The Problem
Contact Us
InSplice AI
Aggregate, Augment, or Generate Data for LLMs.
Securely aggregate data from anywhere in real-time, automate labeling and metadata detection, and synthetically generate missing elements to train new AIs or for existing LLM reference.
Easy integration. No long-term data storage. Just a simple patch to do the work.
InSplice is a new kind of data patch to power the next generation of AI.
A data "patch" because you don't have to rip everything out to use it - start small and build what you need. Don't get trapped.
  • Data Explorer for versioned, AI-powered schema registry and data governance with an ability to learn metadata on its own.
  • Data Flows provide secure real-time streaming of any source connected to and from anywhere.
  • Functions for real-time data healing, completion, or even synthetic generation as a continuous part of your data topology and enterprise.
  • Bring your own databases and continuously write your realt-time, unified, healed, and generated data to them. Tokenize snapshots as needed.
Explore our clickable mock to see what we're building.
Note that not every page has been mocked yet - we're working on it!
How InSplice AI works
Here's one example out of many possibilities.
Colleen needs to aggregating customer success and help desk data to create a new LLM chat bot.
1
Connect Real-time Data
Colleen lets the AI handle the tedious metadata work by linking help desk and ticket sources via Flow Management & Taps.
2
Auto Detect Metadata
Colleen configures the Data Explorer AI to draft schemas based on the new data flow.
3
Clean Mapping & Govern the Sources
Colleen reviews the AI-drafted schemas, adjusts and tags them to establish a trusted metadata baseline. She then locks down the data flows to only allow the known schemas.
4
Clean & Generate Data
Using InSplice Functions, Colleen enriches incomplete tickets where data is missing and generates synthetic ones to boost data volume. AI drafts the functions for her to save time.
5
Aggregate
Through Data Connections, she aggregates the enriched data into a registered SQL database that evolves over time into a robust training dataset.
6
Tokenize
And in no time, through the Tokenizer, Colleen is ready to snapshot the SQL data, converting it into a consistent, tokenized format stored in a dedicated database for seamless AI training integration.
Why do you need a Data Patch?
The hurdles to continuous AI training and opportunities are stifling enterprise innovation.
You need a way to accelerate forward without devaluing existing investments and infrastructure - you need a data patch to bridge the opportunity.
Fragmented Data
Disparate data systems lead to disconnected datasets. Without governance, automation, and tokenization, raw data remains isolated and unstructured for AI training.
Time-Consuming
Manual data integration and cleaning is time-consuming. Automation can streamline these labor-intensive efforts.
Inconsistent Data
Inconsistent data undermines AI training. Transforming, enriching, or generating synthetic data can accelerate the process.
You keep running into walls because current options were built decades ago without AI in mind.
Custom Pipelines
Slow, expensive, difficult to extend, and constantly breaking.
ETL and iPaaS
Not designed for AI workflows - square peg in a round hole situation.
InSplice AI changes the game and empowers your data teams.
Governance
Automate and maintain domain-specific lineage, versioning, labeling/annotation, and tracking of all enterprise metadata. Leverage AI-driven schema generation or detection to automatically identify, propose, and refine standardized schemas, ensuring every data asset adheres to rigorous governance standards. Monitor data quality and mitigate detected anomalies easily from one place.
Real-time Data Flows
Securely stream and manage high-velocity data into and out of AI pipelines from any network or source with simple interfaces and as needed encryption to ensure models are always up to date.
AI-Assisted Transformation
Build, iterate, and deploy dynamic, real-code functions that normalize, enrich, and even generate synthetic training data. By healing incomplete datasets and augmenting data based on existing schemas and examples, this capability accelerates the creation of comprehensive, high-quality training assets.
Integration and Aggregation
Orchestrate the flow of data—whether raw, tokenized, or transformed via custom functions—by seamlessly aggregating to or from your own registered data stores and APIs. Define the data you need for the use cases that matter to your business and ensure your AI training pipeline has everything it needs in one place.
Tokenization
Empower your data pipeline with selective tokenization that lets you choose which unified data sets to refine for AI training. This capability transforms your raw data into a consistent, ready-to-use format—while keeping the original data accessible for further use.
So how does InSplice compare to what's available today?

