PENDING
Claude Code + APIANT
AI builds it. The platform runs it.
  • AI reads API docs, builds and tests connectors autonomously
  • Production-grade: auth, rate limiting, retries, monitoring
  • Visual editors to view, modify, and deploy what AI built
  • Your own dedicated server, white-labeled
  • Deep integrations with business logic, not just field mapping
Who uses this infrastructure

One platform. Your AI runs it. Built for how you sell.

Customer case study
Built with APIANT's CRMConnect product

How Exercise Coach syncs 232 locations to HubSpot

232 locations. One platform. Zero errors.

Exercise Coach uses APIANT's CRMConnect to sync 232 Mindbody locations to a single HubSpot instance. Bi-directional sync, 120+ fields per contact. One codebase serves all 232 locations, each with its own time zones, feature toggles, and configurations. When APIANT upgrades the product, every location gets it simultaneously.

A master account routes all CRM data to the correct Mindbody site. Rate limiting holds at 185 API calls per 10 seconds, calibrated to never exceed CRM limits even when every location renews memberships on January 1st. The Splunk dashboard proves it: zero errors, zero rate limit violations. New locations go live in hours, not weeks.

Network diagram showing Master Account hub connected to 232 location nodes feeding into HubSpot with rate management
232
Connected locations
120+
Fields synced per contact
185/10s
API calls managed per 10 sec
0
HubSpot rate limit errors
Trusted by builders

What builders say

"This is my bet on who wins the API economy. Incredibly flexible and easy to understand."

JE
Jose E.
CTO

"Seamless integrations. Has been instrumental in integrating our data factory across more than 200 locations."

BB
Brad B.
COO

"The preeminent integration and automation: everything you could ever need."

JN
John N.
President

"If you're looking to deliver exceptional results and drive innovation, look no further."

DS
Dave S.
CTO

"Powerful automation, bespoke solutions."

EG
Eric G.
Head of Marketing

"An amazing partner. Has allowed us to develop a comprehensive integration solution."

AP
Angie P.
Director of Technology

"APIANT has proven to be an invaluable asset for our organization. Robust integration solutions."

CK
Corinne K.
Web Master

"Professional team and excellent experience. A company that will work with you and for you."

DS
Debra S.
Senior Director of Operations

"Longtime customer and a huge fan. Absolutely crucial for my business."

CR
Cody R.
Founder

"Seamless implementation and expert support. Reliable and effective automation solutions."

JH
Jake H.
Project Manager

"Great partners! We've been partnering with Apiant since 2019."

LC
Laurie C.
CMO

"Great partnership. We are very happy to continue working together with Apiant!"

CB
Christophe B.
Donor Relations Manager
What just changed

The integration cost curve breaks here.

Same connector. Same automation. Same production target. Different operator.

Before · Human team
6 weeks
1 senior integration engineer
Read the docs. Build the connector. Map fields. Write tests. Deploy. Document. Babysit production.
After · Claude Code on APIANT
1 afternoon
1 PM with a clear spec
Claude reads the docs, builds the connector, composes the automation, runs branch tests, deploys, watches it run.
What you do with the engineers you got back
Stop building plumbing. Build product.
Your senior engineers stop being a connector factory. The platform does that. They go solve the problems your customers actually pay for.
What you actually own

Your own iPaaS. Not a seat on someone else's.

Every paid tier ships on a dedicated server, managed by us, owned by you. Pro, Scale, Enterprise: no multi-tenancy, no commingled data, no neighbors.

The rest of the category

Multi-tenant SaaS

shared backend 7 tenants · 1 server
You're a tenant. Throughput, rate limits, and incidents shared with everyone else on the box.
vs
APIANT

Your dedicated server

your backend 1 customer · 1 server · yours
Your data plane. Your rate limits. Your throughput. White-labeled to your brand.
Your data plane is yours
Customer records, credentials, logs, queues. One tenant on the box, and it's you.
Rate limits you control
Your throughput isn't divided by whoever else signed up this morning.
Noisy-neighbor immunity
A bad actor on another customer's server can't slow your syncs or trip your circuit breakers.
White-label by default
Your brand on the UI, your domain on the endpoints. We're the engine, you're the product.
The rest of the category sells you a tenant account. We hand you the platform.
Install the plugin in 60 seconds

Hand Claude Code the keys to your integration platform.

If you can describe the integration in a sentence, the platform will ship it. If you cannot, talk to us. We will help you scope it.