Multi-model AI infrastructure for engineering education

One API.
Every frontier model.
Built for engineers.

AIIA Lab is an OpenAI-compatible gateway to the full frontier model landscape — so undergraduate and engineering programs can run each task on the model that is genuinely strongest at it. Less time wiring up vendors. Far more time building, testing and shipping.

0
frontier models, one key
99.9%
uptime target for lab hours
0
across every provider
0 code rewrite
plug into existing tools
thermal_study.py
# One client. Every model. Swap with a single string.
from openai import OpenAI

aiia = OpenAI(
    base_url="https://api.aiialab.org/v1",
    api_key="sk-aiia-***",
)

# Different disciplines, different strengths — pick the best fit
RESEARCH = aiia.chat.completions.create(
    model="claude-3-7-sonnet",      # long-form reasoning
    messages=[{"role": "user", "content": "Review this CFD setup"}],
)

CODE = aiia.chat.completions.create(
    model="deepseek-v3",            # code generation
    messages=[{"role": "user", "content": "Write a ROS2 publisher node"}],
)
Cross-model fallbackFailover in under 300 ms
Per-course meteringSpend by lab, team, model

Every frontier model · one integration · one bill

Two things we do differently

Not a wrapper. A model strategy for engineering programs.

Most teams settle for whichever model they signed up for first. AIIA Lab is built around two convictions: every model is worth having, and engineering education deserves a stack designed for it.

01 / BREADTH

Every model, deliberately routed

We keep the widest practical catalogue of frontier models available through one endpoint — then map each discipline to the models that actually win at it.

  • Cross-vendor benchmarks per task type, not per marketing page
  • Compare two or three models on the same prompt in one call
  • New models ship to your endpoint the week they launch
  • Automatic failover when a provider degrades or rate-limits
02 / DEPTH

Built for engineering curricula

Designed around how engineers actually learn and work: lab reports, CAD and simulation, embedded code, capstones and the first ninety days on the job.

  • Course-level quotas and spend controls for departments
  • Lab-ready reference stacks: simulation, data, CAD, embedded
  • Skill-tree onboarding from first principles to shipped project
  • Reference architectures staff can teach without rewriting each term
Model routing

Right model, right discipline

Different models are strong in different places. AIIA Lab ships with discipline-tuned routing profiles your faculty can edit — so students get the best available model for the task in front of them, not the only one you bought.

Routing profiles are editable per course. Availability tracks upstream releases — new models appear in the same list automatically.

Where it lands in the program

From first lab to first job

Undergraduate coursework, capstone projects, research groups and industry onboarding — one platform that scales with a student's competence instead of blocking on it.

Core engineering coursework

Thermo, controls, circuits, structures — a tutor that reads the actual problem set.

Step-by-step derivations with sanity checks
Reads diagrams, plots and hand-drawn schematics
Generates variant problems at the right difficulty

Labs, CAD & simulation

Turn tool friction into teaching time instead of setup time.

Scripts for SOLIDWORKS, FreeCAD, ANSYS, COMSOL
Post-processing and plot explanation on real output
Flags unit errors and implausible results early

Code, embedded & robotics

Firmware, PCB bring-up, ROS nodes and CI, reviewed by a tireless second pair of eyes.

Multi-language: C, C++, Python, Rust, Verilog
Explains register-level bugs and timing hazards
Reviews diffs against a team's own style guide

Design docs & reports

The part of engineering school nobody teaches, everyone grades.

Structures lab reports to a given rubric
Converts measurements into defensible conclusions
Citation and technical-writing checks at scale

Capstones & research

Long-horizon work that needs deep context, not a chat window that forgets.

Long-context reviews of papers, erratas and datasets
Reproducible experiment scripts and ablations
Cross-model verification before you trust a result

Skill ramp-up & onboarding

New graduates and cross-trained staff reaching real productivity in weeks.

Walkthroughs on your internal codebase and standards
Guided first tickets: read, change, test, ship
Progress checkpoints staff can audit
Platform

The unglamorous parts, handled

Wrangling four vendor contracts is not a learning outcome. AIIA Lab takes that off your plate so faculty can teach.

