INTERTIE.ai
MANAGED INFERENCE · DEDICATED COMPUTE

Own your
intelligence.

Build AI around the work your business does. Choose your models, keep control of your data and decide when changes go live.

INTERTIE.ai's original translucent Goursat sculpture
ORIGINAL INTERTIE.ai STUDY

We’re developing managed inference and dedicated GPU services.
Capacity, delivery dates and service scope are agreed in each proposal.

Tell us about your workload ↗

01 / START WITH THE TASK

Real work.
A clear next step.

An answer is useful when it holds up against the source. Start with a task your team knows, then follow the evidence through the workflow.

How we scope inference and agents ↗
ONE TASK / THREE MOMENTSIllustrative example
Three separated wireframe layers: task, model runtime, and approved evidence.EVIDENCERUNTIMETASKILLUSTRATIVE SYSTEM / NOT TO SCALE
REQUEST

“Compare these two quotes.
What still needs confirmation?”

MODEL + TASK STATE

The question sets the scope.

The runtime keeps the request and its permissions together. The model identifies the information needed for the comparison.

Task received01
Approved sources only→
Human review available↗
Model inference · Agent runtime · Approved tools

02 / SERVICES

The compute.
The model. The work.

Start with the part you need.
We can discuss an AI service,
dedicated compute, or both.

Illustrative source-based GPU server studyCOMPUTE / INFRASTRUCTURE
01 / DEDICATED COMPUTE

Make room for
your workload.

Scope GPU capacity on its own, or add the storage, networking and operating support around it.

Hardware, region and availability are confirmed in the proposal.

Dedicated GPUs & managed cloud ↗
INFERENCE / TASK EXECUTIONThree separated wireframe layers: task, model runtime, and approved evidence.EVIDENCERUNTIMETASKILLUSTRATIVE SYSTEM / NOT TO SCALECONCEPTUAL WORKFLOW
02 / MANAGED INFERENCE & AGENTS

Run the model.
Coordinate the task.

A model call is one part of the job. Discuss the tools, permissions, recovery and human review your application needs.

Explore the service ↗
SAME TASK. SAME EVIDENCE.Example
One source record feeds a comparison between current and candidate answers.CURRENTCANDIDATE
SOURCE EXCERPT

“Target delivery: March.
Subject to stock confirmation.”

CURRENT ANSWER

Delivery is in March.

△ Loses a condition
CANDIDATE ANSWER

March is the target.
Stock still needs confirmation.

↳ Keeps the condition
Compare the answer with the record.REVIEW → DECIDE
03 / EVALUATION & MODEL IMPROVEMENT

Keep what works.
Test what changes.

Compare the current setup with a proposed change on the same tasks. Test retrieval, tools and workflow changes before considering post-training.

Try the evaluation example ↗
Also in scopeModel access & routingOpen models and provider APIs, assessed for the task. ↗

03 / OWN YOUR INTELLIGENCE

A model can change.
Your experience
should carry forward.

Keep the tasks, approved examples and evaluation records that make the work useful. Bring that knowledge to the next model decision.

See what ownership means ↗
Two model candidates connect to shared layers of examples, evaluation and release decisions.CURRENT SETUPNEXT CANDIDATEEXAMPLESEVALUATIONDECISIONYOUR KNOWLEDGE CARRIES FORWARD
01Your tasks02Your evaluation03Your decision

Rights and controls depend on the model licence, provider terms and agreed deployment.

04 / WORKING TOGETHER

Start with
one workload.

How a pilot is scoped ↗
01 / SCOPE

Choose the task.

Models, permitted data, expected usage and the result that matters.

02 / COMPARE

Run the agreed tests.

Quality, speed, reliability and the cost of a completed task.

03 / REVIEW

Make the next decision.

Review the evidence, remaining gaps and operating scope.

Research: agent workloads, compute and the cost of a completed task ↗

LET’S START WITH THE WORK

What do you
need to run?

Tell us about the application, the models you use and the capacity you need. Estimates and open questions are welcome.

Prepare a workload brief ↗

Non-sensitive information first. Current customer outreach focuses on US customers.