NIKITRIA
Request investor deck

For enterprises & sovereign-AI organizations

AI that never leaves your perimeter

If your data cannot leave your infrastructure — for regulatory, contractual, or sovereignty reasons — most AI vendors are structurally unable to serve you. NIKITRIA is built the other way around: local-first by architecture, with a planned BYO-server tier and in-company fine-tuning that keep models, data, and inference on hardware you control.

You are probably a fit if

  • Regulation (DPDP, GDPR, sector rules) or contracts prevent off-premises inference
  • You handle sensitive IP, personal data at scale, or government workloads
  • You want model ownership and no vendor lock-in — open formats, portable by construction
  • You operate air-gapped or intermittently connected environments

BYO-server · on-premise

Your servers. Your models. Your data.

The planned enterprise tier licenses the entire NIKITRIA platform to run on infrastructure you already control — your servers, your VPC, your air-gapped rack. No cloud dependency, no vendor lock-in, and data that never crosses your perimeter.

  • Data sovereignty, literally

    Models, routing, and synthesis all execute inside your network boundary. There is no telemetry path to us.

  • No lock-in

    Built on open-source model formats (GGUF) and open protocols — your models remain yours, portable by construction.

  • Regulatory alignment

    Designed for DPDP, GDPR, and EU AI Act environments where off-premises inference is a compliance risk, not a convenience.

Gartner's 2025 Hype Cycle for Government Services predicts that by 2028, 65% of governments worldwide will introduce technological-sovereignty requirements to improve independence and protect against extraterritorial regulatory interference. — Gartner, 2025

Deloitte's 8th State of AI in the Enterprise report (3,235 leaders across 24 countries, fielded Aug–Sep 2025) found 77% of companies now factor an AI solution's country of origin into vendor selection, and 58% build their AI stacks primarily with local vendors. — Deloitte AI Institute, Jan 2026

On-premises deployments dominate sovereign-AI infrastructure for the most sensitive workloads. — NextMSC

Talk to us about a sovereign deployment

The BYO-server tier is a planned offering; deployments today are scoped as design partnerships.

In-company fine-tuning

Your data, distilled into your own private experts

A planned NIKITRIA service: fine-tune a company's or industry's proprietary data into domain-specific Small Language Models that run on the customer's own hardware — private by construction, because the data and the model never leave the building.

  • Specialists beat generalists at the job that matters

    A model trained on your processes, vocabulary, and edge cases — retaining the context generic pilots lose.

  • Fast, cheap iteration

    NVIDIA's SLM research notes small models can be fine-tuned overnight rather than over weeks, and are 10–30× cheaper to serve than frontier-scale LLMs.

  • Private by construction

    Training data, adapters, and inference all stay on infrastructure you control.

MIT Project NANDA's "The GenAI Divide: State of AI in Business 2025" (July 2025) found just 5% of integrated AI pilots are extracting millions in value, while the vast majority show no measurable P&L impact — attributing the gap to a "learning gap" that specialized, context-retaining models are positioned to close. (150 executive interviews, a 350-employee survey, 300 public deployments analyzed; lead author Aditya Challapally.) — MIT Project NANDA, Jul 2025

SLMs can be fine-tuned in hours and served at a fraction of frontier-LLM cost — 10–30× cheaper in latency, energy, and FLOPs. — Belcak et al., NVIDIA Research, arXiv:2506.02153, 2025

Offered initially as an integration-services engagement (pillar 5), productizing over time.

How it works

A panel of experts, on your machine

Instead of one giant generalist model in someone else's datacenter, NIKITRIA runs a panel of small specialist models on your own hardware and combines their answers — with every step visible and attributable.

  1. Ask

    Your question is processed entirely on your device. Nothing is sent anywhere.

  2. Route

    An embedding-based router matches the question to the best-suited specialist expert — semantic matching, not keywords, and still no network calls.

  3. Panel of experts

    One or more specialist Small Language Models produce candidate answers within a strict on-device memory budget.

  4. Synthesize with provenance

    The answers are combined into a single response, and every part of it traces back to the expert that produced it.

Panel-of-experts flow diagram Diagram: a query flows into an on-device router, which dispatches it to specialist experts; their answers merge in a synthesis step that returns one response with provenance. A boundary line marks that everything happens on the device. Everything inside this line runs on your device — no cloud Query Router embedding match Expert A specialist SLMs Expert B specialist SLMs Expert C specialist SLMs Synthesis one answer Provenance who said what

Architecture shown at the conceptual level.

For privacy-sensitive & sovereign-AI organizations

Become a design partner

We are selecting a small group of design partners — organizations whose data cannot leave their infrastructure — to shape the certified marketplace and the BYO-server tier. Design partnership starts with a non-binding letter of intent.

Submissions go only to the founder and are used solely to follow up about design partnership. Privacy policy