One protocol, every vendor

OpenAI-compatible everywhere. GPT, Claude, Gemini, DeepSeek, Qwen, GLM, Kimi and more behind a single SDK and a single key.

Course-level governance

Quotas, rate limits and spend caps per course, lab or project group — with audit logs a registrar or grants office can actually read.

Cost-aware routing

Simple derivations go to cheap, fast models; multi-step design work goes to frontier reasoning. Same outcomes, materially lower spend.

Uptime for lab hours

Multiple upstream routes with health probing. A provider incident becomes a routing event, not a cancelled lab session.

Telemetry that teaches

Latency, token cost and model performance per assignment — so course design decisions are made on evidence.

Fits your existing stack

Works with OpenWebUI, Dify, LangChain, LangGraph, Cherry Studio, NextChat, n8n and Jupyter. No rip-and-replace required.

Getting started

Four steps to a working AI lab

Most departments have a graded assignment running against AIIA Lab the same week they start.

1

Request access

Tell us your program and expected usage. We provision a sandbox with starter credits.

2

Point your code

Change base_url and the key. Nothing else in your repo moves.

3

Pick a profile

Start from a discipline routing profile, or let automatic routing pick the model per request.

4

Teach and measure

Watch usage, latency and cost per course, then tune the profile each term.

Model catalogue

One key, the whole frontier

A representative set below. New releases are added as they become generally available, and appear in your endpoint without any client change.

Model availability follows upstream releases. Institution plans can pin a fixed model list for reproducibility across a term.

Pricing

Pay for tokens, not for six vendor contracts

Start free with enough quota to prove a course design works. Scale to a department without renegotiating anything.

Pilot
Free

For a single course or a proof-of-concept assignment.

  • Starter credits on sign-up
  • Lightweight and mid-tier models
  • Basic usage dashboard
  • Community docs and sample labs
Start free
Department
Usage-based · volume tiers

For departments running AI across multiple courses and labs.

  • Full model catalogue, all routes
  • Discipline routing profiles & failover
  • Per-course quotas and spend caps
  • Priority capacity during lab hours
  • Onboarding support for teaching staff
Request access
Institution
Custom

For universities, polytechnics and corporate engineering academies.

  • Isolated tenancy and dedicated routes
  • Configurable retention and audit policy
  • On-prem or VPC deployment
  • SLA, invoicing and named engineering contact
Talk to us
FAQ

Questions we get from faculty

Is this just a proxy in front of other vendors?
No. A gateway moves bytes; AIIA Lab adds the layer that actually matters in a teaching context: per-discipline routing profiles, cross-model comparison, quota and spend governance per course, failover across independent routes, and usage telemetry you can put in a curriculum review.
How much of our existing code has to change?
Usually two lines: the base URL and the API key. Anything that already speaks the OpenAI protocol — LangChain, Dify, OpenWebUI, your own Python service — keeps working unchanged.
Can students get different models for different courses?
Yes. Routing is set at the key or course level. A controls lab can pin a reasoning-heavy model while an intro programming lab uses a fast, cheap one — all under the same institutional account.
What happens when a provider has an outage or rate-limits us?
Requests are health-probed and re-routed across independent upstream routes, typically in under 300 ms. Lab sessions keep running; you see the switch in the telemetry rather than in a failed assignment.
How do you keep costs predictable across a semester?
Per-course quotas, rate limits and spend caps are enforced before a request is sent, with alerts before a cap is reached. Cost-aware routing also shifts routine work to cheaper models automatically.
What about data handling and academic policy?
Traffic is encrypted in transit, and Institution plans support configurable retention, redaction and audit policies plus on-prem or VPC deployment. We can document exactly what is stored and for how long, so it can go straight into your academic integrity and data governance review.

Give your engineers every model from day one

Tell us about your program and we will set up a sandbox with starter credits, a routing profile for your disciplines, and a reference lab you can run in week one.