Local AI model runtime and analysis workbench

For

IT teams and analysts who must run AI models on their own hardware without cloud services

Solves

Cloud AI services create per-token costs and send sensitive data outside the organization, while local runtimes are hard to set up, tune and validate.

Delivers

Benchmarked, validated local model runs and statistical reports

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

Inside your business, or as part of your offer to clients

Run AI models locally with no per-token charges and keep data on your own machines.

1. Run AI models directly on the local machine.
2. Avoid per-token charges by keeping inference local.
3. Set up with a single command or a few clicks.
4. Auto-tune the inference engine for the detected hardware.
5. Recommend models the hardware can run.
6. Download open-weight models from the command line.
7. Benchmark tokens per second per model and machine.
8. Apply Apple Silicon optimizations such as the Metal 4 tensor API.
9. Run Office Mode so one machine serves other laptops over the network.
10. Operate without online accounts.
11. Search local files and folders with AI.
12. Let teams test models privately before cloud commitment.
13. Support a wide range of local large language models.
14. Generate written content in different styles.
15. Produce articles, emails and social posts.
16. Suggest grammar and readability improvements.
17. Build partial least squares models through guided steps.
18. Validate models with bootstrapping and cross-validation.
19. Show interactive path model and loading visualizations.
20. Import and export multiple data formats.
21. Produce statistical reports with interpretation aids.
22. Compare the reviewed result with the recorded baseline and value assumptions.
23. Capture corrections and named-owner approval before consequential use.
24. Export a versioned benchmarked, validated local model runs and statistical reports with source references and unresolved questions.

Everything these tools do, in one app

• Local on-device execution Runs AI models directly on your own machine so data stays private and no cloud service is needed.Found in BaseRT, Local, AUM
• No per-token costs Avoids ongoing per-token charges because inference happens locally instead of through a paid cloud API.Found in BaseRT, AUM
• Easy setup Gets you running with minimal configuration, such as a single command or a few clicks.Found in BaseRT, Local, AUM and 1 more
• Hardware auto-tuning Automatically optimizes the inference engine for your specific computer to improve speed.Found in Local
• Model recommendations Suggests which AI models your hardware can actually run.Found in Local
• Built-in model downloader Fetches open-weight models directly from the command line without manual file handling.Found in BaseRT
• Performance benchmarking Measures tokens per second for different models on your specific hardware.Found in BaseRT
• Apple Silicon optimization Uses Apple Silicon-specific optimizations, such as the Metal 4 tensor API, for faster prompt processing.Found in BaseRT
• Office Mode Lets one powerful office machine run AI while other laptops connect to it over the network.Found in Local
• No accounts required Runs without creating or signing into an online account.Found in Local
• AI-powered local file search Searches local files and folders using AI.Found in AUM
• Private team testing Lets teams test models privately before committing to a cloud-based model.Found in AUM
• Multiple model support Runs a wide range of large language models locally.Found in AUM
• AI content generation Generates written content tailored to different writing styles.Found in Ollamac
• Multiple content types Supports creating articles, emails, social media posts, and similar formats.Found in Ollamac
• Grammar and readability suggestions Provides real-time suggestions to improve grammar and readability.Found in Ollamac
• Automated PLS model building Builds partial least squares models through guided steps.Found in Ava PLS
• Model validation Validates models using methods such as bootstrapping and cross-validation.Found in Ava PLS
• Interactive visualizations Shows interactive visualizations of path models and loadings.Found in Ava PLS
• Data import and export Supports multiple data formats and makes importing and exporting data easy.Found in Ava PLS
• Detailed statistical reports Produces reports with statistical metrics and interpretation aids.Found in Ava PLS

What goes in, what comes out

The workflow

1. In
   Start with

   Local hardware profiles, open-weight models, organizational data and validation settings

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect local hardware profiles

4. 3

   Open-weight models

5. 4

   Organizational data and validation settings

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Benchmarked, validated local model runs and statistical reports

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Local execution only; model quality and statistical validity checks remain with qualified reviewers. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Hardware and model setup, Local run and benchmark console, Analysis and report workspace. Use a machine list with detected hardware, a central run view with live tokens per second and resource use, and a right-hand panel for model files, validation settings and comments. Let users compare models and runs side by side. Display draft, validated and approved states. Provide a client preview link with comments anchored to the relevant run or report. Make the task-specific outcome benchmarked, validated local model runs and statistical reports visible beside its evidence, review state and value baseline.

Accounts and administration

Machine ownership, model versions, team access, approval states, usage allowances, run history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Organization-owned data sources, permitted local file systems and approved model repositories. Local storage, design-file import/export and reporting destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   6 days

   One buyer segment, one recurring use case; first modules: run AI models directly on the local machine; auto-tune the inference engine for the detected hardware. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   2 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will IT teams and analysts who must run AI models on their own hardware without cloud services use it to solve "cloud AI services create per-token costs and send sensitive data outside the organization, while local runtimes are hard to set up, tune and validate"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Tokens per second on target hardware and accepted local runs per analyst hour.
4. Measure, then decide. Track tokens per second on target hardware and accepted local runs per analyst hour; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One supported operating system and one hardware class; final statistical validity and model quality checks remain with qualified reviewers. Implement one approved input format, a bounded representative case set and the first two task modules: run AI models directly on the local machine; auto-tune the inference engine for the detected hardware. Support the remaining modules with operator review: benchmark tokens per second; validate models with bootstrapping and cross-validation; produce statistical reports. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported hardware and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around benchmarked, validated local model runs and statistical reports. Retain the explicit scope boundary: One supported operating system and one hardware class; final statistical validity and model quality checks remain with qualified reviewers.

What the build depends on. Local install and hardware detection, asynchronous run jobs, editable version history, reviewer access and tested export formats. High-fidelity statistical work requires specialist QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One supported operating system and one hardware class; final statistical validity and model quality checks remain with qualified reviewers.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined hardware and model package. Offer a monthly production allowance after repeat demand. Quote complex multi-machine or specialist statistical work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded benchmarked, validated local model runs and statistical reports. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Run AI models locally with no per-token charges and keep data on your own machines. Demonstrate a concrete benchmarked, validated local model runs and statistical reports using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

IT teams and analysts who must run AI models on their own hardware without cloud services professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample benchmarked, validated local model runs and statistical reports from a small authorized input set, with a transparent calculation of tokens per second on target hardware and accepted local runs per analyst hour and no promised savings.

The first 30 days

1. Week 1: interview five IT teams and analysts who must run AI models on their own hardware without cloud services and inspect a recent example of cloud AI services create per-token costs and send sensitive data outside the organization, while local runtimes are hard to set up, tune and validate.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure tokens per second on target hardware and accepted local runs per analyst hour, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Tokens per second on target hardware and accepted local runs per analyst hour. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Tokens per second on target hardware and accepted local runs per analyst hour; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs benchmarked, validated local model runs and statistical reports. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved hardware profiles, model configurations and review examples, together with reliable delivery for a narrow IT and analytics niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for IT teams and analysts who must run AI models on their own hardware without cloud services. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Ava PLS, BaseRT, Local, Ollamac and AUM. Compare this product with the buyer's present method on tokens per second on target hardware and accepted local runs per analyst hour. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Local compute time, storage, reviewer hours, client revision rounds and licensed model or data assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of benchmarked, validated local model runs and statistical reports. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve data ownership, source attribution, statistical accuracy and usage permissions. Named owners approve substantive changes and external sharing. One supported operating system and one hardware class; final statistical validity and model quality checks remain with qualified reviewers. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.