InSplice vs. Snorkel AI

Broader Scope While Snorkel excels at data labeling and weak supervision, InSplice offers a comprehensive data pipeline that connects disparate sources, transforms data, and even generates new data. Real-Time Integration InSplice's focus on real-time integration addresses upstream data unification and preparation needs that complement Snorkel's labeling capabilities. End-to-End Solution InSplice provides a complete solution for AI data workflows, from ingestion to transformation to model feeding, while Snorkel primarily tackles labeling and dataset management.

InSplice vs. Tamr

Beyond Structured Data While Tamr focuses on structured data mastering, InSplice handles both structured and unstructured data for comprehensive AI training needs. Real-Time Capabilities InSplice offers real-time streaming and direct pipeline integration to ML models, going beyond Tamr's batch-oriented approach. Synthetic Data Augmentation InSplice uniquely provides synthetic data augmentation capabilities, creating a more complete solution for AI training dataset assembly.

InSplice vs. Reltio

AI-First Design While Reltio excels at creating trusted enterprise data, InSplice is specifically designed to feed AI/ML models with ready data in formats they can immediately consume. Domain-Specific Metadata InSplice supports domain-specific metadata tracking and schema inference for any dataset, going beyond Reltio's traditional MDM capabilities. Data Creation InSplice enables synthetic data creation, a capability outside Reltio's traditional MDM scope focused on cleansing existing data.

InSplice vs. Cloud-Native ML Data Prep

Cloud-Agnostic Unlike AWS SageMaker Data Wrangler or Google Cloud Dataprep, InSplice works across cloud environments. Persistent AI Data Bridge InSplice serves as an ongoing unified data hub rather than preparing single datasets for specific experiments. Comprehensive Governance InSplice offers selective tokenization and integrated governance for all incoming data. Continuous Data Flows InSplice emphasizes automation and continuous data flows versus one-off preparation jobs.

InSplice vs. Synthetic Data Platforms

While dedicated synthetic data generators excel at creating artificial data, InSplice delivers a comprehensive solution that integrates this capability within a complete end-to-end pipeline for AI-ready datasets. Synthetic Data Platforms (Gretel.ai, Tonic.ai, MOSTLY AI) Focused exclusively on data generation Typically operate within the constraints of single datasets Limited integration with existing data infrastructure Basic data lineage and governance capabilities InSplice Advantage Seamless end-to-end integration pipeline Combines synthetic creation with real data integration Enforces rigorous data quality standards across real-time flows and enterprise systems Connects and orchestrates data from disparate sources and formats InSplice unifies data from multiple sources, fills gaps with synthetic data, and delivers AI-ready datasets with full lineage tracking.

InSplice vs. Databricks

InSplice Lightweight specialist focused on AI data solutions without an all-or-nothing approach Purpose-built features specifically for AI data preparation AI-assisted schema alignment for efficient data integration In-transit real-time data augmentation and generation without centralizing or collection Databricks Comprehensive but complex data platform Requires significant expertise to operate Functions as a complete data lake solution Collects data, which is a heavy all-or-nothing solution Broader in scope but less specialized for AI data needs We can work together… InSplice provides a lightweight, specialized AI data solution with AI-assisted schema alignment and real-time data augmentation. Databricks offers a comprehensive but complex data platform requiring significant expertise. Organizations can use Databricks as a data lake and leverage InSplice to feed governed, prepared data in real-time.

Help bring InSplice AI to market
Pre-pay now and receive your first year free and 25% off year two once we deliver the MVP.
Sign up to be notified when our MVP is ready and secure a discounted first year.
* If early adopters do not receive access to the MVP within 24 months of pre-paying, they will receive a full refund.
Early adopters can request a full refund at any time up until access.
InSplice AI is a United Effects Venture Studios company
B2B SaaS Focus
We're passionate about building next-generation B2B SaaS startups that solve real-world problems.
Partnership Opportunity
Have a great idea for a new B2B SaaS startup? We want to work with amazing founders.
Get Started
Ready to bring your vision to life?
InSplice AI is a patented technology owned by United Effects, Inc.
© 2025 United Effects Ventures, LLC. All rights reserved.