Your models. Your hardware. Your data stays home.
Imgori designs, deploys and runs AI infrastructure for companies that can't send their data to a public API, or can't afford to. We build private LLM platforms, squeeze more work out of every GPU, and set up the pipelines, security controls and support that keep AI running in production.
AI is most useful on the data you can least afford to share.
The last two years proved what AI can do. The next two are about doing it safely, affordably and on your own terms.
Data that can't leave
Customer records, contracts, source code and patient files are exactly what AI is most useful for, and exactly what security and legal teams won't send to a third-party API.
Keep it in your network →Bills that grow with success
Per-token pricing means the more your teams use AI, the faster costs climb. At steady volume, owning inference is often several times cheaper than renting it.
Pay for hardware, not tokens →Lock-in that arrives quietly
Prompts, fine-tunes and workflows built around one vendor's model are hard to move. Open-weight models and portable infrastructure keep your options open.
Own the weights and the stack →Everything between “we should use AI” and a model in production.
Seven service lines delivered by one senior team. Use a single service, combine several, or let us design, build and run the whole stack for you.
Private & On-Prem LLM Deployment
Run open-weight models on your own servers or private cloud, with no data leaving your network.
Learn more →GPU Infrastructure Optimization
Cut inference and training spend with better scheduling, quantization and right-sized clusters.
Learn more →Cloud-to-AI Migration
Move legacy workloads and data platforms onto infrastructure that is ready for AI.
Learn more →MLOps Pipeline Setup
Automate training, evaluation, deployment and monitoring so models ship like software.
Learn more →Custom Model Fine-Tuning
Adapt open models to your language, documents and workflows with your own data.
Learn more →AI Security & Compliance Audit
Assess model risk, data privacy and controls before regulators or customers ask.
Learn more →Ongoing Managed AI Support
A monthly retainer for monitoring, upgrades and on-call support of your AI stack.
Learn more →NOT SURE?Start with a two-week assessment
We map your workloads, data, hardware and spend, then tell you which of these services you need, and which you don't.
Compare services →Seven layers. All inside your walls.
A private AI platform is not one product. It is a set of layers that each need to be chosen, sized, secured and operated. We design every layer so it fits the tools your platform team already knows, and so each can be upgraded independently as models and hardware improve.
- Gateway first. Every request is authenticated, metered and logged before it reaches a model.
- Swappable models. Upgrade to a better open model without touching your applications.
- Permission-aware retrieval. Users only get answers from documents they are allowed to see.
- Right-sized hardware. Capacity planned from real traffic, not guesses.
Built for the numbers your CFO and CISO both care about.
Same GPUs. Far more work done.
A typical fleet before tuning, and the same workload after continuous batching, 4-bit quantization and right-sizing. Illustrative figures based on common open-weight model benchmarks.
Idle, oversized, unbatched
one request per forward pass · fp16 · 8 × H100
31%tokens/sec
1,140$ / 1M tokens
$2.90
Batched, quantized, right-sized
continuous batching · awq-int4 · 4 × H100
89%tokens/sec
4,380$ / 1M tokens
$0.95
Four kinds of teams call us.
Different starting points, same goal: AI that is secure, affordable and fully under their control.
Mid-to-large enterprise
You need AI infrastructure but not a 20-person platform team to run it.
AI-first startups
GPU costs are eating margin and you need serving that grows with you.
Regulated industries
Your data can't go to a public cloud API, so the AI has to come to it.
PE & VC firms
Audit a portfolio company's AI strategy, spend and risk before or after a deal.
From first call to production in one quarter.
Every project follows the same four stages, with a clear deliverable and a go/no-go decision at the end of each. You never pay for a stage you don't need, and you can bring the work in-house at any point.
- Fixed scope per phase: price and deliverables agreed before each stage starts
- Weekly demos: working software every week, not slideware
- Pairing by default: your engineers work alongside ours
Assess
We map your workloads, data sensitivity, hardware and spend, then agree on success metrics.
≈ 2 weeksPilot
One use case, one model, real users, measured against the metrics we agreed.
3–5 weeksProductionize
High availability, monitoring, security controls and runbooks.
4–8 weeksOperate
Hand over to your team, or keep us on a managed plan.
OngoingWhat engagements look like.
Representative scopes and the targets we set. Client names are shared on request under NDA.
Regional lender moves credit memo drafting on-prem
Llama-based drafting assistant on four L40S GPUs inside the bank's data center, connected to the loan origination system with row-level permissions and full audit logging.
Series B SaaS cuts cost per request
Model routing, int4 quantization and continuous batching on a shared H100 pool.
Health system audits AI before rollout
Inventory, red-teaming and a HIPAA-mapped remediation plan.
Wherever your data lives, we deploy there.
Headquartered in Albuquerque, New Mexico, we deliver remotely to clients around the world, and on-site when your environment requires it.
Where data control isn't optional.
Finance & Fintech
Private AI for banks, lenders, insurers and payment companies.
View industry →Healthcare & Life Sciences
AI that keeps PHI inside your walls.
View industry →Manufacturing & Logistics
AI on the plant floor and across the supply chain.
View industry →Retail & E-commerce
Enterprise AI for catalogs, service and merchandising.
View industry →Legal & Professional Services
AI that respects privilege and client confidentiality.
View industry →Government & Public Sector Soon
Sovereign AI for agencies. Coming soon.
View industry →Built on the open AI stack.
We are vendor-neutral. These are the tools we reach for most often, chosen for each client based on scale, skills and constraints.
Common questions
Straight answers to what buyers ask us most. More in the FAQ, or ask an engineer directly.
Ask a question →Do we need to buy GPUs to work with you?
No. We deploy on hardware you already own, on reserved capacity in your cloud account, or on specialist GPU clouds. Part of the assessment is telling you which option is cheapest for your workload.
Are open-weight models good enough?
For most enterprise tasks (summarization, extraction, drafting, search, classification) current open-weight models perform on par with the large hosted models, especially with retrieval or light fine-tuning. We benchmark on your own tasks before you commit.
Who owns what you build?
You do. Code, infrastructure definitions, fine-tuned weights, datasets and documentation are delivered into your repositories and accounts.
Can you work in air-gapped environments?
Yes. We package models, containers and dependencies for offline installation and can work on-site or through your approved remote access.
Do you work outside the United States?
Yes. We serve clients worldwide, working remotely across time zones and on-site when an environment requires it. We design for regional data residency rules such as GDPR.
How much does a typical project cost?
Assessments are a fixed fee. Pilots and production builds are priced per phase after the assessment, so you always know the cost of the next step before committing.
How do you handle our data during the project?
Our engineers work through accounts you provision and can revoke. Client data stays in your environment, and anything used during delivery is deleted at close-out with written confirmation.
What happens after launch?
You can run the platform yourself with our runbooks and training, or keep us on a managed support plan with monitoring, upgrades and a named engineer.
Find out what private AI would cost you.
A 45-minute call with an engineer, not a sales script. You'll leave with a rough architecture, a cost range and the next three steps.