Start your company's AI journey: own it, don't rent it
Every company will run on its own AI layer, built from its own knowledge, the way every company came to need a network, a database, a website. We're the partner who helps you take the first step: turning your data into a private, owned AI you run on your own infrastructure. We make each step easy. No AI team required.
AI is becoming infrastructure: an owned layer, built on a company's own knowledge. The winners of the next decade won't be the ones who used AI first. They'll be the ones who started owning it first.The companies that start building now will pull ahead. The ones that wait will keep renting someone else's intelligence, and paying for it forever.
The choice: rent your AI, or own it
Renting is the fast way in, and for low-stakes tasks it's fine. But for the work that matters, the difference isn't just where the model runs. It's who holds the asset when the work is done.
Renting a cloud API
- Your private data leaves your walls on every call
- You pay per use, forever, and more as you scale
- The model changes or is deprecated underneath you
- Your knowledge trains someone else's model
- Stop paying and you're left with nothing you can keep
Owning your AI layer
- Your data never leaves your infrastructure
- Run it unlimited on hardware you already control
- Frozen and versioned: it changes only when you say so
- Trained on your data, so it compounds your advantage
- The model, the data asset, and the tools are yours to keep
The most valuable thing we build isn't the model
It's your data asset, and it's step one.
Your knowledge, turned into an asset you own forever
Most companies have decades of knowledge trapped in documents, systems, and people's heads, and have never turned it into something they can build on again and again. We take that messy, private knowledge and structure, label, timestamp, and version it into a clean dataset you own outright.
A model can be retrained, swapped, or upgraded next year. The structured, owned dataset underneath it outlives every model. It's the compounding foundation every future capability is built on. Starting early matters most here: it's the one advantage a competitor can't catch up on by writing a check.
The journey, one step at a time
You never take a giant leap. You take the next small, provable step, see it work, then decide on the one after. Each stage stands on its own and builds the foundation for what comes next. Most companies start with the free scorecard and the Assessment.
AI Data Readiness Scorecard
Free
An honest read on whether your data is ready to power a private model, and what it'd be good for. You run it yourself; we never see your data.
- Runs on your own machine, fully offline
- Data shapes, volume & quality signals
- PII/sensitivity summary (counts only)
- A recommended approach and next step
AI Readiness Assessment
$5k · fixed
We map your data, find the highest-value opportunities, and lay out your journey: a clear plan, not a guess. Credited toward your next step if you proceed within 90 days.
- Data inventory: shape, structure, sensitivity
- Highest-value target use cases
- Feasibility + readiness report
- Recommended base model & approach
Your Owned Data Asset
from $22k
Your knowledge turned into a structured, versioned data asset you own forever: the crown-jewel foundation. (No model yet; this is the groundwork.)
- Structured, labeled, versioned datasets
- PII/PHI redaction & sensitivity handling
- A reproducible data pipeline
- Owned by you, reusable forever
Your First Owned AI
from $45k
Your first owned AI capability, fine-tuned on your data, with a report proving it's better on your work. Deployable privately.
- A working private model, fine-tuned on your data
- Eval report proving measurable lift
- A deployment you can run yourself
- All weights, adapters & datasets handed over
Deploy Your AI Layer
from $85k · + hardware
Your model in production, on-prem or private, governed, access-controlled, and ready for your team.
- Hardened serving with logging & guardrails
- Access control + tamper-evident audit trail
- On-prem / air-gapped install
- Documentation + team training
Grow Your AI Layer
from $4k / month
An ongoing partnership with more data, more models and more capability, so your owned AI layer keeps compounding over time.
- Periodic retraining as your data grows
- Quality & drift monitoring
- New models & capabilities over time
- Advisory + support (your fractional AI team)
Starting prices reflect a typical engagement; each step is scoped and quoted to your data and goals, and you see the number before you commit to anything. The point of showing you the whole journey isn't to sell it all today. It's that there's a clear path, and every step is small enough to say yes to. We take on a small number of engagements at a time so each one gets real attention. Platform white-label / licensing available for partners. Common questions about cost, data, and ownership →
The Lift Guarantee
We don't claim your model is better. We measure it. Your private model beats your current baseline on your own tasks, in a written eval report, or you don't pay for the model. It's a promise we can make because proving the improvement is built into how we work.
What your owned AI could do
Trained on your data, the model learns your jargon, formats, and processes. A few shapes it can take:
A support model that speaks your product
Trained on your tickets, docs, and past resolutions. It answers in your voice and knows your edge cases.
A document model that knows your contracts
Reads, drafts, and extracts from your agreements, policies, and filings using your templates and clauses.
A code model trained on your codebase
Learns your internal libraries, conventions, and patterns, not just generic public code.
A domain model for your field
Clinical, legal, financial, or operational language and reasoning, grounded in how your organization works.
How we build it, and make it easy
A repeatable, low-surprise pipeline. Every step is tool-supported by our own proprietary platform, built from scratch and running on trusted open-weight models, so delivery is engineered, not artisanal, and you don't need an AI team of your own.
Discover
We inventory your data (its shape, structure, and storage) and pin down the job-to-be-done and success metrics.
Profile
Automated profiling learns quality, sensitivity, and structure across files, databases, tickets, code, PDFs, and logs.
Build the data asset
We normalize any-shape data into structured, versioned training and eval examples, and you sign off on samples before we train.
Fine-tune
We select the right open-weight base and method (LoRA/QLoRA, SFT, DPO, or continued pretraining) and run the training.
Evaluate
We score against benchmarks built from your tasks, comparing to the base model: the measured lift behind our guarantee.
