BUILD.DEPLOY.OPERATERELIABLE AI
The AI control plane — your infrastructure, your data, your models, your agents.
- Agentic runtime
- Execution traces
- Guardrails
- Continuous improvement
- Sovereign deployment
Pilots are easy. Production is not.
Enterprises are not short of agent prototypes. They are short of agents that survive contact with production — real data, real volume, and real consequences when a decision goes wrong.
Build
Compose agents from skills, tools, MCP integrations and sub-agents.
Deploy
Ship agents as versioned artifacts on Kubernetes — your servers, your cloud, or air-gapped.
Operate
Every run leaves a full execution trace.
Improve
Production traces become training data.
Invergent AI Lab
Where intelligence is built
From infrastructure to LLM architectures, custom serving kernels and our own training framework, we advance AI development through continuous research and innovation.
Research
Agent tooling, RL alignment, high-speed serving, low-bit quantization.
- Agent and skill optimization through RL and self-evolving harnesses
- Custom serving engines for Blackwell, Ada, Hopper and ROCm
- High-speed training algorithms for SFT and GRPO on consumer cards
Open source
The substrate is open by construction, so nothing we build locks you in.
- Surogates ↗Our agent harness
- Surogate Trainer ↗High-speed, low-bit training engine
- Surogate Hub ↗A private HuggingFace Hub alternative
Trained models
Small models, distilled from work only one organization has.
- Romanian-language foundation work, trained from scratch
- Expert models fine-tuned on a client’s own production traces
- Domain adapters — LoRA and QLoRA — served on your own hardware
- Check out our HuggingFace page ↗
One control plane for the whole agent lifecycle
Build, run, observe, evaluate, govern and improve — in one place, on infrastructure you control. Agent definitions, skills, tools, models and datasets are versioned in one registry, so what runs in production is exactly what you approved.
Agentic runtime
Agents that plan, decide and execute.
Traces beneath every decision
A full execution trace for every run.
Continuous improvement loop
Production traces become training data.
Model training and specialization
LoRA, QLoRA and full fine-tuning on your data.
Data Hub
A versioned registry for models, datasets, agent definitions, skills and tools.
Evaluation you define
Standard benchmarks establish the baseline.
Where to start
The appliance underneath, the platform on top, the automation layer around them. Start where your transformation actually is.
DenseMAX Appliance
Optimized hardware for end-to-end AI enablement
- Hardware and software delivered as one unit, integrated before it reaches your rack
- Tuned for sustained load rather than benchmark peaks
- Surogate ships with it, ready for agent and training workloads
- Up to 8× NVIDIA RTX 5090 or RTX PRO 6000 Blackwell GPUs
Digital Automation
No-code platform for complex digital workflows
- Document workflows, process automation and the long tail of work between systems
- A foundation your teams extend without writing code
- Mission-critical KPIs visible the moment they move
- End-to-end data governance, from APIs through to storage
Surogate
The enterprise AgentOps platform
- Agents composed from skills, tools, models and sub-agents
- Full execution traces, evaluation and guardrails from the first deployment
- Specialized models trained on your production traffic
- Self-hosted on Kubernetes — your servers, your cloud, or air-gapped
What specialization is worth
A compact model, trained on one agent’s task from that agent’s own production traces, measured against a general frontier API on the same evaluation set.
Our own measurements. Yours will differ with the task — which is why every deployment starts by building an evaluation set of your own.
< 30 days
From proof of concept to production
99.95%
Availability SLA on DenseMAX appliances
< 12 months
To return on the investment
Let's talk about your use case
Book a demoProven where the consequences are real
We deploy inside your data boundary, your compliance regime and your operational reality. These are the sectors we already work in.
Finance
Fraud moves faster than a review queue, and the regulator’s questions do not wait.
- Real-time fraud detection
- Automated risk and compliance reporting
- Client insight distilled from your own data
- Faster reconciliation and audit workflows
Telecom
Networks the size of a country, and subscribers who notice every second of degradation.
- Predictive network optimization
- Support agents that resolve rather than deflect
- Churn prediction and retention
- Automated billing and revenue assurance
Healthcare
Clinical data is deep, sensitive, and never allowed to leave.
- Clinical decision support for diagnostics
- Accelerated medical imaging analysis
- Scheduling and intake assistants
- On-premise deployment for HIPAA and GDPR
Manufacturing
Downtime, defects and supply volatility compound.
- Predictive maintenance of equipment
- Automated defect detection in production
- Demand forecasting and supply chain planning
- Worker safety monitoring
Retail
The shelf, the app and the call centre are expected to know the same thing at the same moment.
- Personalized recommendations
- Support that knows the order history
- Demand forecasting
- Loss prevention
- Continuity across every channel
Public Sector
Constrained budgets, rising expectations, and a sovereignty requirement that is not negotiable.
- Citizen service delivery
- Public safety
- Social services casework
- Infrastructure management
- Policy analysis and research
Enforced, not documented
Every agent runs inside a boundary you drew. Identity, budget, redaction and audit are enforced at runtime — not written in a document.
- ISO 27001
- SOC 2 Type II (in progress)
- GDPR / Schrems II
- HIPAA-ready architectures
Zero-trust defaults
Role-based access control, project isolation, scoped credentials in a vault, network isolation.
Agent-scoped authority
Every agent is a registered, versioned object with its own credentials and its own budget cap. Nothing is inherited from whoever launched it.
Data residency
Your VPC or your own metal. Your data, your traces and the weights you train stay inside your perimeter.
Policy guardrails
Redaction, PII handling, content filters, throttling and automated red-teaming, applied to the agent as it runs.
Operational hardening
Encrypted at rest and in transit, secure boot, TPM attestation, sandboxed execution, resource isolation.
Lineage and rollback
Versioned artifacts, dataset cards and reproducible fine-tuning. Every result traces back to the run that produced it.
Sovereign by default
The same platform, wherever your data is allowed to be.
DenseMAX appliance
On your floor, on your power.
Your own cloud
AWS, GCP, Azure or Oracle Cloud, inside your VPC.
Hybrid
Train on the appliance and burst to the cloud, or the reverse.
Air-gapped
Fully isolated deployment for environments where nothing leaves — including the models you trained.
Bring us a workflow that matters
One real use case and your infrastructure constraints are enough to start. We will show you the path to production, what it takes to govern, and what it costs to run.