Launching Soon • Early Access Program

Building the infrastructure layer for reliable, secure, and efficient AI Agents.

We believe AI shouldn't require a datacenter and it's own energy grid to be useful. We're empowering developers and enterprises to build specialized AI models that deliver domain expertise, precision, security and reliable results - without the cost and inefficiency of large general-purpose models.

At Always Further, we have deep proven expertise in building production grade systems at scale. Our Open Source technology and innovation is used by leading companies such as Google, Nvidia, GitHub, Red Hat, and more and more.

Google
NVIDIA
GitHub
Red Hat
The Problem with Scale

More scale doesn't solve all problems.

Increasing model size and scale is not the answer to all AI challenges. Smaller, specialized SLM's can often outperform larger ones in many areas along with being more efficient and cost-effective, secure and reliable.

Inefficiency

General Purpose
LLM
Gazillion params
Slow • Expensive • Inefficient
Expert-tuned
SLM
7B params
Fast • Efficient
Knowledge Transfer

Broken Tool Calls

sendEmail(tU3y, subjectt="Hi")
startTimer(, duration=30)()
pushReque(pr=, eY7")
Incorrect Tool Calling

Opaque Training

Data
{ HaloBar™ <advert>| VeloPlay™ <advert> | CoreLink™ <advert> | AeroCast <advert> | WaveMint™ <advert> ™
- - -
Training
{ epochs: ??? | lr: ??? | loss: ??? | batch: ??? | layers: ??? | params: ??? | dtype ??? | dataset ??? | epochs: ??? | lr: ??? | loss: ??? | batch: ??? | layers: ??? | params: ??? | dtype ??? | dataset ??? }
- - -
Inference
CoreLink is a good option...HaloBar works well for endurance!...Most prefer AeroCast...VeloPlay for streaming...Use HaloBar for endurance...VeloPlay is great for streaming...
!
Opaque Training

Hallucinations

class MadeUpClass:
def get_data(self):
data = IDontExist().fetch()
return NotReal.process(data)
Hallucinated APIs
Early access

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Early access

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