Deploy
We install locally, on-prem, or air-gapped, and wire in access control, an audit trail, and guardrails.
Hand off
You receive the model, adapters, datasets, pipeline artifacts, documentation, and team training. It's yours.
Grow (optional)
We retrain and monitor on a retainer as your data grows: the ongoing partnership that keeps your AI layer compounding.
What you walk away owning
Every engagement hands over real, portable assets, not access to someone else's service.
The model weights
Your fine-tuned model in open formats (safetensors, GGUF).
The adapters
Portable LoRA/QLoRA adapters you can re-apply or update.
Your data asset
The structured, versioned datasets we built from your data.
The eval report
Documented proof of lift vs. the base model: your guarantee, in writing.
The pipeline
Reproducible artifacts so the model can be rebuilt or retrained.
Docs & training
Documentation and team enablement so you can run it yourself.
For regulated industries, this is the whole game
The competitive case for owning your AI applies to every company, but if your data is sensitive or regulated, owning isn't just the better deal. It may be the only deployment that satisfies your obligations. Data sovereignty, a frozen and auditable model, documented PII handling, and a tamper-evident record of what happened aren't nice-to-haves in your world. They're the requirements. Owning is how you meet them.
Data sovereignty by architecture
Your data never leaves your infrastructure. Compliance stops being a negotiation and becomes a property of the design, up to fully air-gapped.
Built for the frameworks you answer to
HIPAA, CJIS, ITAR/EAR, GDPR, SOC 2, FedRAMP, CMMC: a private, owned, on-prem model is often the only path that fits.
Governed & auditable
PII/PHI redaction with verification, sensitivity-based access control, and a tamper-evident audit log, recorded on every engagement.
A partner, not a vendor
Cloud APIs, DIY fine-tuning, and generic MLOps tools all leave you holding the hard parts. We walk the journey with you and deliver the outcome, a proven and owned AI layer, one easy step at a time.
We make it easy
Step-by-step, in plain language, meeting you where you are. No AI team, budget line, or strategy required on your side to start.
You own everything
The model, the data asset, and the tools to run it. No license keys, no per-use fees, no dependence on us continuing to exist.
Your data stays yours
In most engagements it never leaves your infrastructure, and the very first step, the scorecard, you run yourself so we never even see it.
We prove it works
Eval-first delivery. We measure the lift on your own tasks and stand behind it with the Lift Guarantee. Evidence, not claims.
Any data shape
A genuine connector + profiling layer ingests files, databases, tickets, code, chats, PDFs, logs, and structured tables.
In it for the journey
The relationship grows with you: more data, more models, more capability. Building an owned AI layer is a direction, not a purchase.
Questions we get asked
The ones that actually decide whether this is a fit, answered plainly, including where the honest answer is "it depends" or "not yet."
What does a custom AI model actually cost?
The journey starts at a $5,000 fixed-price readiness assessment, which is credited toward your next step if you proceed within 90 days. A first private model typically starts around $45,000, and a full production deployment around $85,000 plus hardware, which we quote separately and never resell at a markup. Every step is scoped and quoted to your data and goals before you commit, and you can stop at any rung with something you own.
How do you know the model is actually better?
We measure it. A portion of your real examples is held out so the model never sees them in training; we score the plain base model on those first, fine-tune, then score again on the identical questions. The difference is the lift, and it goes in a report you keep. On a public benchmark of 510 lawyer-annotated contracts, this method took short-field extraction from 62% to 83–85% across three runs. Here's the full method and the numbers, including two results we measured, caught, and threw away.
How much of our data do we need?
Less than most people assume. In our contract-extraction measurements, 48 training examples produced no measurable improvement, while roughly 400 produced a 22-point gain. What matters more than raw volume is consistency: whether similar inputs got handled similarly. The readiness assessment measures exactly this on your corpus, and tells you plainly if the answer is "not yet."
Does our data leave our building?
No. The free scorecard runs entirely on your machine and opens no network connections, so we never see your data at all. Paid engagements run inside your boundary, on your hardware or in your own cloud tenant. We bring the software to the data, not the other way around, and we take custody only as an explicit last resort you'd have to agree to.
What exactly do we own at the end?
The model weights, the adapters, the training datasets, and the pipeline that produced them, plus the documentation to rebuild it. You can run it after we're gone, on hardware you control, with no per-use fees and nothing to renew. We work with permissively licensed open-weight base models (Apache-2.0 and similar) precisely so that ownership isn't complicated by someone else's terms.
Can this run fully air-gapped?
Yes, and that's a large part of why the platform is built the way it is. Every stage (ingestion, training, evaluation, serving) works with no network path in or out. If your requirement is that data physically cannot leave the facility, that's a requirement rented cloud AI cannot meet at any price.
What if it doesn't work on our data?
Then we tell you, and we'd rather tell you at the $5k assessment than after the pilot. Some tasks have little room for improvement because a general model already handles them well; some corpora are too small or too inconsistent to train on yet. That's a real finding and you keep the report. For the model-building stage we also carry a lift guarantee: it beats your baseline on your own tasks, measured, or you don't pay for it.
Do we need to hire an AI team?
No, and that's the point of the engagement model. We do the work and it executes on your machines, with your people in the room as much or as little as you want. What you're left with is documented and reproducible, so your team can take it further if and when you want to bring it in-house.
Start free: see where you stand
You don't have to commit to a journey to find out where you stand. Start with the free AI Data Readiness Scorecard. Run it on your own data, on your own machine, and get an honest read on what's possible. No data leaves your building, no obligation. Or grab a time and we'll come prepared.
Schedule with Awareness Software Group
Pick a slot that works for you. No commitment.