AI-driven connector builder with human approval
Describe the source you need in plain language and AI drafts the connector. People approve the scope, schema and rules first, and every connector, transformation and output is versioned and traceable.
The challenge
Every new source system needs a connector, and each one is built by hand: reading documentation, discovering the schema, writing validation and transformation logic, then packaging it for deployment. Different developers take different approaches, so connectors are slow to build and hard to standardize or review.
Handing the job to AI removes some of the effort but adds a worry. If a model decides what to extract and how to reshape it, who confirmed that scope was right? Without a human approval step and a clear record, teams cannot trust what the connector pulls in.
What it does
Natural language creation
A user describes the source and what they need in plain language. The system drafts the connector from that request, so the starting point is a reviewable proposal and not a blank code file.
Source and schema discovery
Source structure is discovered automatically and a schema is proposed, covering API, database and file based sources, so reviewers start from a described inventory instead of guesswork.
Human approval gates
A person approves the extraction scope, the schema and the processing rules before anything is built or run. Nothing moves forward on the strength of an AI suggestion alone.
Validation and transformation rules
Pre-processing validations catch bad records on the way in, and post-processing transformations shape the output. Rules are defined in clear terms so reviewers can read what will happen.
Inline testing
Connectors are exercised against mock or sample data before they touch a live source, so mapping errors and broken rules surface early in a safe place.
Versioned, traceable artifacts
The connector, its transformations and its outputs are generated as artifacts with central metadata and version history, so any result can be traced back to what produced it.
How it works
Describe the connector
A user states the source and the goal in plain language. The system drafts a connector plan and proposes the extraction scope for review.
Review and approve
Analysts check the discovered schema, the scope and the rules. They edit, approve or reject each one before any build step starts.
Test with sample data
The connector runs against mock or sample data. Validation and transformation results are inspected and fixed before a live source is involved.
Generate and register
Approved settings become deployable artifacts, stored with metadata and a version, ready to be released under your own process.
Under the hood
- Source types
- REST APIs
- Databases
- File based sources
- SaaS platforms
- Rules and data
- Schema discovery
- Validation rules
- Transformation expressions
- JSON
- Code and delivery
- Generated code packages
- Version control
- Central metadata store
- AI layer
- Any chosen model
- Template based generation
- Sample data testing
Inside the harness
Policy
Approval requirements decide who may sign off scope, schema and rules, and a connector cannot be built or run until the required approvals are recorded.
Guardrails
Validation rules and sample data checks stop bad records and malformed mappings before they reach the target, and AI output is treated as a proposal.
Security
Source credentials are held securely and kept out of generated artifacts, and each connector reaches only the sources it was approved for.
Audit
Approvals, rule changes, generation runs and versions are recorded, so you can show who approved what and which version produced an output.
Business outcome
Connectors built more consistently. One guided framework replaces many individual approaches, so connectors share the same structure, checks and documentation and are easier to review and maintain.
People stay in control. Scope, schema and rules are approved by named reviewers before work runs, so AI speeds up drafting without taking over decisions about what data is pulled.
Better data quality at the source. Validation and transformation rules apply on the way in, so downstream systems and models receive structured, checked data instead of raw extracts.
Every output can be traced. Versioned artifacts and recorded approvals mean any connector or output can be explained later, which supports internal review and audit conversations.
Where it is used
- Onboarding a new SaaS platform to a reporting store by discovering its endpoints and generating the connector.
- Extracting selected tables from an operational database, with reviewers approving exactly which columns leave the system.
- Turning a recurring file export from a partner into a validated, mapped feed with sample data tests.
- Standardizing a backlog of hand-written connectors by regenerating them under one reviewed, versioned framework.
Frequently asked questions
Does AI build connectors without anyone checking?
No. AI drafts the connector, but people approve the extraction scope, schema and processing rules before anything is built or run. The approvals are recorded alongside the connector and its versions.
Which kinds of sources can it connect to?
API, database and file based sources are covered, including SaaS platforms. Your team confirms the exact list for your estate during scoping, since each source type has its own discovery and testing needs.
Do we need a specific AI model?
No. The AI steps are model agnostic. You choose the provider or open-weight model that fits your policy, and it can be changed without redesigning the connector framework.
How is this different from workflow automation?
Workflow automation chains AI steps and systems into end-to-end flows. This use case focuses on the connector itself: describing it, approving its scope and schema, testing it and versioning the artifacts.
See it on your own documents and data
Tell us what you want to automate and we will show you how it runs inside the harness.