AI, grounded in the right model.
ModelGround brings the best AI models together in one intelligent workspace.
One place. Multiple models. Smarter results.
Why ModelGround exists.
Model Fragment Chaos
Over 40+ leading LLMs and specialized tools exist today. Some excel at 200k-token document parsing, others at AST code refactoring, image typography, or complex mathematical proofs. Nobody has time to benchmark them before every question.
The Manual Switcher Trap
Existing multi-model products dump a dropdown list of 20 LLM names in front of you. But forced manual selection turns you into a air-traffic controller for prompts instead of giving you the best answer.
Context Erasure Across Handoffs
Switching from a coding model to a document reader usually resets your thread memory, forcing you to copy-paste your instructions over and over.
How we think about intelligence.
We believe the future of AI isn't one monolithic super-model that attempts to do everything moderately well. The future belongs to coordinated multi-agent orchestration.
Deterministic Orchestration
Task classification, latency benchmarks, and cost efficiency dictate model choice, not static marketing contracts.
Radical Transparency
Every output comes with explicit model attribution and rationale. We show our working so you can verify and stay in control.
Context Continuity
Your workspace state, code references, and academic preferences persist across engine handoffs without memory wipes.
Native Surface Integration
AI should live where work happens: in one browser workspace that keeps your models, tools, files, and context together.
The foundation of ModelGround.
We are building the quiet orchestration layer between human intent and the expanding frontier of artificial intelligence.
A future where intelligence is always within reach.
We envision a world where individual creators, researchers, and enterprises command the collective power of all frontier models through a single, frictionless surface, without cognitive fatigue, fragmented subscriptions, or manual switching.
To create the ground where AI models, assistants, and people build what’s next.
ModelGround unifies 40+ leading LLMs, assistants, and persistent memory into one quiet, self-routing intelligence plane. We route every prompt deterministically to its optimal engine, preserving your context across every task.
Built with intention.
F16 Founder's Note · From Nandita
hey everyone,
we started building modelground because we got tired of having 6 browser tabs open just to solve one assignment. chatgpt was writing generic intros, claude had the long document locked in, perplexity was hunting citations, and cursor was fixing the react state.
everyone in AI right now has access to the exact same frontier models. the bottleneck isn't which LLM exists. it's knowing which one to use when, without burning 20 minutes tab-switching.
modelground is early. it routes your prompts, tells you why it picked a model, and keeps your work together in one browser workspace. if something breaks or a routing decision feels wrong, message me directly. we read every single piece of feedback.
nandita doloi
founder, modelground.ai
Where we are heading next.
Where your work actually goes.
One task can pass through multiple models before it comes back to you. Here is how ModelGround handles your work, what model providers see, and what stays in your control.
What ModelGround does
Splits the task, then puts it back together
Each step goes to the model that handles that kind of work best. You get one finished answer, not four to reconcile yourself.
one task, several stepsHands over only what a step needs
A model receives the input for its own step. Not your chat history, not every file you've uploaded, not the other steps' output unless the work requires it.
scoped handoffNames every model that touched it
Open the trace on any result to see which models ran, in what order, and why each one was chosen for its step.
full run traceClears the work once it's delivered
Your prompt and the intermediate drafts are purged from ModelGround after the answer reaches you. The trace labels stay so you can audit the run; the content doesn't.
zero retention
What the model providers do
Run your step under zero-training terms
OpenAI, Anthropic, Google and Perplexity are called through API endpoints that exclude your input from model training.
no training on your workSee one step, never the whole task
Because the work is split, no single provider receives your complete task, your full file set, or every other model's output.
partial visibilityKeep short logs for abuse monitoring
Providers hold request metadata for up to 30 days under their own API terms. That part is theirs, not ours, and we link their policies.
governed by api terms
What is on you
Deciding what to send
Splitting a task across models doesn't change what a document contains. If it shouldn't leave your laptop, don't submit it.
your judgmentChecking the route when it matters
If a step went to the wrong model, override it and rerun from that step. You keep everything the earlier steps produced.
rerun from any stepKeeps your work in one place
Your tasks, files, route details and results stay together in the browser workspace attached to your account.
browser workspace
Ready to experience intelligent routing?
Start using ModelGround AI today for free directly in your browser.