Self-hosted LLaMA-class models, production-grade RAG and agentic systems, deployed inside your VPC. Architect-led pods, mobilized in 48 hours.
Built for regulated, high-throughput environments
Trusted by AI & engineering leaders at
Logos shown are representative pending publishing approvals.
01 — The Sovereignty Gap
Three structural blockers kill enterprise AI programs. We engineered our entire delivery model to absorb all three.
Sovereignty
Regulated industries — BFSI, healthcare, public sector — can't ship PII, PHI, or proprietary IP to public LLM APIs. Vendor DPAs don't solve residency, audit, or board-level risk.
Economics
Per-call API pricing looks cheap at pilot, then collapses unit economics in production. Predictable GPU spend beats unbounded token bills the moment you ship to real users.
Lock-in
Deep dependency on OpenAI or Anthropic puts your AI roadmap one pricing change, one model deprecation, one outage away from a board conversation you don't want to have.
02 — AI Capabilities
LLaMA 3.x, Mistral, and Qwen served on vLLM and Triton — inside your VPC, your sovereign cloud, or on-prem GPU clusters.
Citation-grade retrieval over policy, contracts, claims, and SOP corpora. Self-hosted vectors, hybrid search, evals built in.
Multi-step reasoning agents executing across CRM, ERP, ticketing, and email — with human-in-the-loop checkpoints.
Legacy code → intent recovery → AI-ready architecture. We rebuild the systems your AI needs to plug into, without losing 20 years of business logic.
The boring discipline that keeps AI in production: model registry, drift monitoring, guardrails, regression evals, cost telemetry.
The .NET 8+ APIs, event backbones, and Flutter front-ends your AI platform plugs into. Engineering depth, not just AI demos.
03 — Economics in production
Self-hosted LLM vs. Public API
Illustrative — enterprise workload at ~100K daily completions, 12-month TCO.
| Metric | Public API | Innovura · Self-hosted | Delta |
|---|---|---|---|
| Annual inference spend | $2.4M | $520K | −78% |
| P50 latency | 1,200 ms | 320 ms | −73% |
| Data egress risk | Vendor-bound | Zero | Eliminated |
| Roadmap lock-in | High | Open weights | Eliminated |
inference cost ↓ at scale
AI pod mobilization
avg. engineer seniority
Agentic AI · patterns we ship
Audit & Compliance Co-Pilot
Multi-agent workflows that read controls, evidence, and ledgers — drafting findings for review.
Diligence Intelligence Agent
Autonomous data-room ingestion + cross-document analysis for M&A and PE diligence cycles.
Process Automation Agents
Reasoning agents that execute multi-step ops across CRM, ERP, ticketing, and email.
Enterprise Knowledge Brain
Domain-specific RAG over policy, contract, and SOP corpora — with citation-grade outputs.
04 — The Sovereign Stack
Most AI vendors are reselling someone else's API. We engineer the production stack underneath it — so your data, your models, and your roadmap stay yours.
Deploy targets: AWS / Azure / GCP private VPC · GPU on-prem · Sovereign cloud · Air-gapped.
Architect a sovereign pilot →05 — Proof in production
Two recent engagements. Both replaced public-API prototypes that couldn't clear compliance.
BFSI · Tier-1 Bank
Replaced a 40-analyst manual alert review queue with a self-hosted agentic workflow. Citation-grade reasoning over policy + transaction history, full audit trail, zero data egress to third-party LLMs.
alert resolution time ↓
data kept in-tenancy
pilot to production
Healthcare · National Payer
Self-hosted Qdrant + fine-tuned Mistral over 14 years of claims, policy, and clinical guidelines. Replaced a Snowflake + OpenAI prototype that legal couldn't approve — same accuracy, sovereign deployment.
first-pass claims accuracy
annual API spend avoided
executed in-quarter
Named-client publishing pending NDA approvals. Briefing call includes live case walk-through.
06 — Delivery framework
Outcome-priced where it matters. Architect-owned at every stage. No SOW theater.
01 · Week 1
Use-case shaping, data audit, sovereignty constraints, eval criteria.
→ Opportunity brief + go/no-go
02 · Week 2
Reference architecture, model + serving choice, security review, cost envelope.
→ Signed-off architecture + SOW
03 · Weeks 3–10
Architect-led pod ships a working agent / RAG / fine-tune against the live use-case.
→ Production-grade pilot + evals
04 · Weeks 11–16
LLMOps, guardrails, observability, scale tests, change-management.
→ Go-live + runbooks
05 · Ongoing
Drift monitoring, model refresh, expansion to adjacent use-cases.
→ Quarterly value review
07 — Mobilization
A lean onboarding designed around how enterprise procurement actually buys delivery capacity.
Day 0–2
Use-case shaping, capability fit, delivery model alignment.
Day 3–5
MSA, rate card, and IP terms aligned.
Day 6–9
Role mix, seniority blend, shadow-PM identification.
Day 10–12
Tooling, security clearances, client-context briefings.
Day 13–14
Sprint zero, definition-of-done aligned, first commits live.
You convert a strategy SOW into a shipping engineering program inside two weeks — without expanding permanent headcount, bench cost, or delivery risk.
Start the 14-day clock →08 — Why Innovura
data stays in your tenancy
AI pod mobilization
AI service lines, one pod
avg. engineer seniority
Every reference architecture starts inside your VPC. Open-weight models, self-hosted vectors, zero third-party LLM dependencies unless you ask for them.
SOC2-aligned controls, HIPAA / BAA / DPA-ready, India-Plus delivery, audit-logged engineering workflows. Built for what your CISO actually approves.
60%+ of every pod is engineers with 7+ years shipping production AI, .NET, and mobile systems. No bench juniors learning on your roadmap.
"We don't compete with our clients on AI strategy. We make their AI strategy executable — inside their firewall, on their data, with their CISO's signature on the architecture."
09 — Verticals we serve
Sovereign LLMs for AML, KYC, and credit. RAG over policy with full audit trail.
HIPAA-grade RAG over claims and clinical guidelines. BAA-ready agentic workflows.
Network ops co-pilots, OSS/BSS agents, field-engineer mobile with on-device AI.
Inspection-vision models, predictive ops agents, MES + ERP integration.
Merchandising agents, store-ops automation, sovereign customer-data RAG.
10 — Architect-level answers
11 — Next step
A 15-minute capability briefing with one of our AI architects. Reference architectures, named-case walk-throughs, and real numbers for your sovereign-AI roadmap.
